Specification-Driven Governance for Fairness-by-Design in Agentic Software Engineering: How Far Can We Go?

arXiv:2611.03231v1 [cs.SE] 2 Nov 2026

Specification-Driven Governance for Fairness-by-Design in Agentic Software Engineering: How Far Can We Go? ALFONSO CANNAVALE, Software Engineering Lab - University of Salerno, Italy ALESSANDRA PARZIALE, Gran Sasso Science Institute, Italy VALERIA PONTILLO, Gran Sasso Science Institute, Italy PATRIZIO PELLICCIONE, Gran Sasso Science Institute, Italy ANDREA DE LUCIA, Software Engineering Lab - University of Salerno, Italy FABIO PALOMBA, Software Engineering Lab - University of Salerno, Italy Agentic AI is shifting developers’ responsibilities from software implementation to software governance. While this paradigm improves productivity, it also raises challenges in ensuring that autonomous coding agents preserve developers’ intentions. Among these, operationalizing fairness-by-design is largely unexplored, as existing approaches provide limited support for representing and propagating fairness requirements throughout agentic development workflows. In this paper, we investigate whether specification-driven development can operationalize fairness-by-design in the agentic development of machine learning systems. We study three coding agents on four machine learning applications, using four prompts that progressively operationalize fairness through nine Fairness Specification Elements, and analyze the traceability of fairness requirements across the generated artifacts, the fairness–accuracy trade-off of the resulting models, and the agents’ design decisions and rationales. The results show that when fairness is operationalized, agents capture almost all fairness requirements in the specification (97.22%) and preserve most of them in the code (82.41%). However, the resulting models are not significantly fairer, as agents implement fairness as a post-hoc audit that neither shapes training nor prevents model delivery. Specifications thus make fairness traceable, but not enforceable, calling for fairness specifications that state what agents should do when requirements are not met, and for workflows that make the fairness decisions agents delegate to developers explicit. Additional Key Words and Phrases: Fairness-by-Design; Agentic Software Engineering; Semi-Formal Specifications; Machine Learning Fairness; Empirical Software Engineering.

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Introduction

Artificial Intelligence (AI) is reshaping software engineering through agentic AI systems, which autonomously plan and execute development tasks and interact with external tools with limited human intervention [33, 50, 66]. As development is delegated to these agents, developers increasingly govern, rather than implement, the resulting software [20]. Although this shift improves productivity [32, 44, 54], delegating engineering decisions to agents raises concerns about the trustworthiness of the resulting software [27, 43, 47, 53, 60]. These concerns are increasingly recognized as a governance problem: for instance, 92% of organizations report governance challenges associated with AI-generated code [25], suggesting that the next frontier of agentic software engineering lies not in generating code autonomously, but in governing the agents that generate it [17]. Among the various governance challenges surrounding AI-generated software, responsible AI has emerged as a major research and industrial priority [45]. Responsible AI advocates the development of AI systems that are transparent, accountable, trustworthy, privacy-preserving, sustainable, and fair [31]. Among these principles, fairness, i.e., the absence of systematic and unjustified discrimination toward individuals or groups affected by algorithmic decisions, is particularly challenging to put into practice because it is inherently context-dependent [40, 41], often subjective [16], and Authors’ Contact Information: Alfonso Cannavale, Software Engineering Lab - University of Salerno, Fisciano, Italy, acannavale@unisa.it; Alessandra Parziale, Gran Sasso Science Institute, L’Aquila, Italy, alessandra.parziale@gssi.it; Valeria Pontillo, Gran Sasso Science Institute, L’Aquila, Italy, valeria.pontillo@gssi.it; Patrizio Pelliccione, Gran Sasso Science Institute, L’Aquila, Italy, patrizio.pelliccione@gssi.it; Andrea De Lucia, Software Engineering Lab - University of Salerno, Fisciano, Italy, adelucia@unisa.it; Fabio Palomba, Software Engineering Lab - University of Salerno, Fisciano, Italy, fpalomba@unisa.it.

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characterized by multiple, sometimes incompatible, formal definitions [10, 34, 56, 59]. Moreover, fairness concerns may arise throughout the machine learning lifecycle [51, 52], making fairness a challenging software engineering problem [22]. To address this challenge proactively, research has promoted the vision of fairness-by-design [4, 7], inspired by the shift-left principle originally introduced in software security [29]: rather than treating fairness as a property to verify after model development, fairness considerations should be embedded from the earliest development stages. Most existing techniques, however, act on the model or its data rather than on the development process that produces them, as in the case of fairness-aware learning algorithms [12, 61], bias mitigation techniques [11, 58, 64], and fairness testing frameworks [13, 30, 35, 42]. Requirements engineering research has started to address this gap with approaches for eliciting and specifying fairness requirements [5, 6, 21], but these approaches mainly support developers in identifying fairness concerns, not in carrying them through development. In agentic development, this gap is critical: once fairness requirements are elicited, it is unclear how they should be represented in the specification artifacts that guide coding agents, and how they can be propagated as agents refine these artifacts into executable code. In this context, we argue that specification-driven development [39] offers a promising foundation for addressing this challenge. Rather than directly translating a natural-language request into executable code, a specification-driven workflow progressively refines developers’ intent through a sequence of intermediate artifacts, including structured specifications, implementation plans, and executable development tasks, that explicitly capture design decisions before implementation begins. Recent frameworks, such as SpecKit,1 embody this paradigm by explicitly structuring the interaction between developers and coding agents. Thus, these artifacts enable fairness requirements to be documented, refined, and propagated throughout development. ◎ Goal. In this work, we investigate whether specification-driven workflows can operationalize fairness-by-design by transforming abstract fairness requirements into explicit, assessable, and traceable development artifacts capable of guiding autonomous coding agents. We study this problem in the context of agentic development of machine learning systems, where preserving fairness requirements throughout the development process is essential to obtaining fair models. Specifically, we investigate whether the SpecKit workflow enables coding agents to preserve fairness requirements across specification, planning, and implementation, and how these requirements influence the resulting machine learning pipelines. We hypothesize that making fairness explicit and measurable in semi-formal specifications reduces ambiguity, improves intent preservation, and enhances the transparency and auditability of agentic decisions. We further examine whether these improvements lead to fairer machine learning models. To evaluate this hypothesis, we design four representative development scenarios corresponding to increasing levels of fairness expressiveness. The first represents a baseline in which fairness is absent from the developer request. The second introduces fairness only implicitly, requiring the agent to infer relevant fairness concerns from the application context. The third explicitly requests fairness while leaving its interpretation to the agent. Finally, the fourth operationalizes fairness through nine Fairness Specification Elements (FSEs), which we derive from the requirements engineering literature on fairness and which define the information required to specify fairness, from the affected subjects and protected attributes to the fairness metrics and acceptance thresholds used to assess it. This progression allows us to isolate the contribution of fairness awareness, explicitness, and operationalization in guiding agent behavior. 1 The SpecKit toolkit: https://github.com/github/spec-kit.

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Our empirical evaluation involves three state-of-the-art coding agents tasked with developing machine learning solutions for four representative application domains, using the Adult Income, COMPAS, Diabetes, and Bank Marketing datasets. We assess the effectiveness of specificationdriven governance from three complementary perspectives. First, we investigate process-level governance by measuring fairness traceability, namely the extent to which fairness-related information introduced in the original request is preserved across the specifications, implementation plans, and source code generated by the agents. Second, we investigate outcome-level governance by evaluating whether preserving fairness intent throughout the development process translates into fairer machine learning models while maintaining predictive performance. Third, we examine design decisions and rationales by combining a static analysis of the generated code with the manual coding of the code and design documents, to understand how agents implement fairness requirements and why they make the decisions they make. Our results show that specification-driven workflows govern development, but not its outcome. When fairness is explicitly operationalized, agents capture nearly all requirements in specifications (97.22%) and preserve most in source code (82.41%), while domain context alone has little effect. Losses occur mainly during planning and task generation, affecting fairness intent more than quantitative criteria. Yet, preservation does not yield fairer models, with no significant differences in predictive performance or group fairness across prompt levels. Agents treat fairness as a post-hoc audit, adopting neither established mitigation techniques nor checks that block delivery when requirements are unmet. Although acknowledging these limitations, they defer responsibility to human reviewers. Thus, specifications make fairness traceable and verifiable, but not enforceable unless they also define how agents should respond to unmet requirements. To sum up, this paper provides the following contributions. • To the best of our knowledge, the first empirical study on how fairness operationalization affects the preservation of fairness requirements across agent-generated artifacts and the performance and fairness of the resulting models. • A characterization of how coding agents implement fairness requirements and justify their decisions, showing that they treat fairness as a post-hoc audit and delegate fairness decisions to human reviewers. • A replication package with prompts, generated artifacts, annotations, coding sheets, and analysis scripts [9]. 2

Background & Related Work

This section presents the background and related work relevant to our study. 2.1

Background

Recent advances in LLMs have enabled the development of autonomous coding agents, giving rise to the paradigm of Agentic Software Engineering. Unlike traditional code completion systems, modern coding agents can interpret high-level development goals, reason about implementation strategies, interact with external tools, and autonomously produce software artifacts throughout the development lifecycle. As a consequence, the role of developers is progressively shifting from directly implementing software to supervising autonomous agents by defining objectives, constraints, and expected system behavior [33, 50, 66]. This evolution also changes how developers interact with software engineering tools. In traditional AI-assisted workflows, developers typically interact with coding assistants via naturallanguage prompts that are translated directly into executable code. While effective for isolated programming tasks, this code-centric paradigm requires agents to simultaneously infer functional

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requirements, architectural decisions, implementation strategies, and quality constraints from a single prompt. Consequently, much of the reasoning process remains implicit, making it difficult to inspect how developer intent is interpreted and propagated during software generation. To address these limitations, recent work has advocated Specification-Driven Development (SDD), a development paradigm in which software construction is guided by structured specifications rather than direct code generation. Instead of immediately translating a natural-language request into source code, SDD progressively refines developer intent through a sequence of intermediate software engineering artifacts that explicitly capture requirements, design decisions, implementation strategies, and development tasks before implementation begins. Under this paradigm, specifications become first-class software artifacts that reduce ambiguity, improve traceability, and provide a structured representation of developer intent throughout the development process. In this work, we adopt the workflow implemented by SpecKit, an open-source framework that operationalizes this specification-driven paradigm. Starting from a natural-language request, the workflow incrementally generates four development artifacts [24]. First, the request is transformed into a structured software specification (spec.md) describing functional requirements and acceptance criteria. The specification is then refined into a technical implementation plan (plan.md), which documents architectural decisions, implementation strategies, and supporting design considerations. Next, the implementation plan is decomposed into a set of executable development tasks (tasks.md), providing a fine-grained description of the activities required to implement the system. Finally, the coding agent executes these tasks to generate the final source code. Collectively, these intermediate artifacts provide an explicit representation of the development process, allowing developers to inspect, audit, and reason about how information introduced in the original request evolves throughout the software development process. Rather than treating code generation as a single inference step, specification-driven workflows expose the intermediate reasoning stages performed by autonomous agents, providing an opportunity to analyze how requirements are preserved, refined, or lost across successive development artifacts. 2.2

Related Work

Our work lies at the intersection of three active research directions: fairness engineering for AIenabled software systems, responsible software engineering with autonomous coding agents, and specification-driven software development. In the following, we discuss the literature most closely related to our work and position our contribution within these research areas. Fairness and Responsible AI in Software Engineering. Fairness has increasingly emerged as a major quality concern for AI-enabled software systems. Early work introduced the notion of software fairness, arguing that fairness should be treated as a software engineering concern rather than exclusively as a machine learning problem [7]. This vision has motivated extensive research on fairness-aware software engineering, including fairness-aware machine learning processes [22], bias mitigation techniques [11, 64], fairness-aware learning algorithms [12], fairness testing methodologies [13, 30, 35, 42], and empirical studies investigating developers’ adoption of fairness-aware practices and toolkits [10, 57, 59]. More recently, the community has shifted its attention toward integrating fairness and ethical concerns into the software development lifecycle itself. Existing work has proposed techniques for understanding fairness requirements [3], eliciting fairness requirements from practitioners [5], recommending context-aware fairness requirements [21], performing fairness hazard analysis [6], systematically specifying fairness requirements [48], and automatically identifying ethical requirements through machine learning models [23]. Collectively, these studies establish fairness requirements engineering as an emerging research direction, providing methodologies for eliciting, documenting, and operationalizing fairness concerns during software

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development. However, existing studies largely assume traditional development workflows and primarily focus on fairness elicitation, specification, or model-level mitigation. Recent evidence from gray literature [37] further highlights that fairness requirements remain weakly integrated into early software engineering activities and are rarely managed consistently throughout the SDLC. Consequently, little is known about how fairness requirements can be preserved while autonomous coding agents progressively transform software specifications into executable systems. Responsible Agentic Software Engineering. The rapid adoption of autonomous coding agents has recently shifted attention from code-generation capabilities to broader concerns related to governance, trustworthiness, and responsible software engineering. Recent vision papers argue that the increasing autonomy of coding agents requires new engineering practices to ensure reliability, transparency, security, and developer supervision throughout the software development lifecycle [17, 33, 45, 47, 50, 53]. Complementing these vision papers, recent empirical studies have started investigating the ethical implications of AI-assisted software engineering. Existing work has explored ethically sourced code generation [63], social bias in LLM-assisted software engineering activities [43], bias in LLM-based code generation [30], and fairness evaluation of coding agents through software engineering tasks [18]. These studies consistently show that autonomous coding agents may unintentionally introduce or amplify ethical issues during software development, motivating the need for explicit governance mechanisms. Nevertheless, current approaches primarily focus on evaluating or mitigating these issues after software artifacts have already been generated, providing limited support for guiding autonomous agents through explicit ethical requirements during software development. Specification-Driven Development. As autonomous coding agents become increasingly capable of performing software engineering tasks, recent research has begun to move beyond prompt-centric code generation toward more structured development workflows. Rather than directly translating a natural-language request into executable code, these approaches progressively refine developer intent through intermediate software engineering artifacts, such as software specifications, implementation plans, and task decompositions, before implementation begins. This paradigm, commonly referred to as Specification-Driven Development (SDD), was originally introduced to improve development traceability and reduce ambiguity by treating specifications as first-class software artifacts [39]. Recent LLM-based software engineering frameworks have revisited this paradigm by promoting specification-centric workflows in which structured software engineering artifacts explicitly guide autonomous coding agents throughout the software development process. Frameworks such as SpecKit [24] organize software generation through a sequence of intermediate artifacts, while Spec-Driven AI [8] extends this vision by coordinating multiple specialized agents around specifications, planning activities, architectural decisions, and software lifecycle artifacts. Research Gap. Existing research has advanced fairness engineering for AI-enabled software systems, responsible agentic software engineering, and specification-driven development workflows. However, whether structured software engineering artifacts can operationalize fairness-by-design by preserving fairness requirements throughout an agentic software development workflow remains unexplored. We address this gap by investigating how progressively operationalized fairness specifications influence both fairness preservation across intermediate software engineering artifacts and the fairness of the resulting machine learning systems.

