---
type: source
title: "Automated Interpretability-Driven Model Auditing and Control: A Research Agenda"
author: "Oxford Martin AI Governance Initiative (AIGI)"
url: https://aigi.ox.ac.uk/wp-content/uploads/2026/01/Automated_interp_Research_Agenda.pdf
date: 2026-01-15
domain: ai-alignment
secondary_domains: []
format: paper
status: processed
priority: high
tags: [interpretability, alignment-auditing, automated-auditing, model-control, Oxford, AIGI, research-agenda, tool-to-agent-gap, agent-mediated-correction]
---

## Content

Oxford Martin AI Governance Initiative (AIGI) research agenda proposing a system where domain experts can query a model's behavior, receive explanations grounded in their expertise, and instruct targeted corrections — all without needing to understand how AI systems work internally.

**Core pipeline:** Eight interrelated research questions forming a complete pipeline:
1. Translating expert queries into testable hypotheses about model internals
2. Localizing capabilities in specific model components
3. Generating human-readable explanations
4. Performing surgical edits with verified outcomes

**Two main functions:**
1. **Explanation for decision support**: Generate faithful, domain-grounded explanations that enable experts to evaluate model predictions and identify errors
2. **Agent-mediated correction**: When experts identify errors, an agent determines the optimal interpretability tool and abstraction level for intervention, applies permanent corrections with minimal side effects, and improves the model for future use

**Key distinction**: Rather than optimizing for plausible explanations or proxy task performance, the system is optimized for **actionability**: can domain experts use explanations to identify errors, and can automated tools successfully edit models to fix them?

The agenda explicitly attempts to address the tool-to-agent gap (though doesn't name it as such) by designing the interpretability pipeline around the expert's workflow rather than around the tool's technical capabilities.

LessWrong coverage: https://www.lesswrong.com/posts/wHBL4eSjdfv6aDyD6/automated-interpretability-driven-model-auditing-and-control

## Agent Notes
**Why this matters:** This is a direct counter-proposal to the problems documented in AuditBench. Oxford AIGI is proposing to solve the tool-to-agent gap by redesigning the pipeline around the human expert's need for actionability — not asking "can the tool find the behavior?" but "can the expert identify and fix errors using the tool's output?" This is a more tractable decomposition of the problem. However, it's a research agenda (January 2026), not an empirical result. It tells us the field recognizes the tool-to-agent problem; it doesn't show the problem is solved.

**What surprised me:** The framing around "domain experts" (not alignment researchers) as the primary users of interpretability tools. This shifts the governance model: rather than alignment researchers auditing models, the proposal is for doctors/lawyers/etc. to query models in their domain and receive actionable explanations. This is a practical governance architecture, not just a technical fix.

**What I expected but didn't find:** Empirical results. This is a research agenda, not a completed study. No AuditBench-style empirical validation of whether agent-mediated correction actually works. The gap between this agenda and AuditBench's empirical findings is significant.

**KB connections:**
- scalable oversight degrades rapidly as capability gaps grow — this agenda is an attempt to build scalable oversight through interpretability; the research agenda is the constructive proposal, AuditBench is the empirical reality check
- [[no research group is building alignment through collective intelligence infrastructure despite the field converging on problems that require it]] — Oxford AIGI is attempting to build the governance infrastructure; this partially addresses the "institutional gap" claim
- formal verification of AI-generated proofs provides scalable oversight — formal verification works for math; this agenda attempts to extend oversight to behavioral/value domains via interpretability

**Extraction hints:**
- CLAIM CANDIDATE: "Agent-mediated correction — where domain experts query model behavior, receive grounded explanations, and instruct targeted corrections through an interpretability pipeline — is a proposed approach to closing the tool-to-agent gap in alignment auditing, but lacks empirical validation as of early 2026"
- This is a "proposed solution" claim (confidence: speculative to experimental) — pairs with AuditBench as problem statement
- Note the actionability reframing: most interpretability research optimizes for technical accuracy; this agenda optimizes for expert usability

**Context:** Oxford Martin AI Governance Initiative — academic/policy research organization, not a lab. Published January 2026. Directly relevant to governance architecture debates. The research agenda format means these are open questions, not completed research.

## Curator Notes
PRIMARY CONNECTION: [[no research group is building alignment through collective intelligence infrastructure despite the field converging on problems that require it]]
WHY ARCHIVED: Partially challenges the "institutional gap" claim — Oxford AIGI is actively building the governance research agenda for interpretability-based auditing. But the claim was about implementation, not research agendas; the gap may still hold.
EXTRACTION HINT: Extract as a proposed solution to the tool-to-agent gap, explicitly marking as speculative/pre-empirical. Pair with AuditBench as the empirical problem statement. The actionability reframing (expert usability > technical accuracy) is the novel contribution.
