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| type | domain | description | confidence | source | created | attribution | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| claim | ai-alignment | Oxford AIGI's research agenda reframes interpretability around whether domain experts can identify and fix model errors using explanations, not whether tools can find behaviors | speculative | Oxford Martin AI Governance Initiative, January 2026 research agenda | 2026-03-30 |
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Agent-mediated correction proposes closing the tool-to-agent gap through domain-expert actionability rather than technical accuracy optimization
Oxford AIGI proposes a complete pipeline where domain experts (not alignment researchers) query model behavior, receive explanations grounded in their domain expertise, and instruct targeted corrections without understanding AI internals. The core innovation is optimizing for actionability: can experts use explanations to identify errors, and can automated tools successfully edit models to fix them? This directly addresses the tool-to-agent gap documented in AuditBench by redesigning the interpretability pipeline around the expert's workflow rather than the tool's technical capabilities. The agenda includes eight interrelated research questions covering translation of expert queries into testable hypotheses, capability localization, human-readable explanation generation, and surgical edits with verified outcomes. However, this is a research agenda published January 2026, not empirical validation. The gap between this proposal and AuditBench's empirical findings (that interpretability tools fail through workflow integration problems, not just technical limitations) remains significant. The proposal shifts the governance model from alignment researchers auditing models to domain experts (doctors, lawyers, etc.) querying models in their domains and receiving actionable explanations.
Relevant Notes:
- alignment-auditing-tools-fail-through-tool-to-agent-gap-not-just-technical-limitations
- no research group is building alignment through collective intelligence infrastructure despite the field converging on problems that require it
- formal verification of AI-generated proofs provides scalable oversight that human review cannot match because machine-checked correctness scales with AI capability while human verification degrades
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