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Research Goal and Questions

The goal of this study is to investigate whether specification-driven workflows can operationalize fairness-by-design in the agentic development of machine learning systems. The purpose is to understand whether the degree to which fairness requirements are explicitly represented in software specifications determines if agents capture and preserve them across development artifacts, if the resulting models achieve better group fairness without sacrificing predictive performance, and how agents implement these requirements and justify their design decisions, rather than leaving fairness to their autonomous interpretation. The perspective is that of software engineering researchers investigating governance mechanisms for autonomous coding agents, and of practitioners developing machine learning systems in socially sensitive domains. To this end, we formulate three complementary research questions (RQs). First, we examine whether progressively operationalizing fairness within software specifications improves the ability of autonomous coding agents to capture, preserve, and propagate fairness requirements as these requirements are progressively refined into software engineering artifacts. Hence, we ask: RQ1 – Process-Level Governance To what extent do autonomous coding agents capture and preserve fairness requirements across software engineering artifacts? Second, we investigate whether increasingly operationalized fairness specifications ultimately translate into machine learning models exhibiting improved fairness while maintaining predictive performance. In particular, we seek to understand the extent to which explicitly operationalizing fairness during specification influences the fairness-accuracy trade-off of the resulting models. Hence, we ask: RQ2 – Outcome-Level Governance How does the operationalization of fairness within software specifications affect the predictive performance and group fairness of the resulting machine learning models? Finally, we investigate how autonomous coding agents address fairness requirements when building the machine learning pipeline and why they make certain design decisions. In particular, we seek to understand whether and through which mechanisms agents address fairness in the generated code, such as fairness toolkits, changes to the training procedure, or a specific handling of protected attributes, and which rationales they document in the design artifacts for their decisions, including the decision not to mitigate bias. Hence, we ask: RQ3 – Design Decisions and Rationales How do autonomous coding agents implement fairness requirements in the generated machine learning pipelines, and what rationales do they document for their design decisions? Figure 1 provides an overview of our experimental methodology. In phase (1) “Fairness Specification Design”, we defined four development requests with increasing levels of fairness expressiveness. In phase (2) “Specification-Driven Framework”, each prompt variant was instantiated and executed across three autonomous coding agents and four application domains. In each run, the agent progressively refined the request into a specification, a technical plan, a task list, and, finally, the source code. In phase (3) “Governance Evaluation”, we analyzed the artifacts, models, and code produced in each run from three perspectives. For process governance (RQ1 ), we measured FSE coverage in each artifact. For outcome governance (RQ2 ), we evaluated the predictive performance and group fairness of the models. Finally, for design decisions and rationales (RQ3 ), we combined

Specification-Driven Governance for Fairness-by-Design in Agentic Software Engineering: How Far Can We Go?

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Fairness Specification Design

Specification-Driven Framework

Governance Evaluation

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RQ1 - Process Governance Fairness Coverage Score per artifact:

LEVEL OF EXPRESSIVENESS

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Audits artifacts & models

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RQ2 - Outcome Governance Predictive Performance: Accuracy - Precision - Recall - F1 Score Group Fairness: Average Absolute Odds Difference - False Discovery Rate Difference - Disparate Impact

Application Domains

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Adult - Compas - Diabetes - Bank Marketing

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RQ3 - Decisions and Rationales Static Analysis and Manual Coding:

3 Coding Agents x 4 Datasets x 4 Prompt Levels

48 runs

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Fig. 1. Overview of the experimental design: (1) four specification expressiveness levels incorporating 0 to 9 FSEs; (2) full-factorial execution across 3 coding agents and 4 application domains (48 runs), each refining the request through 4 artifact stages; and (3) evaluation of process governance (RQ1 ), outcome governance (RQ2 ), and design decisions and rationales (RQ3 ).

static analysis and manual coding to examine how agents acted on fairness in the code and why they made these choices, based on the documentation provided in the artifacts. 4

Research Method

To address the RQ𝑠 , we designed an empirical study to investigate whether and how specificationdriven workflows can operationalize fairness-by-design in agentic software engineering. First, we defined what information a fairness specification should communicate, through an intent engineering process resulting in the definition of the Fairness Specification Elements (Section 4.1). Second, we defined how this information was progressively communicated to agents through a prompt engineering process that produced four specification variants with increasing levels of fairness operationalization (Section 4.2). Third, we adopted the artifact refinement workflow implemented in SpecKit to transform these specifications into software engineering artifacts (Section 4.3). Fourth, we instantiated this workflow across target coding agents and application domains within a controlled experimental execution (Section 4.4). Finally, we defined the analysis protocols for process governance (RQ1 , Section 4.5), the statistical procedure for outcome governance (RQ2 , Section 4.6), and the combination of static analysis and manual coding used to examine design decisions and rationales (RQ3 , Section 4.7). 4.1

Intent Engineering

Fairness is often discussed as a high-level quality attribute. Nonetheless, autonomous coding agents require explicit and actionable guidance that can be interpreted and propagated throughout a specification-driven workflow. Consequently, our study required a structured representation of fairness requirements that can be systematically embedded into software specifications and subsequently traced across the software engineering artifacts generated by the coding agents. To this end, we introduced the notion of Fairness Specification Elements (FSEs), namely the fundamental specification constructs that collectively describe how fairness should be interpreted, operationalized, and evaluated during the development of machine learning systems. To derive the FSEs, we first identified the methodological principles that the literature considers essential for specifying fairness requirements. To this end, we manually screened the papers

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published between 2020 and 2026 in the flagship Requirements Engineering venues, i.e., the IEEE International Requirements Engineering Conference2 and the International Working Conference on Requirements Engineering: Foundation for Software Quality,3 together with the proceedings of the three major software engineering conferences, i.e., the IEEE/ACM International Conference on Software Engineering,4 the ACM International Conference on the Foundations of Software Engineering,5 and the IEEE/ACM International Conference on Automated Software Engineering.6 We specifically searched for studies discussing how fairness should be represented within software requirements and software specifications, with the goal of identifying the fundamental information that a fairness specification should contain. Finally, we applied backward snowballing [62] to the selected studies to identify additional methodological papers that elaborate on specific aspects of fairness engineering relevant to our scope. It is worth noting that this search was not intended to be systematic or exhaustive: it was conceived as a scoping activity aimed at identifying representative methodological work that defines the essential components of fairness requirements, which could subsequently be consolidated into a unified specification framework. This process resulted in the identification of a small body of literature discussing how fairness should be addressed during requirements engineering activities [3, 21, 48]. Building on these studies, we adopted a deductive concept-extraction process [14] to derive the FSEs. Table 1 summarizes the identified literature principles, the rationale for their inclusion, their operationalization, and the resulting FSEs. Specifically, we first extracted the fairness-related concepts explicitly discussed in the studies and interpreted them as candidate specification constructs. For example, Ramadan et al. [48] argue that a well-defined fairness requirement should explicitly identify the characteristics requiring protection. This concept was translated into the specification-oriented element Protected Attributes (FSE2 ), capturing whether a software specification explicitly identifies the sensitive attributes (e.g., sex, race, or age) relevant to the fairness requirement. Subsequently, four of the authors jointly analyzed the extracted concepts through a series of iterative design discussions aimed at: (i) consolidating semantically overlapping concepts; (ii) reformulating the concepts as specification-oriented elements describing the information that should be explicitly communicated within a software specification, rather than implementation-specific decisions; and (iii) identifying additional specification constructs required to support specification-driven development but not explicitly covered by the selected studies. For instance, during these discussions, concepts describing alternative ways of treating protected attributes were reformulated into the more general notion of Protected Attribute Assessment (FSE9 ), which captures whether the specification explicitly states if and how protected attributes are expected to be considered when assessing fairness, independently of any particular fairness approach. The discussions continued until consensus was reached on the final set of FSEs. The resulting FSEs capture complementary dimensions of fairness specifications. FSE1 and FSE8 define the foundational decision context in which fairness applies; FSE2 and FSE3 introduce domain-specific sensitive features and justified operational constraints; FSE4 –FSE7 specify the formal fairness objectives alongside the quantitative criteria and metrics through which they are operationalized and assessed; and FSE9 captures explicit evaluation guidance regarding how protected attributes should be accounted for during fairness assessment.

2 The IEEE International Requirements Engineering Conference: https://re-conferences.org.

3 The International Working Conference on Requirements Engineering: Foundation for Software Quality: https://2026.refsq.

org/series/refsq.

4 The IEEE/ACM International Conference on Software Engineering: https://www.icse-conferences.org.

5 ACM International Conference on the Foundations of Software Engineering: https://www.esec-fse.org.

6 The IEEE/ACM International Conference on Automated Software Engineering: https://conf.researchr.org/series/ase.

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Table 1. Derivation and operationalization of the Fairness Specification Elements (FSEs).

Literature Principle

Rationale

Fairness requirements should explicitly identify the individuals affected by software decisions [48]. Fairness requirements should explicitly specify the protected characteristics to consider [48]. Fairness requirements should specify domain assumptions and constraints governing attribute usage [21, 48]. Fairness requirements should explicitly define the intended fairness objective [3, 48].

Fairness can only be assessed with respect to a well-defined population of decision subjects. Protected attributes define the dimensions along which potential discrimination is assessed. The applicability and boundaries of fairness depend on domain-specific constraints and exceptions. Different fairness notions capture different ethical objectives and may lead to different engineering decisions. Fairness requirements should define Fairness objectives must be transmeasurable acceptance criteria [3]. lated into quantitative conditions that can be verified. Fairness should be operationalized Verification requires explicitly specthrough appropriate fairness defini- ifying how fairness is measured. tions and metrics [3]. Fairness evaluation should consider Aggregate performance may conthe behavior of different protected ceal disparities affecting individual groups [3, 48]. subgroups. Fairness requirements should explic- Fairness can only be interpreted itly define the decision context in with respect to a well-defined dewhich fairness applies [21, 48]. cision scenario. Fairness assessment requires explic- Whether protected attributes are itly specifying the role of protected considered influences the interpreattributes [21, 48]. tation of fairness results.

4.2

Operationalization

Resulting FSE

Explicit identification of the individuals or groups affected by software decisions. Explicit designation of the sensitive attributes relevant to the fairness requirement. Explicit identification of situations or constraints in which attribute usage is justified. Explicit statement of the fairness objective pursued by the system.

FSE1 – Decision Subjects

Quantitative acceptance criteria determining whether fairness objectives are satisfied. Explicit identification of the mathematical fairness metrics adopted for evaluation. Requirement to report predictive performance and fairness separately across protected subgroups. Explicit definition of the decision task to which the fairness requirement applies. Explicit statement of whether protected attributes should be considered during fairness assessment.

FSE5 – Acceptance Thresholds

FSE2 – Protected Attributes FSE3 – Domain Constraints & Exceptions FSE4 – Fairness Intent

FSE6 – Fairness Metrics FSE7 – Disaggregation Analysis FSE8 – Decision Scope

FSE9 – Protected Attribute Assessment

Prompt Engineering

Upon defining the FSEs, the next step of our protocol consisted of embedding them into software specifications with progressively increasing levels of fairness operationalization. To this end, we designed four prompt variants—minimal, implicit, explicit, and operationalized—that systematically vary the amount of fairness-related information communicated to the autonomous coding agent while keeping all other aspects of the development request constant, including the application domain, target dataset, and underlying machine learning task. The dataset-specific values used to replace the placeholders in these generic prompt templates are presented in Section 4.4. The FSEs were introduced according to their contribution to the progressive operationalization of fairness requirements. We first introduced the elements required to establish the decision context in which fairness becomes relevant. We then incorporated the elements that make fairness an explicit aspect of the specification while still leaving its interpretation largely to the agent. Finally, we introduced the elements that operationalize fairness through explicit definitions, quantitative evaluation criteria, and reporting requirements. Consequently, each prompt extends the previous one by incorporating an additional subset of FSEs while preserving all previously specified information. The minimal prompt serves as the baseline condition and contains only the information required to perform the underlying machine learning task. Specifically, it defines the prediction objective, identifies the target dataset, and specifies the basic technical constraints required for implementation. No FSEs are explicitly communicated, leaving the coding agent without any guidance regarding fairness considerations. Listing 1 reports the generic prompt template adopted for this condition. Write a Python script to implement a binary classification model on the <DATASET> dataset.

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Specifications: - Use Scikit-Learn - Target variable: <TARGET_VARIABLE> - Type of data: tabular data - Save the trained model to disk as model.joblib

Listing 1. Generic template of the minimal prompt.

The implicit prompt extends the minimal condition by introducing contextual information about the application domain. In particular, it describes the decision subjects affected by the system (FSE1 ) and explicitly defines the decision task to which the prediction applies (FSE8 ). This additional context may expose the social relevance of the application and implicitly suggest potential fairness concerns. However, the prompt neither identifies protected attributes nor explicitly requires the agent to consider fairness. Consequently, any recognition and operationalization of fairness remains entirely dependent on the agent’s interpretation of the application context. Listing 2 reports the generic template. Write a Python script to implement a binary classification model on the <DATASET> dataset. Dataset description: <DATASET_DESCRIPTION> Specifications: - Use Scikit-Learn - Target variable: <TARGET_VARIABLE> - Type of data: tabular data - Save the trained model to disk as model.joblib - State the subjects to which the prediction task applies - State the decision task to which the prediction applies

Listing 2. Generic template of the implicit prompt.

The explicit prompt extends the implicit condition by enriching the specification with explicit fairness-related information. In addition to the contextual elements introduced by the previous variant (FSE1 , FSE8 ), it explicitly identifies the protected attributes relevant to the application domain (FSE2 ) and requires the specification to communicate any domain-specific assumptions, constraints, or justified exceptions governing their use (FSE3 ). Consequently, fairness is no longer solely implied by the application context, but becomes an explicit aspect of the specification. Nevertheless, the prompt still leaves fairness conceptually specified, as it does not prescribe a fairness definition, quantitative acceptance criteria, mathematical fairness metrics, or subgroupspecific evaluation requirements. As a result, the coding agent is made aware of the relevance of fairness while retaining substantial freedom in determining how fairness should be operationalized. Listing 3 reports the generic template adopted for this condition. Write a Python script to implement a binary classification model on the <DATASET> dataset. Dataset description: <DATASET_DESCRIPTION> Specifications: - Use Scikit-Learn - Target variable: <TARGET_VARIABLE> - Type of data: tabular data - Save the trained model to disk as model.joblib - Sensitive features: <PROTECTED_ATTRIBUTES> - State the subjects to which the prediction task applies - State the decision task to which the prediction applies

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- State any domain-specific assumptions, constraints, or justified exceptions relevant to ensuring fairness

Listing 3. Generic template of the explicit prompt.

