The Capture of Answerability: AI Governance and Institutional Accountability

When an institution controls not only the decision affecting a person, but also the criteria, evidence, forum and remedies through which that decision can be challenged, accountability becomes circular. This paper calls that condition the capture of answerability. Building on Schedler, Bovens, Pettit and Mashaw, it argues that accountability mechanisms may be formally present while substantively hollow where the epistemic and procedural preconditions of challenge are themselves institutionally controlled. The paper introduces a two-tier taxonomy: structural captures of decision, criteria, evidence and forum constitute the core condition; amplifying captures of remedy, exit, narrative and liability indicate severity and durability. The self-sealing architecture is most acute where criteria capture and forum capture coincide: the affected person cannot challenge the evaluative standard without a competent forum, while the forum cannot test the challenge without access to the evaluative standard. The taxonomy is developed through a doctrinal synthesis of public law, data protection, contract and regulatory materials, showing both where existing law already supports answerability and where AI-mediated decision-making exposes structural gaps. The framework is developed through AI-mediated decision-making generally and UK university AI-assisted academic-integrity processes specifically. The paper anticipates and addresses ten predictable objections, distinguishing answerability from accountability, transparency, explainability, and existing doctrine. It proposes an Answerability Capture Audit, presented as a doctrinal synthesis and practical governance instrument, alongside four differentiated reform proposals directed at the ICO, the Office for Students, the OIA, and the Law Commission.