Engineering Shared Answerability: Design Primitives for Answerable Control

This paper presents an engineering framework for shared answerability in AI systems, automated decision-making, clinical software, public administration, and institutional workflows. It argues that current systems are often designed for performance, compliance, and seamless execution rather than for correction under real-world pressure. As a result, contradiction is frequently converted into documentation instead of revision, and human oversight remains visible even after effective control has shifted to models, scores, and automated pathways. The paper proposes a practical architecture for answerable control, centered on a layered control stack and a set of design primitives for grounding, contestability, auditability, bounded delegation, human authorization, and binding revision. These include causal anchoring, adversarial mirroring, proof of contact receipt, revision triggers, refusal structures, and burden-path audit. The paper also introduces activation thresholds for when stronger protocols are required and proposes metrics for evaluating whether systems actually become more corrigible in use. A worked example in clinical decision support shows how human witness and machine recommendation can be redesigned so that error becomes correction before harm is exported. The paper speaks to AI governance, responsible AI, cybernetics, safety engineering, human-computer interaction, and technology policy.