The operationalized prompt extends the explicit condition by translating fairness into measurable and verifiable specification elements. In addition to preserving the contextual and fairness-related information introduced by the previous variants (FSE1 , FSE8 , FSE2 , FSE3 ), it requires the specification to state the fairness definition to be satisfied (FSE4 ), identify the quantitative fairness metrics used for its assessment (FSE6 ), and define the corresponding acceptance thresholds (FSE5 ). It also requires fairness and predictive performance to be reported separately across the relevant protected groups (FSE7 ). Finally, the prompt requires the specification to document the protected attributes and groups considered during fairness assessment (FSE9 ). Consequently, fairness is no longer communicated solely as a contextual concern, but through explicit criteria defining its scope, measurement, and assessment. Listing 4 reports the generic template adopted for this condition. Write a Python script to implement a binary classification model on the <DATASET> dataset. Dataset description: <DATASET_DESCRIPTION> Specifications: - Use Scikit-Learn - Target variable: <TARGET_VARIABLE> - Type of data: tabular data - Save the trained model to disk as model.joblib - Sensitive features: <PROTECTED_ATTRIBUTES> - State the fairness definition that should be satisfied - State the subjects to which the prediction task applies - State the decision task to which the prediction applies - State any domain-specific assumptions, constraints, or justified exceptions relevant to ensuring fairness Evaluation: - Implement the following performance metrics: Accuracy, Precision, Recall, and F1-score - Evaluate fairness using the following metrics for <PROTECTED_GROUPS>: Average Absolute Odds Difference (AAOD), False Discovery Rate Difference (FDRD), and Disparate Impact (DI) - Specify an acceptance threshold for each fairness metric - State the protected attributes and groups considered during fairness assessment

Listing 4. Generic template of the operationalized prompt.

All prompt variants share the same technical constraints, which we kept constant by design. The scikit-learn constraint fixes the model interface so every model can be loaded and evaluated by the same harness (Section 4.6), and the request to save the trained model ensures every run yields a model to evaluate. Neither constraint prevents fairness interventions: scikit-learn estimators accept per-sample weights in fit(), which allow reweighing the protected groups, and expose predicted probabilities, on which group-specific decision thresholds can be applied. No variant prescribes a mitigation technique or states whether saving the model should depend on the fairness assessment; we discuss the implications of these choices in Sections 6 and 7. The formulation of the prompt templates was iteratively refined through a series of internal pilot experiments aimed at identifying a syntax that consistently conveyed the intended FSEs while minimizing ambiguities introduced by prompt wording. During this process, we followed established prompt engineering recommendations [2], including the use of clear and explicit

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instructions, consistent prompt structure, standardized formatting, and unambiguous placeholders, while preserving the same prompting strategy across all experimental conditions. The resulting prompt variants constitute the experimental treatments adopted throughout the study and serve as the input to the specification-driven workflow described next. 4.3

Specification-Driven Workflow

The next step of our protocol consisted of operationalizing the resulting software specifications into a sequence of software engineering artifacts that could be systematically analyzed throughout the development process. Such an analysis requires a development workflow that explicitly materializes each intermediate artifact rather than directly producing executable code. To this end, we leveraged SpecKit [24], an open-source specification-driven development framework that structures agentic software development as a sequence of explicit refinement stages. Rather than prompting a coding agent to directly generate source code from a natural-language request, SpecKit progressively transforms an initial software specification into increasingly implementation-oriented artifacts, each representing a distinct stage of the software development lifecycle. This artifact refinement process enables complete traceability from initial requirements to final implementation, providing the observability needed to analyze not only how fairness requirements evolve, are preserved, or are modified throughout development, but also which design decisions agents make along the way and how they justify them. Specifically, SpecKit operationalizes software development through an Artifact Refinement Chain composed of four sequential stages: (1) ‘Requirements Specification’ (/speckit.specify). Refines the initial natural-language request into a structured software specification (spec.md) describing the system requirements, expected behavior, and acceptance criteria in a technology-agnostic manner. During this stage, the agent was granted workspace access to the benchmark dataset (.csv) to inspect its schema and derive the information required to formulate the specification. In our case, this stage captures how the fairness requirements expressed in the initial prompt are translated into an explicit software specification, thereby enabling us to analyze how different levels of fairness operationalization influence the completeness and preservation of fairness information in the generated specification. (2) ‘Technical Planning’ (/speckit.plan). Refines the software specification into a technical design package centered on plan.md and complemented by supporting artifacts (e.g., data-model.md and research.md). These artifacts define the implementation strategy, including architectural decisions, model selection, feature representations, and data processing choices. In our case, this stage shows how different levels of fairness operationalization propagate into the technical design, enabling us to analyze the extent to which fairness requirements influence the design and methodological decisions underlying the resulting technical solution, as well as the rationales agents provide for these decisions. (3) ‘Task Decomposition’ (/speckit.tasks). Refines the technical design into an executable implementation plan (tasks.md) by decomposing the system into a hierarchy of development tasks, spanning project setup, data preparation, model implementation, validation, and verification activities. In our case, this stage captures how different levels of fairness operationalization are translated into concrete development activities, enabling us to analyze whether fairness requirements systematically influence the implementation plan. (4) ‘Automated Implementation’ (/speckit.implement). Refines the implementation plan into executable source code (code.py) by autonomously executing the generated tasks and managing dependencies, workspace interactions, and execution logs. This stage allows us to assess whether fairness requirements communicated in the initial specification are

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ultimately preserved in the generated source code, thereby completing the end-to-end traceability of fairness requirements across the artifact refinement chain. It also enables us to examine how agents address fairness in practice, such as by handling protected attributes or implementing fairness checks, through the generated code and execution logs. By collecting and auditing the complete sequence of artifacts generated throughout this refinement chain (spec.md → {plan.md, data-model.md, research.md} → tasks.md → code.py), we obtained a complete trace of how fairness requirements propagated across successive software engineering artifacts and how agents made design decisions throughout this process. This trace supports all three research questions by enabling us to evaluate the preservation of fairness information throughout the development process (RQ1 ), relate these observations to the predictive performance and fairness of the models (RQ2 ), and examine how agents implement fairness requirements and the rationales for their design decisions (RQ3 ). 4.4

Experimental Context

To observe how specification-driven governance operates across varying agent architectures and decision contexts, our experimental setup instantiated a full-factorial environment that combines autonomous coding agents with tabular machine learning benchmarks. Target Autonomous AI Coding Agents. We evaluated three state-of-the-art CLI-driven coding agents representing distinct architectural paradigms and foundation model families: Claude Code [1] (powered by Claude 5 Sonnet), Antigravity CLI [26] (powered by Gemini 3.6 Flash), and OpenAI Codex [38] (powered by GPT-5.6-Terra). All agents were deployed in a low-effort / lowreasoning operational mode with extended multi-pass reasoning routines disabled. Methodologically, this choice of configuration was grounded in two primary considerations. First, it mirrors realistic industrial environments in which developers operate under strict execution-latency constraints, token quotas, and operational budgets [28]. Second, restricting extended multi-pass reasoning isolates the governing role of the specification artifacts by minimizing confounding autonomous selfcorrections, thereby ensuring that requirement preservation directly reflects the explicit guidance provided throughout the specification chain. Application Domains. We selected four widely recognized tabular machine learning datasets in the fairness literature [19]: • Adult Income [55]: an economics dataset derived from the 1994 U.S. Census, used to implement a binary classification task predicting whether an individual’s annual income exceeds $50,000. Protected sensitive attributes include race, sex, and age. • COMPAS [46]: a criminal justice dataset comprising defendant records from Broward County, Florida, supporting the prediction of two-year recidivism risk. Protected sensitive attributes include race, sex, and age. • Diabetes [15]: a healthcare dataset containing ten years of clinical care records, collected between 1999 and 2008 from 130 US hospitals, including patient demographics, in-hospital procedures, and diagnoses, used to predict early hospital readmission. Protected sensitive attributes include sex and age. • Bank Marketing [36]: a marketing dataset of telemarketing phone calls made by a Portuguese bank between 2008 and 2010, including client-specific and call-related features, used to predict whether a client subscribes to a term deposit. Protected sensitive attributes include age.

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Experimental Execution. The empirical evaluation encompassed a full factorial design across 4 prompt expressiveness levels, 3 coding agents, and 4 application domains, resulting in 48 experimental executions. To prevent data contamination during agentic development, each dataset was split into a stratified 80/20 development/hold-out split. For each run, an isolated workspace was initialized containing the target prompt variant and only the 80% dataset partition, which the agent accessed to inspect schema definitions, train models, and perform internal validation. The remaining 20% hold-out partition was strictly isolated from the agentic environment and reserved for downstream model evaluation. Upon initialization, the target coding agent executed the four-stage SpecKit refinement chain, generating the intermediate and final software artifacts (spec.md → planning package → tasks.md → code.py). These artifacts were directly evaluated to assess process-level requirement traceability (RQ1 ), the resulting trained machine learning models were evaluated on the isolated hold-out partition to assess outcome-level quality and equity (RQ2 ), and the generated source code and design documents were analyzed to examine design decisions and rationales (RQ3 ). 4.5

RQ1 Analysis Protocol: Process Governance

To evaluate how fairness requirements are captured and preserved across agentic workflows (RQ1 ), we implemented an automated auditing pipeline based on the LLM-as-an-Annotator paradigm, following the empirical guidelines for LLM-based software engineering research [2]. We employed Qwen3.8-27B (accessed via OpenRouter7 ) configured with deterministic decoding (temperature = 0.0) as the primary annotator. Methodologically, selecting a model from an independent foundation model family (Alibaba) that does not overlap with any of the evaluated coding agent families (Claude, Gemini, and OpenAI) mitigates potential self-enhancement bias [65]. For each experimental execution, the annotator systematically inspected the generated development artifacts—spec.md, the technical planning package (plan.md, data-model.md, research.md), tasks.md, and the generated implementation code—evaluating the binary presence (1 for present, 0 for absent) of each of the nine Fairness Specification Elements defined in Section 4.1. The annotator executed the following detection checklist: • FSE1 – Decision Subjects. Does the specification identify who the subjects are on whom the system decides? • FSE2 – Protected Attributes. Does the specification explicitly name protected attributes? • FSE3 – Domain Constraints & Exceptions. Does the specification identify situations or constraints in which the use of protected attributes is considered justified? • FSE4 – Fairness Intent. Does the specification explicitly state which definition of fairness it is intended to satisfy? • FSE5 – Acceptance Thresholds. Does the specification indicate a quantitative threshold for judging fairness achieved? • FSE6 – Fairness Metrics. Does the specification identify the fairness metric to be satisfied? • FSE7 – Disaggregation Analysis. Does the specification report the metrics by group? • FSE8 – Decision Scope. Does the specification explicitly define the decision to which the fairness requirement applies? • FSE9 – Protected Attribute Assessment. Does the specification state whether protected attributes are expected to be considered when assessing fairness? To ensure construct validity and prevent LLM hallucinations [2], our protocol enforced a verbatim evidence rule, namely, an FSE𝑖 was marked as present if the annotator extracted an exact text snippet from the target artifact as supporting evidence for its assessment. An automated script 7 https://openrouter.ai/qwen/qwen3.8-27b

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systematically validated that every extracted snippet appeared verbatim in the corresponding source file. Discrepancies or ungrounded extractions were automatically flagged as null and queued for human inspection. Of the 1,728 text-extraction checks, the annotator marked 731 elements as present and 992 as absent; the deterministic validation script flagged only five discrepancies (0.29%), all resulting from minor markdown spacing variances rather than semantic hallucinations. The complete prompt template, system instructions, structured output schema, and results provided to the annotator are available in our online replication package [9]. To validate the reliability of the LLM annotator against human judgment [2], the first two authors conducted an independent manual audit of a stratified sample of 12 complete execution pipelines (representing 25% of the 48 experimental runs). Specifically, the sample covered 4 full pipeline runs per coding agent across all 4 prompt expressiveness variants, auditing all artifacts generated across the four refinement stages (spec.md, the technical planning package, tasks.md, and the generated implementation codebase). We adopted this end-to-end validation setup to evaluate: (i) the annotator’s accuracy across individual artifact stages; (ii) its annotation consistency along the sequential refinement chain (spec → plan → task → code); and (iii) whether annotation quality remained consistent across different coding agents and prompt expressiveness levels. Inter-rater agreement between the human raters, as well as between human consensus and the LLM annotator, was evaluated using Cohen’s Kappa (𝜅). The blind human audit yielded moderate inter-rater agreement (𝜅 = 0.5982; 81.94% raw agreement across 432 evaluations, with 78 discrepancies). It is worth noting that this divergence did not stem from conflicting interpretation standards, as both raters showed nearly identical positivity rates (35.4% vs. 32.6%) and discrepancies were bidirectional (45 instances where only Rater 1 detected an element versus 33 where only Rater 2 did). Furthermore, 82% of human discrepancies concentrated on narrative governance clauses (FSE3, FSE8, FSE9 ) dispersed across lengthy planning packages and codebase files, whereas agreement on mathematical constructs (FSE5, FSE6 ) was near-unanimous. By contrast, the LLM annotator, Qwen3.8-27B, achieved almost perfect agreement against the shared human consensus baseline (𝜅 = 0.8673, 94.07% raw agreement across 354 consensus evaluations). The model captured every consensus-positive element (100% recall across 108 instances) and confirmed 91.5% of negative instances (225/246). In the 78 split-decision cases where human raters diverged, the model split evenly between raters (46.2% agreement with Rater 1, 53.8% with Rater 2), suggesting that it consistently retrieved textual evidence that individual human raters occasionally overlooked. We therefore considered the annotator reliable for the analysis of RQ1 . Building on these validated annotations, we characterized process governance by analyzing RQ1 across the full 4 × 4 experimental matrix (Prompt Expressiveness × Artifact Stage) through a primary completeness metric, the Fairness Coverage Score (𝐹𝐶𝑆). We used this metric to quantify how much of the fairness specification an agent captures or preserves in each artifact, while ensuring comparability across experimental runs and artifact stages. The resulting measure directly operationalizes RQ1 . Specifically, since each FSE𝑖 was assessed as a binary indicator, a coverage score provides a direct way to aggregate the individual elements. We assigned equal weight to the nine FSEs, as they were derived through the same systematic process and capture complementary facets of a fairness specification. Moreover, normalizing the score to [0, 100%] facilitates comparison across artifacts that differ substantially in format and level of abstraction. Count of FSEs present in artifact 𝑎 under prompt level 𝑝 × 100% (1) 9 The evaluation of RQ1 was synthesized by assessing the complete lifecycle matrix of 𝐹𝐶𝑆 (𝑝, 𝑎), evaluating the extent to which autonomous agents establish end-to-end fairness governance from initial request to executable source code (𝐹𝐶𝑆 (𝑝, code.py)). Specifically, we analyzed this 𝐹𝐶𝑆 (𝑝, 𝑎) =

