Neurosymbolic AI as a Pathway to Ethical Reasoning: Integrating Human Values into Business Intelligence Systems

The rise of business intelligence (BI) systems powered by artificial intelligence has amplified concerns over ethical accountability in data-driven decision-making. While neural networks excel at pattern discovery, they lack the capacity to reason about moral rules or human values. Conversely, symbolic logic systems can represent and enforce ethical constraints but struggle with contextual learning. This article explores neurosymbolic AI, a paradigm that fuses neural learning with symbolic reasoning, as a promising pathway toward embedding ethical reasoning within BI tools. Through conceptual synthesis and secondary data analysis, it argues that neurosymbolic architectures offer the structural flexibility to encode ethical principles such as fairness, transparency, and accountability directly into BI workflows. The study develops an integrative framework termed Value-Sensitive Neurosymbolic Business Intelligence (VS-NBI), combining deontic logic, value-based argumentation, and neural contextualization to support ethical decision-making in complex organizational environments. Drawing from philosophical ethics, AI governance literature, and enterprise analytics case studies, the framework demonstrates how ethical constraints can function as symbolic guardrails within neural BI systems, enabling them to reason about duties, consequences, and stakeholder values rather than merely optimize performance metrics. By mapping ethical rules across the BI pipeline, from data ingestion to decision deployment, the article illustrates how neurosymbolic systems can enhance auditability, mitigate bias, and sustain moral consistency under changing business conditions. It concludes that integrating human values into machine reasoning represents a shift from intelligence to wisdom in data systems: a transition essential for trustworthy, socially aligned enterprise analytics. The proposed VS-NBI model thus provides both a theoretical and practical foundation for constructing ethically aware BI ecosystems capable of balancing commercial efficiency with moral responsibility.