Engineering Shared Answerability: Design Primitives for Answerable Control

This paper develops a practical framework for AI governance, algorithmic accountability, human oversight, corrigibility, automation, and answerable control. Its central claim is that high-performing automated systems are not yet answerable systems: a system can remain efficient, compliant, and visibly supervised while still becoming structurally unsafe when contradiction fails to become correction before harm is exported. The paper argues that the standard language of human in the loop is often weaker than it appears, because humans may remain present in the workflow after real steering has already migrated to models, metrics, scores, or automated pathways. In response, it proposes an engineering architecture for shared answerability, including a layered control stack and a compact set of design primitives: causal anchoring, adversarial testing, proof of contact receipt, binding revision triggers, bounded delegation, consequence-bearing authorization, burden-path audit, verifiable trace, and refusal structures. It also identifies activation thresholds for high-stakes systems and offers evaluation metrics for whether contradiction actually changes future system behavior. Using clinical decision support as a worked example, the paper contributes to philosophy of AI, philosophy of technology, cybernetics, and ethics of automation by showing how machine execution, human witness, and reality-based correction can be bound together more rigorously in consequential systems.