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primary finding through the specification elicitation effect, which evaluates how initial prompt expressiveness drives requirement capturing by analyzing variations in 𝐹𝐶𝑆 (𝑝, 𝑎) across prompt levels 𝑝 for a given artifact stage, and the workflow requirement propagation, which evaluates how fairness requirements transition downstream by measuring the step-wise coverage delta between consecutive workflow artifacts (ΔProp = 𝐹𝐶𝑆 (𝑝, 𝑎 downstream ) − 𝐹𝐶𝑆 (𝑝, 𝑎 upstream )). This neutral transition metric captures requirement decay (ΔProp < 0), preservation (ΔProp = 0), or emergent requirement expansion (ΔProp > 0). Finally, to examine retention at the level of individual elements, we computed the survival rate of each FSE𝑖 as the share of runs whose spec.md contains FSE𝑖 in which FSE𝑖 is also present in code.py. A survival rate of 100% thus indicates that the element is never lost between the specification and the final code. 4.6

RQ2 Analysis Protocol: Outcome Governance

To evaluate how initial fairness expressiveness translates into the behavioral quality and equity of the final machine learning models (RQ2 ), we developed an automated, isolated evaluation harness. During the final stage of the SpecKit refinement chain, the target coding agent executed the task pipeline in code.py, training the classification model on the 80% development partition and persisting the resulting model artifact to disk (model.joblib), as requested in every prompt. Once execution was complete, our evaluation harness loaded the persisted model in a separate runtime environment and executed inference on the 20% hold-out dataset partition. Crucially, because the hold-out set remained strictly isolated throughout agentic development, this evaluation protocol eliminated potential data contamination, ensuring that measured performance directly reflects out-of-sample generalization. In 16 of the 48 runs (14 by OpenAI Codex and 2 by Antigravity CLI), the agent implemented the training pipeline but did not execute it before ending the session, e.g., because it only ran the test suite. In these cases, we executed the agent-generated training script without modifications on the development partition to obtain the model artifact. We recorded a run as an operational failure when code execution failed, no valid model artifact could be obtained, or the model was not trained on the provided development partition. One run met this condition: under the operationalized prompt, Antigravity CLI trained its model for Bank Marketing on a synthetic dataset of 1,000 rows that it generated itself, rather than on the provided data. Following the methodology by Parziale et al. [41], the evaluation harness computed a comprehensive set of predictive classification metrics and group fairness metrics across the protected sensitive attributes defined for each application domain. Predictive performance was evaluated via Accuracy, Precision, Recall, and F1-score. Group fairness was quantified using three established group equity metrics: Average Absolute Odds Difference (AAOD), which measures disparity in equalized odds; False Discovery Rate Difference (FDRD), which measures predictive disparity; and Disparate Impact (DI), which evaluates demographic parity ratios across protected subgroups. To analyze metric variation and trade-offs across prompt expressiveness levels, we established a non-parametric statistical evaluation pipeline, extending the analysis protocol adopted in prior work [41] to our multi-condition setting. Since each (agent, dataset) pair was evaluated once under each of the four prompt expressiveness levels, our unit of analysis was a matched block defined by the tuple (agent, dataset), observed across all four conditions. Since a block could be compared across conditions only if all four runs were available, we excluded the block containing the operational failure, which left 𝑛 = 11 complete blocks. We first applied the Shapiro–Wilk test (𝛼 = 0.05) to assess whether performance and fairness distributions deviate from normality. Given that metric distributions across non-deterministic LLM executions are routinely non-normal, and given the limited number of blocks, we employed non-parametric hypothesis testing throughout. Specifically, we conducted the Friedman test for global multi-condition comparisons across the four prompt expressiveness levels, followed by three planned pairwise comparisons, which isolate the effect of

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making fairness explicit (minimal vs. explicit) and of operationalizing it (minimal vs. operationalized and implicit vs. operationalized). We performed these comparisons with the Wilcoxon signed-rank test and applied the Holm–Bonferroni correction to the three comparisons of each metric to control the family-wise error rate (𝛼 = 0.05). Finally, we quantified the magnitude of performance and equity shifts using Cliff’s Delta (𝛿), a non-parametric effect size measure. We interpreted effect size thresholds following Romano et al. [49] (i.e., |𝛿 | < 0.147 as negligible, 0.147 ≤ |𝛿 | < 0.33 as small, 0.33 ≤ |𝛿 | < 0.474 as medium, and |𝛿 | ≥ 0.474 as large). 4.7 RQ3 Analysis Protocol: Design Decisions and Rationales We addressed the two parts of RQ3 through two complementary analyses of the 48 workspaces, i.e., the directories in which each run produced its artifacts. The static analysis automatically inspected the source code of every workspace to extract the fairness-relevant properties of the pipeline. The manual coding characterized how the executed pipeline handled fairness, including what hand-written fairness functions computed, and which rationales agents documented in the design artifacts. Part (i) of RQ3 , which concerns how agents implement fairness requirements, was answered by combining the two analyses on the executable pipeline. Part (ii), which concerns why agents make their decisions, was answered by the manual coding of the design documents produced by SpecKit, in which agents recorded the alternatives they considered and the reasons for their choices. The two analyses also validated each other: the manual coding confirmed the candidates flagged by the static analysis and served as the oracle against which we assessed the static analysis’s accuracy. Static analysis. The analyzer was built on Python’s ast module and answered four questions for each workspace: (1) whether a fairness toolkit was used, (2) which classifier was trained and whether the training procedure included any intervention, (3) whether each protected attribute reached the trained classifier, and (4) which functions might implement custom fairness logic. The analyzer parsed every Python file and notebook code cell of a workspace, excluding virtual environments and cache directories, and separated pipeline code from unit tests according to path conventions (e.g., tests/, test_*.py); all files were parsed without errors. Since the same library can be referenced under different local names, the analyzer first mapped every local name to its fully qualified name through the import statements of each file, including imports between modules of the same workspace: for instance, LR(...) after from sklearn.linear_model import LogisticRegression as LR resolves to sklearn.linear_model.LogisticRegression. To detect fairness toolkits, we adopted the catalog of Cannavale et al. [10], which comprises the Python toolkits exposing a fairness-specific programmatic API: AIF360, Fairlearn, Aequitas, Themis-ML, FairML, Audit-AI, and TensorFlow Model Remediation. The analyzer matched the top-level module of every Import and ImportFrom node against this catalog. We also included the fairness-specific components of scikit-lego (e.g., DemographicParityClassifier), which the catalog excludes because package-level imports of scikit-lego cannot be distinguished from generalpurpose usage. To capture toolkits that are installed but not imported, the analyzer also scanned dependency declarations and execution logs for the same package names and for installation commands. To identify the trained classifier (2), the analyzer considered a Call node as a classifier when its resolved name belonged to scikit-learn’s classifier registry, extended with the XGBoost, LightGBM, and CatBoost classifiers. Since a workspace may instantiate classifiers that it never trains, the analyzer resolved the receiver of each .fit() call through assignments, function parameters, return values, and wrappers such as Pipeline and GridSearchCV, and read the hyperparameters from the keyword arguments of the resolved node. It also recorded training-time intervention signals, namely class and sample weights, resampling, and non-default decision thresholds.

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To determine whether each protected attribute reached the trained classifier, the analyzer started from the matrix passed to .fit() and followed its definition backward across functions and modules, interpreting the operations that add or remove columns, such as column lists and named constants, set differences, comprehensions with not in filters, drop(columns=...), and the column lists of ColumnTransformer objects. An attribute was labeled excluded or retained only when the code provided explicit evidence of either outcome, and undetermined otherwise; in our workspaces, no attribute remained undetermined. Finally, since the semantics of a hand-written function cannot be established from its syntax tree alone, for custom fairness logic the analyzer only flagged candidates, i.e., functions whose name matched fairness-related terms (e.g., parity, aaod) or that referenced a protected attribute while computing confusion-matrix terms; whether a candidate actually implemented a fairness computation was decided during the manual coding. Every finding was stored with its file, line, and source segment, so that it could be traced back during the manual coding. On protected attributes, the analyzer agreed with the manual coding on all 108 workspace–attribute pairs, i.e., the protected attributes of each dataset across its 12 workspaces. Manual coding. Two authors manually coded all 48 workspaces using the codebook summarized in Table 2, which organizes the codes into four dimensions. Dimensions A–C describe what the pipeline does: how protected attributes enter the feature space (A), at which point of the pipeline fairness is handled (B), and which custom fairness computations and safeguards are implemented (C). Together with the static analysis, they answer part (i) of RQ3 . Dimension D captures the rationales that agents document, and answers part (ii). The two groups of dimensions rely on different evidence. Codes in dimensions A–C were assigned only from code that was executed when the pipeline ran: a comment or docstring stating an intention, a unit test, or a safeguard that was defined but never called was not sufficient to assign a code. Codes in dimension D were assigned only from the design documents, namely spec.md, plan.md, research.md, data-model.md, quickstart.md, and the interface contracts. We excluded tasks.md and the generated checklists, since they list development activities rather than justify design choices. The codebook emerged inductively. The authors first described a subset of workspaces with open labels, then grouped labels with the same meaning into codes, organized the codes into the four dimensions, and defined each code through inclusion and exclusion criteria. When a later workspace presented a case that fit no code, the codebook was refined, and the refinement was reapplied to the workspaces already coded. The coding followed a walkthrough approach, in which the two authors coded each workspace jointly. For each workspace, they first examined the pipeline code, from its entry point through the modules it calls, and then the design documents, discussing the evidence step by step, proposing labels, and comparing interpretations until they reached consensus. Each label had to be anchored to at least one verbatim quote: a script resolved every quote to its file and line and rejected labels whose quote could not be located, yielding 197 evidence items, i.e., label–quote pairs. The authors followed a precision-over-recall rule: when a case did not clearly fit a code, they assigned the code only if it met the codebook definition, leaving borderline cases uncoded rather than assigning a code that the evidence did not fully support. For instance, R-6 requires the prediction to be framed as support for a named human role, such as a clinician or an underwriter; hence, a specification describing the model only as “decision support” was not coded as R-6. To illustrate the process, consider the workspace in which Claude Code addressed COMPAS under the operationalized prompt. In the code, the authors coded as CE-1 the function computing AAOD from the per-group true- and false-positive rates, and as CE-3 the constant SMALL_SAMPLE_MIN_COUNT = 30, which the evaluation routine used to flag groups too small

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Table 2. Codebook used to code the 48 workspaces for RQ3 . Dimensions A–C are coded from executable pipeline code; dimension D from the SpecKit design documents.

Dimension

Code

Name

Definition

A – Feature space

TS-1

Full exclusion

TS-2

Partial exclusion

TS-3

Full retention

Every protected attribute is excluded from the matrix passed to the trained estimator. At least one protected attribute is excluded and at least one reaches the trained estimator. Every protected attribute reaches the trained estimator, raw or encoded.

TC-1

Pre-training handling

TC-2

Post-training group metrics

TC-3

Threshold verdict

CE-1

Hand-written disparity

CE-2

Zero-denominator guard

CE-3

Minimum group size

CE-4

Non-evaluable state

R-1

Fairness out of scope

R-2

Bias or proxy caveat

R-3

Toolkit choice

R-4

Model simplicity

R-5

Fairness trade-off

R-6

Human in the loop

B – Lifecycle timing

C – Custom engineering

D – Rationales

Before training, the code treats protected attributes specifically (validation, normalization, separation into an audit-only frame). Group-level rates or disparities are computed on held-out predictions in the workspace. A group metric is compared with a numeric threshold, producing PASS/FAIL or a warning; qualified as blocking, report-only, or advisory. Hand-written difference or ratio between groups, or between a group and the overall population (e.g., AAOD, FDRD, DI). Hand-written guard against a zero denominator or an empty group in a fairness computation. Explicit minimum group size, called by the pipeline, that changes how a group is treated. A metric or verdict can take an explicit “not evaluable” value (e.g., undefined, unavailable). Fairness mitigation or evaluation is stated to be out of scope, not requested, or deferred. Caveat on bias in the data or on proxy discrimination, or a statement that excluding attributes or computing metrics does not guarantee fairness. Fairness toolkits are considered or rejected, or hand-implementing fairness metrics is justified. The trained estimator is justified by interpretability or transparency, or alternatives are rejected for adding complexity. Explicit trade-off between fairness and accuracy, or conflict between fairness criteria. The prediction is framed as supporting a named human role, or sole automated use is forbidden.

for a reliable estimate. In research.md, the agent listed among the rejected alternatives “Using fairlearn library — rejected to keep the dependency footprint minimal”, which the authors coded as R-3, and described a rejected model as “higher variance, less interpretable”, coded as R-4 because it justifies the choice of the trained estimator. 5

Analysis of the Results

We organize our findings according to the research questions. We first address RQ1 on process-level governance, tracing how fairness requirements propagate through agent-generated artifacts. We then examine RQ2 on outcome-level governance, assessing whether these requirements affect the predictive performance and fairness of the trained models. Finally, we address RQ3 , examining how agents implement fairness requirements and how they justify their design decisions. 5.1

RQ1 – Process-Level Governance

The agents capture fairness requirements in the specification to an extent that closely follows the information provided in the request, but they do not fully preserve them down to the code (Table 3).

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Table 3. Mean Fairness Coverage Score (𝐹𝐶𝑆% ± std) per artifact stage and net transition ΔTotal = 𝐹𝐶𝑆 code − 𝐹𝐶𝑆 spec , by coding agent (4 runs per row) and overall (12 runs per row). Darker cells indicate higher coverage (steps of 20 percentage points).

Prompt level

spec.md

FCS (%) per artifact stage Planning tasks.md

code.py

Claude Code Minimal 13.89 ± 5.56 11.11 ± 0.00 13.89 ± 5.56 0.00 ± 0.00 Implicit 30.55 ± 5.56 16.66 ± 19.24 11.11 ± 0.00 25.00 ± 10.64 52.78 ± 5.56 50.00 ± 6.42 36.11 ± 10.64 47.22 ± 5.56 Explicit Operationalized 100.00 ± 0.00 88.89 ± 9.07 88.89 ± 15.71 97.22 ± 5.55

𝚫Total −13.89 ± 5.56 −5.56 ± 6.41 −5.56 ± 6.42 −2.78 ± 5.56

Antigravity CLI Minimal 8.33 ± 10.64 16.66 ± 6.41 0.00 ± 0.00 5.56 ± 6.41 −2.78 ± 10.64 Implicit 16.66 ± 11.11 16.66 ± 11.11 0.00 ± 0.00 11.11 ± 9.07 −5.56 ± 14.34 Explicit 80.56 ± 5.55 69.45 ± 13.98 63.89 ± 13.98 61.11 ± 14.35 −19.44 ± 16.67 Operationalized 97.22 ± 5.56 83.34 ± 11.11 69.45 ± 5.55 69.45 ± 5.55 −27.78 ± 11.11 OpenAI Codex Minimal 5.56 ± 6.41 5.56 ± 6.41 Implicit 13.89 ± 5.56 11.11 ± 9.07 Explicit 77.78 ± 12.83 75.00 ± 13.98 Operationalized 94.44 ± 11.11 86.11 ± 5.56

0.00 ± 0.00 11.11 ± 9.07 77.78 ± 9.07 63.89 ± 5.55

0.00 ± 0.00 5.56 ± 6.41 69.45 ± 10.64 80.56 ± 10.64

−5.56 ± 6.41 −8.33 ± 10.64 −8.33 ± 5.56 −13.89 ± 5.56

Overall Minimal 9.26 ± 7.97 11.11 ± 6.70 4.63 ± 7.43 1.85 ± 4.32 −7.41 ± 8.65 Implicit 20.37 ± 10.41 14.81 ± 12.83 7.41 ± 7.24 13.89 ± 11.72 −6.48 ± 10.00 Explicit 70.37 ± 15.23 64.82 ± 15.59 59.26 ± 20.83 59.26 ± 13.68 −11.11 ± 11.61 Operationalized 97.22 ± 6.91 86.11 ± 8.37 74.08 ± 14.47 82.41 ± 13.78 −14.81 ± 12.83

For capturing, specification coverage grows from 9.26% under the minimal prompt to 97.22% under the operationalized prompt. However, in terms of preservation, part of this coverage is lost by the time agents reach code.py. More specifically, the level of fairness operationalization determines how much of this information reaches the code. With minimal prompts, coding agents treated fairness as entirely out of scope, with final implementation coverage averaging just 1.85 ± 4.32% as the agents defaulted to standard pipelines without any fairness checks. Providing domain context in implicit prompts barely changed implementation outcomes (13.89 ± 11.72% in code.py). Although agents consistently documented the affected population in spec.md (driving initial specification coverage to 20.37%), they treated this context purely as descriptive text rather than an actionable requirement, omitting any dedicated fairness logic from the final codebase. Coverage jumped once prompts became explicit, reaching 59.26 ± 13.68% in the final implementation. In this setting, the agents frequently went beyond the four requested elements (FSE1 , FSE8 , FSE2 , FSE3 ): both OpenAI Codex and Antigravity CLI proactively introduced quantitative parity metrics and subgroup evaluations into intermediate specifications, pushing initial spec.md coverage to 70.37%. Finally, the operationalized prompt made this behavior systematic; indeed, specification coverage reached 97.22 ± 6.91% across all required elements, and coverage in the final executable scripts remained at 82.41 ± 13.78%.

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Table 4. Occurrences of each Fairness Specification Element (FSE) per artifact stage across the 48 runs, and survival rate, i.e., the share of runs whose spec.md contains the FSE in which it is also present in code.py. Introduced is the prompt level at which the FSE is first requested. Darker cells indicate higher survival. Occurrences Survival Introduced spec plan tasks code Runs Rate

ID

Element

FSE1 FSE2 FSE3 FSE4 FSE5 FSE6 FSE7 FSE8 FSE9

Decision Subjects Implicit Protected Attributes Explicit Domain Constraints & Exceptions Explicit Fairness Intent Operational Acceptance Thresholds Operational Fairness Metrics Operational Disaggregation Analysis Operational Decision Scope Implicit Protected Attribute Assessment Operational

44 28 20 15 14 18 20 30 24

38 29 21 3 13 18 19 26 24

24 26 9 4 9 18 20 23 24

23 28 12 5 14 18 19 27 24

23/44 52.3% 26/28 92.9% 12/20 60.0% 5/15 33.3% 14/14 100.0% 18/18 100.0% 19/20 95.0% 26/30 86.7% 23/24 95.8%

To better understand these results, we first examined where in the workflow fairness information is lost. Under the operationalized prompt, coverage declines during both technical planning (Δspec→plan = −11.11 ± 10.59%) and task decomposition (Δplan→task = −12.04 ± 13.78%), reaching its minimum in tasks.md, and then partially recovers during implementation (Δtask→code = +8.33 ± 13.50%). When converting architectural designs into operational to-do lists, agents focus on functional steps, such as data preprocessing and model training, and often leave out fairness constraints and quantitative thresholds. However, this omission does not prevent agents from implementing fairness in the final code; indeed, when writing code.py, the agents draw on the original specifications and architectural plans to reinstate the required evaluation metrics and acceptance thresholds and recover much of this omitted information. We then examined which fairness elements are lost (Table 4), observing a clear split between quantitative instructions and descriptive context. Elements that map directly onto code logic are almost always preserved: Acceptance Thresholds (FSE5 ) and Fairness Metrics (FSE6 ) survive in every run in which they are specified, followed by Protected Attribute Assessment (FSE9 , 95.8%), Disaggregation Analysis (FSE7 , 95.0%), Protected Attributes (FSE2 , 92.9%), and Decision Scope (FSE8 , 86.7%). In contrast, narrative elements frequently disappear: Fairness Intent (FSE4 ) survives in only 5 of the 15 runs in which it is specified (33.3%), followed by Decision Subjects (FSE1 , 52.3%) and Domain Constraints & Exceptions (FSE3 , 60.0%). While agents write code to compute the requested metrics, they rarely state which notion of fairness these metrics are meant to satisfy. The incomplete preservation therefore largely concerns the loss of why, as agents retain how fairness is measured but not the intent and constraints that motivate these measurements. We also evaluated the differences observed among the considered agents, which help explain both the variability of the results and the role of operationalization. Under the operationalized prompt, Claude Code tracked the specification most closely, preserving 97.22 ± 5.55% of required elements in the final code, and was the only agent retaining Fairness Intent (FSE4 ) in all four runs. Under the explicit prompt, however, it proved conservative (47.22% in code.py), implementing only the requested elements without introducing unprompted evaluation metrics. In contrast, OpenAI Codex and Antigravity CLI showed the opposite pattern. They took greater initiative under the explicit prompt (69.45% and 61.11%, respectively), independently adding metrics and grouplevel checks, but retained less under full operationalization (80.56% and 69.45%). Antigravity

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CLI, in particular, focused almost exclusively on runnable calculation routines, omitting Domain Constraints & Exceptions (FSE3 ) and Fairness Intent (FSE4 ) across all datasets. This difference is also visible in the net transition: under the operationalized prompt, Claude Code loses only 2.78% of coverage between spec.md and code.py, compared with 13.89% for OpenAI Codex and 27.78% for Antigravity CLI (Table 3). Finally, we examined whether the results differ across application domains. We observed no relevant differences: averaged across all prompt levels, coverage in code.py ranges from 37.96% (Adult Income, COMPAS, and Bank Marketing) to 43.52% (Diabetes). The same holds under the operationalized prompt, where specification coverage ranges from 92.59% to 100% and code coverage from 77.78% to 88.89% across the four datasets. Thus, the effect of fairness operationalization and the losses along the workflow depend not on the application domain, but on the information provided in the request and on the agent. The per-dataset results are available in our online replication package [9]. Answer to RQ1 Coding agents capture fairness requirements in the specification almost entirely when these are explicitly provided (97.22% under operationalized prompts), but only partially preserve them down to the code (82.41%). The level of fairness operationalization strongly affects this outcome. Providing domain context alone has little effect, while explicitly requesting fairness substantially increases coverage, which becomes nearly complete once fairness is operationalized. Losses concentrate in the intermediate artifacts and affect narrative elements, such as fairness intent, rather than quantitative ones, such as metrics and thresholds. Agents also differ in how they respond to operationalization, with Claude Code benefiting most from it (97.22% code coverage, against 80.56% for OpenAI Codex and 69.45% for Antigravity CLI). Results are consistent across the four application domains. 5.2

RQ2 – Outcome-Level Governance

Overall, the operationalization of fairness within software specifications does not substantially affect the predictive performance or group fairness of the resulting models. Across the 11 complete matched blocks, introducing fairness requirements did not significantly affect model predictive performance (Table 5). Mean accuracy remained stable across conditions: 0.857±0.085 under minimal prompts, 0.824±0.110 under implicit prompts, 0.837±0.085 under explicit prompts, and 0.838±0.108 under operationalized prompts (𝑄 = 2.000, 𝑝 = 0.572). Similarly, F1-scores showed no significant variation (𝑄 = 2.693, 𝑝 = 0.441), averaging between 0.283 (operationalized) and 0.349 (implicit). Precision (𝑄 = 4.261, 𝑝 = 0.235) and recall (𝑄 = 1.964, 𝑝 = 0.580) likewise showed no significant changes, and post-hoc Wilcoxon tests confirmed that no pairwise difference was statistically significant after correction (𝑝 adj ≥ 0.234 across all planned comparisons). These results indicate that specification-driven development does not negatively affect the predictive performance of the resulting models. This is an important result because it shows that enriching the specification with fairness requirements does not compromise the effectiveness of the models delivered by the agents. Group fairness metrics followed the same pattern. Across the nine fairness measures we could test, covering race, sex, and age, eight showed no statistically significant differences across prompt levels in the Friedman tests (𝑝 ≥ 0.24). For instance, Average Absolute Odds Difference (AAOD) remained unchanged for race (𝑄 = 4.171, 𝑝 = 0.244), sex (𝑄 = 1.457, 𝑝 = 0.692), and age (𝑄 = 3.102, 𝑝 = 0.376). The only metric showing an omnibus difference was Disparate Impact for race (𝑄 = 9.878, 𝑝 = 0.020), but post-hoc comparisons with Holm-Bonferroni correction revealed no significant difference

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Table 5. Predictive performance and group fairness of the trained models across prompt levels (mean ± std over the complete matched (agent, dataset) blocks), with Friedman test results. One block is excluded because of an operational failure (Section 4.6). Each fairness metric is computed only on the datasets that include the corresponding protected attribute, so the number of blocks 𝑛 varies by attribute. Medians and interquartile ranges are reported in the replication package. ∗ 𝑝 < 0.05.

Operationalized

Friedman 𝑄 𝑝

0.837 ± 0.085 0.369 ± 0.362 0.316 ± 0.340 0.292 ± 0.342

0.838 ± 0.108 0.393 ± 0.349 0.308 ± 0.350 0.283 ± 0.356

2.000 4.261 1.964 2.693

Group fairness Race (𝑛 = 6) AAOD 0.011 ± 0.018 0.097 ± 0.132 0.072 ± 0.157 FDRD 0.077 ± 0.120 0.095 ± 0.141 0.198 ± 0.365 DI 0.884 ± 0.180 0.695 ± 0.246 0.673 ± 0.406

0.066 ± 0.151 0.096 ± 0.130 0.954 ± 0.289

4.171 0.244 1.098 0.778 9.878 0.020∗

Sex (𝑛 = 9) AAOD FDRD DI

0.021 ± 0.038 0.059 ± 0.115 0.040 ± 0.074 0.010 ± 0.010 0.031 ± 0.057 0.036 ± 0.064 0.863 ± 0.322 0.725 ± 0.352 0.937 ± 0.533

0.059 ± 0.129 0.013 ± 0.015 0.978 ± 0.640

1.457 2.143 2.143

0.692 0.543 0.543

Age (𝑛 = 11) AAOD 0.081 ± 0.158 0.143 ± 0.280 0.145 ± 0.226 FDRD 0.068 ± 0.162 0.227 ± 0.410 0.277 ± 0.431 DI 2.163 ± 2.734 1.211 ± 1.006 1.251 ± 1.073

0.137 ± 0.293 0.273 ± 0.467 1.519 ± 2.091

3.102 2.200 0.673

0.376 0.532 0.879

Metric

Minimal

Prompt level Implicit Explicit

Predictive performance (𝑛 = 11) Accuracy 0.857 ± 0.085 0.824 ± 0.110 Precision 0.329 ± 0.361 0.398 ± 0.367 Recall 0.312 ± 0.361 0.384 ± 0.323 F1-score 0.302 ± 0.360 0.349 ± 0.335

0.572 0.235 0.580 0.441

between any pair of prompt levels (𝑝 adj ≥ 0.188; lowest unadjusted 𝑝 = 0.063, Cliff’s 𝛿 = 0.556, for implicit versus operationalized). Overall, none of the 27 planned pairwise comparisons on fairness metrics reached statistical significance. Hence, although agents capture fairness requirements in the specification and preserve most of them down to the code, the resulting models are not statistically different in terms of group fairness. In other words, the fairness information that agents preserve throughout the workflow does not translate into fairer models. This may reflect a limitation of specification-driven development, which we investigate in RQ3 . A closer look at the generated code helps explain this disconnect between process compliance and model outcomes. Under operationalized prompts, all 12 pipelines compute the requested fairness metrics and compare them with an acceptance threshold, but only after training: the underlying training routines continue to fit standard scikit-learn estimators without any fairness constraint (Section 5.3). As a result, the fairness information preserved in the code changes what is measured, not how the model is learned. This disconnect also extends to model delivery. Every prompt asks agents to save the trained model, and none states whether saving should depend on the fairness check: all 12 operationalized pipelines save the model regardless of the check’s outcome, and none withholds it or presents a failed check as a decision for the developer. This includes 3 models that never correctly predict

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Table 6. Workspaces coded with each RQ3 code, by prompt level and coding agent (AG = Antigravity CLI, CL = Claude Code, CX = OpenAI Codex; 4 workspaces per cell). Codes are grouped by dimension: A (TS), B (TC), C (CE), and D (R). Darker cells indicate more workspaces; codes in dimensions B–D are not mutually exclusive.

Code Name

Minimal Implicit Explicit Operational. AG CL CX AG CL CX AG CL CX AG CL CX Tot.

TS-1 Full exclusion · TS-2 Partial exclusion · TS-3 Full retention 4 TC-1 Pre-training handling · TC-2 Post-training group metrics · TC-3 Threshold verdict ·

· · 4 · · ·

· · 4 · · ·

· · 4 · · ·

· · 4 · · ·

· · 4 · · ·

· · 4 1 4 ·

1 · 3 1 · ·

4 · · 4 4 3

· 1 3 1 4 4

1 · 3 2 4 4

4 · · 4 4 4

10 1 37 13 20 15

CE-1 Hand-written disparity CE-2 Zero-denominator guard CE-3 Minimum group size CE-4 Non-evaluable state

· · · ·

· · · ·

· · · ·

· · · ·

· · · ·

· · · ·

2 4 · ·

· · · ·

3 4 4 4

4 4 · 1

4 4 2 4

4 4 · 4

17 20 6 13

R-1 R-2 R-3 R-4 R-5 R-6

· · · 1 · ·

· · · 1 · ·

1 · · 3 · ·

· · · 1 · 1

2 · · 2 · 1

2 1 · 3 · 2

1 3 2 1 2 1

4 1 · 1 · 3

· 4 · 3 · 4

· 2 1 · 2 ·

· 1 2 3 · 3

1 4 · 3 1 4

11 16 5 22 5 19

Fairness out of scope Bias or proxy caveat Toolkit choice Model simplicity Fairness trade-off Human in the loop

the positive class (F1 = 0.0), namely those produced by Antigravity CLI for Adult Income and by Claude Code and OpenAI Codex for COMPAS. For instance, in the workspace in which OpenAI Codex addressed Bank Marketing under the operationalized prompt, the code preserves both Fairness Metrics (FSE6 ) and Acceptance Thresholds (FSE5 ), thus contributing to the high coverage observed in RQ1 . It is also the only pipeline in our sample in which a failed fairness check terminates the run with an error; even so, it saves the trained model to disk regardless of the check’s outcome. Consequently, a more detailed specification adds verification routines, but neither the request nor the agents make the delivery of the model depend on them. Answer to RQ2 Specification-driven development does not compromise the predictive performance of the resulting models, which remains statistically unchanged across prompt levels. However, it does not improve their fairness either: only one of the nine tested fairness measures shows an omnibus difference (Disparate Impact for race), and no pairwise comparison remains significant after correction. Although agents preserve fairness requirements in the code, these act only as post-hoc checks that never constrain training or delivery. 5.3

RQ3 – Design Decisions and Rationales

The results of RQ1 and RQ2 point in opposite directions: under operationalized prompts, fairness metrics and acceptance thresholds reach the generated code whenever they are specified, yet the resulting models are not significantly fairer than those trained without any fairness guidance. RQ3 explains this gap by examining what the pipelines do and what the agents document. Table 6 reports the distribution of the codes across agents and prompt levels. Concerning part (i) of RQ3 , no workspace implements a mechanism that acts on fairness during training. Indeed, none of the 48 workspaces imports, declares, or installs a fairness toolkit,

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and no pipeline reweights or resamples the training data or applies group-specific decision thresholds. All agents train standard scikit-learn classifiers, mostly LogisticRegression (27) and RandomForestClassifier (18); the 9 workspaces that set class_weight balance the target classes, not the protected groups, and no workspace passes per-sample weights to fit(), which the same API provides to reweigh them. Hence, agents build the model in the same way regardless of the specification they receive, which directly explains the absence of differences observed in RQ2 . Yet, under the operationalized prompt, the protected attributes, the fairness metrics, and the acceptance thresholds reach the code (RQ1 ), and none of this information influences how the model is trained. Agents therefore seem to treat fairness requirements as additional deliverables to produce alongside the model, rather than as constraints on how the model should be built. The absence of fairness toolkits mirrors how these toolkits are used in open-source development, where Cannavale et al. [10] found that 93.4% of the toolkit calls in applied ML projects compute metrics, whereas only 6.6% invoke a mitigation algorithm: like open-source developers, agents measure fairness but do not act on it. We discuss possible explanations of this parallel in Section 6. Prompt levels change how much agents measure fairness, but not what happens with the result. Under minimal and implicit prompts, no pipeline computes group-level metrics; under explicit prompts, 8 pipelines do; under operationalized prompts, all 12 compute them and compare them with an acceptance threshold. However, this comparison has no consequence. Notably, the prompt asks agents to specify the thresholds themselves: they are not values imposed on the agents, but acceptance criteria that the agents define in their own specifications. Of the 15 pipelines that compare metrics with a threshold (all 12 operationalized and 3 explicit), 11 only print a PASS or FAIL verdict, and 3 only raise a warning. Only one pipeline, generated by OpenAI Codex for Bank Marketing, ends with an error when a check fails, and even this pipeline saves the trained model. Consequently, every pipeline saves its model, whether or not the model meets the fairness thresholds. Acceptance thresholds thus work as verdicts to report rather than as criteria for accepting the model. Combined with RQ1 , where Acceptance Thresholds (FSE5 ) survive in every run in which they are specified, this shows that the threshold is preserved but its role is not: an acceptance threshold defines when the model can be accepted, yet agents complete the task by delivering the model whatever the verdict. The only design choice through which fairness reaches the model is the exclusion of protected attributes from the training matrix. OpenAI Codex excludes all of them in its 8 explicit and operationalized workspaces, Claude Code in 2, and Antigravity CLI excludes one attribute in 1; in the remaining 37 workspaces, every protected attribute reaches the classifier. When agents handle protected attributes specifically before training (TC-1, 13 workspaces), they validate or normalize them, or separate them into an audit-only frame: they prepare the attributes for the audit or, at most, exclude them from training. Thus, when fairness does affect the model, it does so through the simplest available intervention, namely fairness through unawareness [56], which is known to be insufficient when other features act as proxies for the protected attributes. Moreover, this intervention depends on the agent rather than on the specification, as it is almost exclusive to OpenAI Codex, the agent that also took the most initiative under the explicit prompt in RQ1 : the same specification leads to different design decisions depending on the agent that executes it. Most of the engineering effort goes instead into computing the metrics robustly: agents implement them by hand (17 workspaces), guard against zero denominators and empty groups (20), and represent non-evaluable results explicitly (13). These implementations are not always correct: two workspaces of OpenAI Codex compute AAOD as a signed average, which lets disparities of opposite sign cancel out, and in the operationalized workspaces of Antigravity CLI undefined rates silently default to 0.0, which can turn a non-evaluable metric into an apparent absence of disparity. These results show that agents invest in making the audit robust rather than the model

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fair, and that even the audit is not always reliable: since no workspace relies on an established toolkit, a PASS verdict does not necessarily mean that the requirement is satisfied. This adds a nuance to the coverage reported in RQ1 , which captures whether a fairness metric is present in the code, but not whether it is computed correctly. In short, agents implement fairness requirements as a post-hoc audit, which becomes more complete as the prompt becomes more operationalized, while neither the training nor the release of the model depends on its result. Concerning part (ii), the absence of mitigation is often not justified. In 11 of the 24 explicit and operationalized workspaces, the design documents never state that mitigation is out of scope, never discuss fairness toolkits, and never mention a fairness trade-off: mitigation is not rejected, but simply never considered. When agents do justify their choices, the most frequent fairnessrelated rationale is that fairness is out of scope (R-1), found in 11 of the 48 workspaces. This rationale appears mostly under implicit and explicit prompts, including all four explicit workspaces of Claude Code, e.g., “No fairness-mitigation technique (e.g., reweighing, adversarial debiasing) is required in this script” (Claude Code, COMPAS, explicit). Under operationalized prompts, it appears once and suggests that agents read the request literally: “The feature requires evaluation, not hyperparameter optimization or mitigation” (OpenAI Codex, Adult Income). Less often, agents consider fairness toolkits and mostly reject them, e.g., to limit dependencies (R-3, 5 workspaces), as in “Using fairlearn library — rejected to keep the dependency footprint minimal” (Claude Code, COMPAS, operationalized), or acknowledge a tension between fairness and accuracy or between fairness criteria (R-5, 5 workspaces) without changing the pipeline accordingly. Finally, the rationale found most often, in 22 workspaces, does not concern fairness at all, as it justifies the choice of a simple classifier (R-4). These results show that, for fairness, agents derive the scope of their work from the literal content of the request: when mitigation is not mentioned, they either exclude it explicitly or do not consider it at all. This is consistent with what we reported in RQ1 , where Fairness Intent (FSE4 ) is the element most frequently lost along the workflow: without the purpose behind the requested metrics, agents have no reason to go beyond measuring them. The design documents also show that agents recognize the limits of their solutions. In 16 workspaces, they warn that the data may embed historical bias or that other features may act as proxies for the protected attributes (R-2). This caveat appears in 8 of the 10 workspaces that exclude all protected attributes, e.g., “Exclusion is never presented as proof that proxies cannot cause disparity” (OpenAI Codex, COMPAS, explicit). At the same time, 19 workspaces delegate fairness decisions to a human, framing the model as support for a clinician, an underwriter, or a reviewer, or forbidding its use as the sole basis for decisions (R-6), e.g., “must not be the sole basis for high-impact decisions” (OpenAI Codex, Adult Income, operationalized). These results indicate that the limited fairness of the resulting models observed in RQ2 does not stem from a lack of awareness: agents know that excluding protected attributes is insufficient, yet they do not complement it with any further measure. Rather than addressing the risks they identify in the pipeline, agents record them in the design documents and transfer their resolution to the humans who will use the model. Answer to RQ3 According to our findings, agents implement fairness requirements as a post-hoc audit that affects neither how the model is built nor whether it is delivered. The training procedure is the same at every prompt level, no workspace uses a fairness toolkit or an established biasmitigation technique, and the only intervention on the model is excluding protected attributes (11 workspaces, mostly by OpenAI Codex). Under operationalized prompts, all pipelines check group metrics against thresholds, but only one check affects the run outcome, every pipeline saves its model, and some hand-written metrics are incorrect. Agents act on fairness

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only to the extent that the request asks them to: in 11 of the 24 explicit and operationalized workspaces, mitigation is never discussed; when it is, agents mostly declare it out of scope. They acknowledge that their audit does not guarantee fairness (16 workspaces), but leave the problem to human reviewers (19). 6

Discussion, Lessons Learned, and Implications

Operationalizing fairness in semi-formal specifications makes agentic development more traceable, but not fairer. Our results confirm the central hypothesis of this study with respect to the development process: the more fairness is operationalized, the more fairness requirements are captured in the specification and preserved down to the code. However, this improved preservation does not extend to the outcome: the resulting models are no fairer than those developed without any fairness guidance, and they are delivered even when they violate the fairness requirements stated in their own specifications. The specification-driven workflow thus governs what agents write and measure, but not what they train and deliver. These findings provide multiple insights and implications, which we discuss in the following. Traceable, but Not Binding. Fairness must be stated explicitly to enter the workflow. Providing only the application context, as in the implicit prompt, leads agents to document the affected population, but results in a code coverage of just 13.89%; explicitly naming fairness in the request, as in the explicit prompt, raises coverage to 59.26%. However, bringing fairness into the workflow does not mean that agents satisfy it. The operationalized prompt does not merely ask agents to measure fairness: it asks them to state the fairness definition that should be satisfied and to specify an acceptance threshold for each fairness metric. Agents comply with this part of the request: they restate these elements in their specifications, and every operationalized pipeline compares the fairness metrics with a threshold. Yet, they train the same models they train when fairness is never mentioned, no workspace adopts a fairness toolkit or a mitigation technique, and every pipeline delivers its model whether or not it meets the thresholds. When an intervention reaches the model, agents select the simplest one, i.e., excluding protected attributes, while acknowledging in their own design documents that it does not prevent proxy discrimination. One may argue that agents simply did what they were asked: the prompt never requests mitigation, and every variant asks agents to save the trained model. This objection, however, applies differently to the two behaviors we observe. Concerning mitigation, no instruction forbids it, and the means were at hand. A fairness definition that should be satisfied and an acceptance threshold express requirements on the model, not content to document, and agents restate them as such in their own specifications. Moreover, in 9 workspaces agents set class_weight to balance the target classes, a reweighting that no prompt asks for; the same scikit-learn API accepts per-sample weights, which would allow reweighing the protected groups without any additional dependency, yet no workspace uses them. Agents therefore go beyond the letter of the request when the goal is predictive performance, but not when it is fairness: their literal reading is selective, treating predictive quality as an obligation and fairness as information to report. Concerning delivery, the request does ask agents to save the model, while also asking them to define acceptance thresholds. Saving a model that fails these thresholds is thus consistent with one instruction and at odds with the other, and agents never surface this conflict: beyond printing a verdict, nothing in the workflow presents a failed threshold as a decision for the developer. The issue is therefore not which choice agents make, but that the choice remains invisible to the developer, a point we return to in Lesson 3. This behavior closely mirrors how fairness toolkits are used in open-source development. Cannavale et al. [10] found that 93.4% of the toolkit calls in applied machine learning projects compute fairness metrics, whereas only 6.6% invoke a mitigation algorithm: fairness is measured, and then

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set aside. Like open-source developers, the agents in our study measure fairness but do not act on it. A plausible explanation for this parallel is that coding agents reproduce the practices most prevalent in the code they learned from: if fairness is predominantly measured but rarely mitigated in open-source machine learning projects, agents may have learned fairness as a measurement activity rather than as a property to achieve. Although our data do not allow us to verify this explanation, its implication is worth stressing: rather than improving on current practice, coding agents may replicate its shortcomings at scale, embedding a measure-without-acting pattern by default in every pipeline they generate. Fairness-by-design in agentic development cannot therefore rely on the default behavior of the agents. Finally, this behavior is unlikely to be specific to fairness. Agents are expected to satisfy the specifications they are given, and they do so for functional requirements. If they do not satisfy a fairness definition and acceptance thresholds that are explicitly stated, and that they themselves restate in their own specifications, there is little reason to expect that they will satisfy less concrete non-functional requirements, such as privacy, sustainability, or maintainability goals. Specificationdriven development reduces the ambiguity of what should be produced, but it does not ensure that agents treat every element of the specification as binding. These results call for intent engineering that goes beyond defining the content of a specification, as we did with the FSEs, to ensure that agents recognize which elements are requirements to satisfy, how they rank against other goals, and what must happen when they cannot be met. Such a perspective echoes recent calls to treat the loss of intent as a form of debt in AI-assisted development [53]. Lesson Learned 1: Fairness reaches the code only when it is explicitly requested, and even then it is traceable but not binding: agents state the fairness definition the model should satisfy and define acceptance thresholds, but deliver every model regardless of whether it meets them. A literal reading of the request does not explain the absence of mitigation, since agents go beyond the letter of the request when the goal is predictive performance; and when a model fails the thresholds, agents deliver it without presenting this as a decision for the developer. Like open-source developers, agents measure fairness without acting on it. Specification-driven development is therefore necessary but not sufficient: governing agents requires intent engineering, i.e., ensuring that agents recognize which elements of a specification are binding, how they rank, and what must happen when they are not met. Where Fairness Requirements Are Lost. Under operationalized prompts, the loss of fairness requirements concentrates in the intermediate artifacts. When agents translate the plan into a list of implementation tasks, they reason in terms of functional steps, and fairness constraints are among the first elements to disappear. Crucially, what is lost is primarily the why behind fairness: Fairness Intent (FSE4 ), i.e., the fairness definition the model should satisfy, is preserved in only 33.3% of the runs in which it is specified, followed by Decision Subjects (FSE1 ) and Domain Constraints & Exceptions (FSE3 ), whereas metrics and thresholds are consistently preserved. This pattern is consistent with the selective reading discussed above: the elements that agents preserve are those they can turn into code, while the element that states what the code should achieve is the first to be discarded. Once this goal is lost, the metrics and thresholds that remain no longer refer to a requirement to satisfy, and agents have no reason to go beyond computing them. The preservation of fairness requirements also varies across agents, but not across application domains. The same specification led Claude Code to preserve almost every requirement down to the code, and Antigravity CLI to lose more than a quarter of them, while the exclusion of protected attributes, the only intervention that reaches the model, is almost exclusive to OpenAI Codex.

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A specification-driven workflow can therefore improve the traceability of fairness requirements without neutralizing agent-specific differences in how those requirements are interpreted and carried forward. This makes the choice of the agent itself a governance decision. Lesson Learned 2: Fairness requirements are progressively lost as specifications are translated into task lists, and the fairness definition the model should satisfy is the first to disappear. The same specification is interpreted differently by different agents, independently of the application domain: the task decomposition is where fairness should be reviewed, and the choice of the coding agent is itself a governance decision. Who Governs Fairness Decisions. The rationales documented by the agents reveal that the problem is not a lack of awareness. In the explicit and operationalized workspaces, the design documents acknowledge potential bias in the data and the risk that some features act as proxies, including in 8 of the 10 workspaces that exclude all protected attributes. Agents thus recognize the limitations of their solutions and do not assume that they guarantee fairness. However, rather than acting on these limitations, agents delegate the fairness decisions to the developer, leaving the pipeline unchanged with respect to the one built in the absence of fairness requirements. For instance, OpenAI Codex states that the model “must not be the sole basis for highimpact decisions”, yet no subsequent step in the workflow takes up this recommendation. A circular delegation emerges: the developer delegates the development to the agent, including the satisfaction of the fairness requirements, while the agent returns the fairness decisions to the developer through research.md or spec.md, where they are listed among other design considerations. This delegation is never framed as such: beyond printing a PASS or FAIL verdict, nothing in the workflow presents a failed requirement as a decision that the developer must take before the model is used. Since no step of the workflow turns these delegated decisions into decisions that require resolution, they remain documented but unaddressed. Lesson Learned 3: Agents acknowledge that their solutions do not guarantee fairness, but do not act on these risks and silently hand them back to the developer. Without a point in the workflow at which these delegated decisions must be resolved, fairness risks remain documented but unaddressed. Implications for Researchers. Our results have multiple implications for the research community. First, they show that requirement satisfaction cannot be taken for granted when the implementer is an agent. The FSEs we derived from the requirements engineering literature describe what a complete fairness requirement should contain, and with a human developer, an acceptance threshold would normally be understood as a criterion that the delivered model must meet. With coding agents, this implicit understanding does not hold. Fairness requirements engineering should therefore investigate how to make the binding nature of requirements explicit, e.g., by complementing fairness specifications with elements that state the expected mitigation, whether a model that violates the thresholds can be delivered, and who is responsible when a requirement cannot be met. For mitigation, such elements are not missing information that would justify the observed behavior, since the means to act were available; for delivery, they would resolve a conflict that current requests leave open. Whether they are sufficient to change agent behavior remains to be assessed empirically. Second, our results warn against evaluating agentic workflows through process-level measures alone. Had we only measured traceability, the operationalized prompt would appear to be

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an almost complete success, with 97.22% of the fairness requirements captured in the specification and 82.41% preserved in the code. The outcome-level analysis shows that this success does not translate into fairer models, and the code analysis shows that even the preserved metrics are not always computed correctly. Third, the coding agent is an experimental variable that cannot be abstracted away, which calls for research on characterizing, and possibly predicting, how different agents interpret the same specification. Finally, the parallel with open-source practice suggests investigating to what extent coding agents inherit the fairness practices, and their shortcomings, of the code they learned from, and how this inheritance can be counteracted. ☼ Actionable Implication 1: Researchers should study how to make the binding nature of non-functional requirements explicit to coding agents, e.g., by extending fairness specifications with elements stating the expected mitigation, the enforcement of acceptance thresholds, and the ownership of unresolved decisions, and assess whether these elements change agent behavior. Evaluations of agentic workflows should jointly assess requirement preservation, implementation correctness, and requirement satisfaction, and treat the coding agent as a first-class experimental variable. In addition, our results also open a research direction on how engineering responsibilities are delegated between developers and agents. The vision of agentic software engineering assumes that developers delegate implementation while retaining agency over the decisions that matter [20]. Our findings show that, for fairness, this division of responsibilities does not hold in either direction. On the one hand, developers delegate to the agent the satisfaction of the fairness requirements, but the agent does not satisfy them. On the other hand, the agent delegates the fairness decisions back to the developer, in 19 workspaces explicitly invoking a human reviewer, a clinician, or an underwriter, but it does so silently, through caveats scattered across design documents rather than through an explicit request for a decision. As a result, developers formally retain the final say on fairness, but are not informed that a decision is required, nor of the evidence needed to take it: human agency is preserved on paper, but not in practice. This calls for research on delegation mechanisms for agentic development, i.e., explicit protocols that govern which engineering decisions an agent may take autonomously and which it must escalate. Several questions arise. First, how should delegation boundaries be specified, so that an agent knows, for instance, that delivering a model that violates an acceptance threshold is not a decision it is entitled to take? Second, how should agents escalate, i.e., at which stage of the workflow, with which evidence, and in which form, so that an escalation can be distinguished from the many design considerations that agents routinely document? Third, how can escalation be balanced against the cost of interrupting developers, considering that frequent escalations may lead developers to approve them without scrutiny, re-introducing the problem they are meant to solve? Finally, how should delegated decisions be recorded, so that it remains traceable who took each fairness decision and on which basis? Our study provides a starting point to address these questions, as it identifies concrete delegation points, i.e., failed fairness checks, bias and proxy caveats, and human-oversight recommendations, at which agents currently hand decisions back without escalating them. More broadly, these questions connect the governance of coding agents with research on normative multi-agent systems, which studies how norms and responsibilities can be shared between humans and autonomous agents [17].

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☼ Actionable Implication 2: The delegation between developers and agents is currently implicit in both directions: developers delegate the satisfaction of fairness requirements to agents, and agents silently delegate fairness decisions back to developers, preserving human agency only on paper. Future research may therefore want to investigate novel methods to design and evaluate delegation mechanisms that specify which decisions agents may take autonomously, how and when they must escalate, how escalation is balanced against developers’ attention, and how delegated decisions are recorded. Implications for Practitioners. Turning to practitioners, our results provide concrete recommendations on how to interact with coding agents when developing machine learning systems. First, practitioners should not assume that an agent will satisfy a fairness requirement because it is stated in the specification: until agents reliably treat non-functional requirements as binding, a request should also state what the agent must do when a requirement is not met, e.g., applying a given mitigation technique, stopping the delivery of the model, or escalating the decision. Second, the intermediate artifacts deserve as much attention as the code: since losses concentrate in the task decomposition, practitioners should review tasks.md against the fairness requirements of the specification before implementation begins. Third, a fairness report produced by an agent should not be taken at face value. Agents implement fairness metrics by hand rather than relying on established toolkits, and some of these implementations are incorrect, e.g., a signed AAOD that lets opposite disparities cancel out, or undefined rates silently replaced by zero; a PASS verdict is therefore not evidence that the requirement is satisfied, and a FAIL verdict is not evidence that anything was done about it. Finally, statements such as “must not be the sole basis for high-impact decisions” should be read for what they are: decisions that the agent hands back to the developer, which remain open until someone takes them. ☼ Actionable Implication 3: Practitioners should not assume that agents satisfy the fairness requirements they are given. They should verify whether the delivered model meets them, state explicitly what must happen when they are not met, review the task decomposition against the fairness requirements before implementation, validate agent-generated fairness metrics against established implementations, and treat the caveats and human-in-the-loop statements in the design documents as open decisions to resolve. Implications for the Design of Auditing Mechanisms. In our workspaces, auditing is something that agents produce: they write fairness checks into the pipeline, document caveats, and recommend human oversight. None of these elements is binding, because the workflow provides no point at which the audit result constrains what happens next, and the agent being audited is the one that decides whether the audit matters. Auditing mechanisms should therefore be placed in the workflow rather than delegated to the agent, and should verify requirement satisfaction rather than requirement presence. They should operate at four levels. At the artifact level, traceability should be checked between consecutive stages, so that a fairness requirement that disappears from the plan or from the task list is flagged before implementation; the LLM annotator constrained by a verbatim evidence rule and the static analyzer that we used as research instruments show that such checks can be automated reliably. At the implementation level, the fairness metrics computed by the pipeline should be validated against reference implementations, since the presence of a metric does not imply its correctness. At the release level, acceptance thresholds should act as gates that the workflow enforces, so that a model violating its own specification cannot be delivered

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without an explicit decision. Finally, at the decision level, the fairness decisions that agents delegate to developers, such as bias caveats, proxy warnings, and recommendations of human oversight, should be collected as open items that require explicit resolution and sign-off. ☼ Actionable Implication 4: Auditing mechanisms for agentic development should not be delegated to the agents being audited, and should verify that requirements are satisfied, not only that they are present. They should be embedded in the workflow and operate at four levels: traceability checks between artifacts, validation of fairness computations, release gates that enforce acceptance thresholds, and explicit resolution of the fairness decisions that agents hand back to developers. 7

Threats to Validity

We identify potential threats to our study’s validity and describe the mitigation strategies we used to address them. Construct Validity. The first threat concerns the Fairness Coverage Score, which treats each FSE as a binary indicator and assigns equal weight to all nine elements. Presence alone does not capture how well an element is specified, and equal weighting may not reflect the relative importance of each element in a given domain. Moreover, some elements, such as Decision Subjects (FSE1 ) and Fairness Intent (FSE4 ), describe context rather than behavior, and may be absent from source code. We mitigated this threat by deriving the FSEs through a structured process grounded in the literature, and by complementing the aggregate score with element-level survival rates. A second threat concerns the manual coding of RQ3 , which follows a precision-over-recall rule. Therefore, the reported frequencies should be interpreted as lower bounds. Internal Validity. The first threat concerns the annotations of RQ1 , which were produced by an LLM and may therefore misclassify the presence of FSEs. To mitigate this threat, we selected an annotator from a model family independent of the evaluated agents, enforced a verbatim evidence rule verified by a deterministic script, and validated the annotator against two human raters, obtaining almost perfect agreement with their consensus. A second threat concerns the manual coding of RQ3 , which was jointly conducted by two authors. To limit subjectivity, every code was anchored to at least one verbatim quote resolved to its file and line, yielding 197 evidence items. A third threat concerns the non-deterministic behavior of the evaluated agents, as we executed a single run for each combination of agent, dataset, and prompt level. Repeated executions might yield different artifacts and models. The matched-block design mitigates this threat by comparing prompt levels within the same agent and dataset. In one run, the agent trained its model on synthetic data it generated rather than the provided data; we recorded this run as an operational failure and excluded its block from the analysis of RQ2 , so every remaining block is observed under all four prompt levels. Finally, a fourth threat concerns how the prompts were formulated. All prompts ask agents to use scikit-learn and to save the trained model, unconditionally. The first constraint does not prevent mitigation, since scikit-learn estimators support per-sample weights and expose predicted probabilities for threshold adjustment; the second may have led agents to prioritize delivery over the outcome of the fairness check. Moreover, the operationalized prompt asks agents to state the fairness definition that should be satisfied and to define acceptance thresholds, but it does not prescribe mitigation or what should happen when a threshold is violated. Since most narrative elements are introduced with the verb state, agents may also have read them as content to document rather than as requirements to satisfy. We cannot exclude that prescribing mitigation and enforcement explicitly would change agent behavior; assessing this is part of our future work.

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Conclusion Validity. The statistical analysis of RQ2 relies on a small sample of 11 complete matched blocks, which drops to 9 and 6 blocks for the fairness metrics computed on sex and race, respectively, since these attributes are not available in all datasets. These sample sizes limit test power, so small or moderate effects may go undetected. To mitigate this threat, we adopted non-parametric tests suited to small paired samples and corrected for multiple comparisons using the Holm-Bonferroni procedure. Moreover, we complemented significance testing with effect sizes, which are negligible or small for most comparisons. External Validity. Our findings are specific to the experimental setting considered in this study. We evaluated three coding agents in specific versions, and agents configured with extended reasoning capabilities or released in future versions may behave differently, particularly given the rapid evolution of these tools. In addition, we limited our evaluation to four tabular datasets and binary classification tasks. Other application domains, data types, and learning tasks may yield different results. Finally, we instantiated specification-driven development using a single framework, SpecKit. Other frameworks that structure the development workflow differently may influence how fairness requirements propagate across software engineering artifacts. 8

Conclusion

This paper investigated whether specification-driven development can operationalize fairness-bydesign when autonomous coding agents develop machine learning systems. Our study shows that it governs the development process, but not its outcome. When fairness is operationalized, agents carry fairness requirements from the specification down to the code, but they turn them into a post-hoc audit that neither shapes training nor stops model delivery. The resulting models are therefore not significantly fairer than those developed without any fairness guidance, while agents acknowledge the limits of their solutions and leave the remaining decisions to human reviewers. These findings point to a broader lesson for the governance of autonomous agents: making a requirement traceable does not make it enforceable. Specifications can tell agents what to measure, but agents do not treat the fairness requirements they contain as binding, even when these define the conditions under which the model is acceptable. Our future research agenda builds on this lesson. We plan to extend fairness specifications with action-oriented elements, such as mitigation expectations and enforcement rules, and to assess whether they lead agents to produce fairer models. We also plan to design workflows in which agents explicitly escalate fairness decisions to developers, and to develop automated governance mechanisms that monitor whether developers’ requirements are preserved and enforced throughout autonomous software development. Finally, we plan to investigate whether these findings extend to other responsible AI principles and to other specification-driven frameworks. Data Availability To facilitate replicability and reproducibility, we made all the data, scripts, and additional analyses conducted in the study publicly available in our online appendix [9]. References [1] Anthropic. [n. d.]. Claude Code: Overview. https://code.claude.com/docs/en/overview. Accessed: 2026-07-23. [2] Sebastian Baltes, Florian Angermeir, Chetan Arora, Marvin Muñoz Barón, Chunyang Chen, Lukas Böhme, Fabio Calefato, Neil Ernst, Davide Falessi, Brian Fitzgerald, Davide Fucci, Junda He, Christoph Treude, Marcos Kalinowski, Stefano Lambiase, Daniel Russo, Mircea Lungu, Cristina Martinez Montes, Lutz Prechelt, Paul Ralph, Rijnard van Tonder, and Stefan Wagner. 2026. Guidelines for Empirical Studies in Software Engineering involving Large Language Models. Empirical Software Engineering (2026). arXiv:2508.15503 [cs.SE] https://arxiv.org/abs/2508.15503 [3] Luciano Baresi, Chiara Criscuolo, and Carlo Ghezzi. 2023. Understanding Fairness Requirements for ML-based Software. In 2023 IEEE 31st International Requirements Engineering Conference (RE). 341–346. doi:10.1109/RE57278.2023.00046

34

Cannavale et al.

[4] Matteo Bianchin and Ann Heylighen. 2017. Fair by design. Addressing the paradox of inclusive design approaches. The Design Journal 20, sup1 (2017), S3162–S3170. [5] Chaima Boufaied, Thanh Nguyen, and Ronnie de Souza Santos. 2025. Practitioner Insights on Fairness Requirements in the AI Development Life Cycle: An Interview Study. ACM Transactions on Software Engineering and Methodology (2025). [6] Giovanna Broccia, Lucio Lelii, Roberto Cirillo, Dario Di Nucci, Samuel Fricker, Fabio Palomba, Giorgio O Spagnolo, and Alessio Ferrari. 2026. Fairness as a First-Class Requirement: A Fairness Hazard Analysis Approach to Socio-Technical Processes. In International Working Conference on Requirements Engineering: Foundation for Software Quality. Springer, 229–244. [7] Yuriy Brun and Alexandra Meliou. 2018. Software fairness. In Proceedings of the 2018 26th ACM joint meeting on european software engineering conference and symposium on the foundations of software engineering. 754–759. [8] João Campista, Daniel Fazzioni, Vitor Mesaque Alves de Lima, Taciana Kudo, and Anderson da Silva Soares. 2026. Spec-Driven AI: A Multiagent Framework for Specification-Centric SDLC Automation. In International Conference on Software Maintenance and Evolution. IEEE, To appear. [9] Alfonso Cannavale, Alessandra Parziale, Valeria Pontillo, Patrizio Pelliccione, Andrea De Lucia, and Fabio Palomba. 2026. Replication Package — Specification-Driven Governance for Fairness-by-Design in Agentic Software Engineering: How Far Can We Go? https://figshare.com/s/fb38b6e7df54135ad025 [10] Alfonso Cannavale, Gianmario Voria, Antonio Scognamiglio, Giammaria Giordano, Gemma Catolino, and Fabio Palomba. 2025. Fairness set and forgotten: Mining fairness toolkit usage in open-source machine learning projects. Information and Software Technology (2025), 107957. [11] Joymallya Chakraborty, Suvodeep Majumder, and Tim Menzies. 2021. Bias in machine learning software: Why? how? what to do?. In Proceedings of the 29th ACM joint meeting on european software engineering conference and symposium on the foundations of software engineering. 429–440. [12] Joymallya Chakraborty, Suvodeep Majumder, Zhe Yu, and Tim Menzies. 2020. Fairway: a way to build fair ML software. In Proceedings of the 28th ACM joint meeting on European software engineering conference and symposium on the foundations of software engineering. 654–665. [13] Zhenpeng Chen, Jie M Zhang, Max Hort, Mark Harman, and Federica Sarro. 2024. Fairness testing: A comprehensive survey and analysis of trends. ACM Transactions on Software Engineering and Methodology 33, 5 (2024), 1–59. [14] Victoria Clarke and Virginia Braun. 2017. Thematic analysis. The journal of positive psychology 12, 3 (2017), 297–298. [15] John Clore, Krzysztof Cios, Jon DeShazo, and Beata Strack. 2014. Diabetes 130-US Hospitals for Years 1999-2008. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5230J. [16] Giordano d’Aloisio, Claudio Di Sipio, Antinisca Di Marco, and Davide Di Ruscio. 2024. How fair are we? From conceptualization to automated assessment of fairness definitions. arXiv preprint arXiv:2404.09919 (2024). [17] Hoa Khanh Dam, Geeta Mahala, Rashina Hoda, Xi Zheng, and Cristina Conati. 2025. Towards autonomous normative multi-agent systems for Human-AI software engineering teams. arXiv preprint arXiv:2512.02329 (2025). [18] Yongkang Du, Jen-tse Huang, Jieyu Zhao, and Lu Lin. 2025. Faircoder: Evaluating social bias of llms in code generation. arXiv preprint arXiv:2501.05396 (2025). [19] Alessandro Fabris, Stefano Messina, Gianmaria Silvello, and Gian Antonio Susto. 2022. Algorithmic fairness datasets: the story so far: A. Fabris et al. Data Mining and Knowledge Discovery 36, 6 (2022), 2074–2152. [20] Dana Feng, Bhada Yun, and April Yi Wang. 2026. From Junior to Senior: Allocating Agency and Navigating Professional Growth in Agentic AI-Mediated Software Engineering. In Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems. 1–24. [21] Carmine Ferrara, Francesco Casillo, Carmine Gravino, Andrea De Lucia, and Fabio Palomba. 2024. Refair: Toward a context-aware recommender for fairness requirements engineering. In Proceedings of the IEEE/ACM 46th International Conference on Software Engineering. 1–12. [22] Carmine Ferrara, Giulia Sellitto, Filomena Ferrucci, Fabio Palomba, and Andrea De Lucia. 2024. Fairness-aware machine learning engineering: how far are we? Empirical software engineering 29, 1 (2024), 9. [23] Elizangela de Freitas Ximenes, José Siqueira Cerqueira, Pekka Abrahamsson, and Edna Dias Canedo. 2026. ERC4AI: A BERT-Based Model for Ethical Requirements Classification in AI Systems. Journal of Software: Evolution and Process 38, 7 (2026), e70156. e70156 smr.70156. [24] GitHub. 2026. Spec Kit: Toolkit for Spec-Driven Development. https://github.com/github/spec-kit. Accessed: 2026-07-23. [25] GitLab Inc. 2026. GitLab Research Reveals Organizations Are Generating AI Code Faster Than They Can Control It. https://about.gitlab.com/press/releases/2026-06-23-gitlab-research-reveals-organizations-are-generating-ai-codefaster-than-they-can-control-it/. Press release. Accessed: 2026-07-10. [26] Google. [n. d.]. Antigravity CLI. https://antigravity.google/. Accessed: 2026-07-23.

Specification-Driven Governance for Fairness-by-Design in Agentic Software Engineering: How Far Can We Go?

35

[27] Yifeng He, Ethan Wang, Yuyang Rong, Zifei Cheng, and Hao Chen. 2025. Security of ai agents. In 2025 IEEE/ACM International Workshop on Responsible AI Engineering (RAIE). IEEE, 45–52. [28] Xinyi Hou, Yanjie Zhao, Yue Liu, Zhou Yang, Kailong Wang, Li Li, Xiapu Luo, David Lo, John Grundy, and Haoyu Wang. 2024. Large language models for software engineering: A systematic literature review. ACM Transactions on Software Engineering and Methodology 33, 8 (2024), 1–79. [29] Michael Howard and Steve Lipner. 2006. The security development lifecycle. Vol. 8. Microsoft Press Redmond. [30] Dong Huang, Jie M. Zhang, Qingwen Bu, Xiaofei Xie, Junjie Chen, and Heming Cui. 2025. Bias testing and mitigation in llm-based code generation. ACM Transactions on Software Engineering and Methodology 35, 1 (2025), 1–31. [31] Krishnaram Kenthapadi, Himabindu Lakkaraju, and Nazneen Rajani. 2023. Generative ai meets responsible ai: Practical challenges and opportunities. In Proceedings of the 29th ACM SIGKDD conference on knowledge discovery and data mining. 5805–5806. [32] Stefano Lambiase, Gemma Catolino, Fabio Palomba, Filomena Ferrucci, and Daniel Russo. 2025. Investigating the role of cultural values in adopting large language models for software engineering. ACM Transactions on Software Engineering and Methodology 35, 1 (2025), 1–43. [33] Hao Li, Haoxiang Zhang, and Ahmed E Hassan. 2025. The rise of ai teammates in software engineering (se) 3.0: How autonomous coding agents are reshaping software engineering. arXiv preprint arXiv:2507.15003 (2025). [34] Suvodeep Majumder, Joymallya Chakraborty, Gina R Bai, Kathryn T Stolee, and Tim Menzies. 2023. Fair enough: Searching for sufficient measures of fairness. ACM Transactions on Software Engineering and Methodology 32, 6 (2023), 1–22. [35] Sergio Morales, Robert Clarisó, and Jordi Cabot. 2024. A dsl for testing llms for fairness and bias. In Proceedings of the ACM/IEEE 27th International Conference on Model Driven Engineering Languages and Systems. 203–213. [36] Sergio Moro, Paulo Rita, and Paulo Cortez. 2014. Bank Marketing. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5K306. [37] Thanh Nguyen, Chaima Boufaied, and Ronnie de Souza Santos. 2026. A gray literature study on fairness requirements in AI-enabled software engineering. In Proceedings of the 2nd International Workshop on Requirements engineering for AI-powered SoftwarE. 1–9. [38] OpenAI. [n. d.]. Codex CLI. https://openai.com/index/introducing-codex/. Accessed: 2026-07-23. [39] Jonathan S Ostroff, David Makalsky, and Richard F Paige. 2004. Agile specification-driven development. In International Conference on Extreme Programming and Agile Processes in Software Engineering. Springer, 104–112. [40] Alessandra Parziale, Gianmario Voria, Giammaria Giordano, Gemma Catolino, Gregorio Robles, and Fabio Palomba. 2025. Contextual fairness-aware practices in ML: A cost-effective empirical evaluation. In 2025 IEEE International Conference on Software Analysis, Evolution and Reengineering-Companion (SANER-C). IEEE, 1–8. [41] Alessandra Parziale, Gianmario Voria, Giammaria Giordano, Gemma Catolino, Gregorio Robles, and Fabio Palomba. 2025. Fairness on a budget, across the board: A cost-effective evaluation of fairness-aware practices across contexts, tasks, and sensitive attributes. Information and Software Technology (2025), 107858. [42] Alessandra Parziale, Gianmario Voria, Valeria Pontillo, Gemma Catolino, Andrea De Lucia, and Fabio Palomba. 2025. Toward Systematic Counterfactual Fairness Evaluation of Large Language Models: The CAFFE Framework. arXiv preprint arXiv:2512.16816 (2025). [43] Alessandra Parziale, Gianmario Voria, Valeria Pontillo, Amleto Di Salle, Patrizio Pelliccione, Gemma Catolino, and Fabio Palomba. 2026. Once Upon a Team: Investigating Bias in LLM-Driven Software Team Composition and Task Allocation. arXiv preprint arXiv:2601.03857 (2026). [44] Sida Peng, Eirini Kalliamvakou, Peter Cihon, and Mert Demirer. 2023. The impact of ai on developer productivity: Evidence from github copilot. arXiv preprint arXiv:2302.06590 (2023). [45] Nuno Pombo and Aazaade Faraji. 2026. Toward Responsible Autonomy: The Opportunities and Obstacles of Large Language Models in Software Engineering. In Proceedings of the 2026 9th International Conference on Software Engineering and Information Management. 114–119. [46] ProPublica. 2016. COMPAS Analysis GitHub Repository. https://github.com/propublica/compas-analysis. Accessed: 2026-08-26. [47] Xue Qin and Maurício Gruppi. 2026. From Correctness to Consistency: Redefining Reliability for the Agentware Era. In Proceedings of the 3rd ACM International Conference on AI-Powered Software. 145–150. [48] Qusai Ramadan, Jukka Ruohonen, Abhishek Tiwari, Adam Alami, and Zeyd Boukhers. 2025. Towards Systematic Specification and Verification of Fairness Requirements: A Position Paper. In 2025 IEEE 33rd International Requirements Engineering Conference Workshops (REW). IEEE, 405–411. [49] Jeanine Romano, Jeffrey D Kromrey, Jesse Coraggio, and Jeff Skowronek. 2006. Appropriate statistics for ordinal level data: Should we really be using t-test and Cohen’sd for evaluating group differences on the NSSE and other surveys. In annual meeting of the Florida Association of Institutional Research, Vol. 177.

36

Cannavale et al.

[50] Abhik Roychoudhury, Corina Păsăreanu, Michael Pradel, and Baishakhi Ray. 2026. Agentic AI software engineers: Programming with trust. Commun. ACM 69, 5 (2026), 56–58. [51] Ronnie de Souza Santos, Felipe Fronchetti, Savio Freire, and Rodrigo Spinola. 2024. Software fairness debt. arXiv preprint arXiv:2405.02490 (2024). [52] Emeralda Sesari, Federica Sarro, and Ayushi Rastogi. 2026. It Is Giving Major Satisfaction: Why Fairness Matters for Software Practitioners. ACM Transactions on Software Engineering and Methodology 35, 5 (2026), 1–44. [53] Margaret-Anne Storey. 2026. From technical debt to cognitive and intent debt: Rethinking software health in the age of AI. arXiv preprint arXiv:2603.22106 (2026). [54] Viktoria Stray, Nils Brede Moe, Nivethika Ganeshan, and Simon Kobbenes. 2025. Generative ai and developer workflows: How github copilot and chatgpt influence solo and pair programming. In Proceedings of the 58th Hawaii International Conference on System Sciences. [55] US Department of Commerce, Bureau of the Census. 1995. Current Population Survey: Annual Demographic File, 1994. Technical Report. US Department of Commerce, Bureau of the Census. [56] Sahil Verma and Julia Rubin. 2018. Fairness definitions explained. In Proceedings of the international workshop on software fairness. 1–7. [57] Gianmario Voria, Stefano Lambiase, Maria Concetta Schiavone, Gemma Catolino, and Fabio Palomba. 2025. From expectation to habit: Why do software practitioners adopt fairness toolkits?. In 2025 IEEE/ACM 47th International Conference on Software Engineering: Software Engineering in Society (ICSE-SEIS). IEEE, 94–105. [58] Gianmario Voria, Moses Openja, Foutse Khomh, Gemma Catolino, and Fabio Palomba. 2026. Tracing Stereotypes in Pre-trained Transformers: From Biased Neurons to Fairer Models. arXiv preprint arXiv:2601.05663 (2026). [59] Gianmario Voria, Giulia Sellitto, Carmine Ferrara, Francesco Abate, Andrea De Lucia, Filomena Ferrucci, Gemma Catolino, and Fabio Palomba. 2025. Fairness-aware practices from developers’ perspective: A survey. Information and Software Technology 182 (2025), 107710. [60] Zhengren Wang, Chufan Wang, Yongan Yu, Sizhe Wang, Feiyu Xiong, Wentao Zhang, et al. 2026. Maintaincoder: Maintainable code generation under dynamic requirements. Advances in Neural Information Processing Systems 38 (2026), 15399–15436. [61] Zichong Wang, Zhou Yang, David Lo, and Wenbin Zhang. 2026. Towards fair machine learning software: Understanding and addressing model bias through counterfactual thinking. AI and Ethics 6, 2 (2026), 181. [62] Claes Wohlin. 2014. Guidelines for snowballing in systematic literature studies and a replication in software engineering. In Proceedings of the 18th international conference on evaluation and assessment in software engineering. 1–10. [63] Zhuolin Xu, Chenglin Li, Qiushi Li, and Shin Hwei Tan. 2026. What Makes Code Generation Ethically Sourced?. In 2026 IEEE/ACM International Conference on Software Engineering. To appear. [64] Zhe Yu, Joymallya Chakraborty, and Tim Menzies. 2024. Fairbalance: How to achieve equalized odds with data pre-processing. IEEE Transactions on Software Engineering 50, 9 (2024), 2294–2312. [65] Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric Xing, et al. 2023. Judging llm-as-a-judge with mt-bench and chatbot arena. Advances in neural information processing systems 36 (2023), 46595–46623. [66] Zibin Zheng, Kaiwen Ning, Qingyuan Zhong, Jiachi Chen, Wenqing Chen, Lianghong Guo, Weicheng Wang, and Yanlin Wang. 2025. Towards an understanding of large language models in software engineering tasks. Empirical Software Engineering 30, 2 (2025), 50.

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