Who Does What in AI Auditing? Designing Human-AI Collaboration for Auditing Generative AI

2026-09-21

AI Summary

研究提出Human-Agent Audit Collaboration (HAAC)系统，旨在通过人机协作方式解决AI审计中的人工智能与人类角色分配问题，以确保在关键判断上保持人类监督。

Abstract

AI auditing increasingly incorporates AI agents to expand the scale and breadth of audit coverage, yet little is known about how auditing work should be divided without displacing human judgment. We introduce Human-Agent Audit Collaboration (HAAC), a workflow and system for structuring human-AI collaboration in AI auditing. Drawing on prior work and formative consultations with AI auditing practitioners, HAAC specifies how agents can support exploration, assessment, reporting, and review while preserving human oversight where contextual judgment is critical. We instantiate HAAC for conversational shopping agents and evaluate it through two studies. With 71 auditors, AI assistance increased attack success and broadened exploration, while also shaping later attacks and increasing auditors' reliance on AI-generated assessments and reports. Interviews with Responsible AI practitioners showed that actionable audits require visibility into coverage, reproducible attack trajectories, and evaluation of the auditing agents themselves. Our findings identify design considerations for effective and accountable human-AI auditing.

Problem

• AI auditing
• Human-AI collaboration
• audit coverage

Innovation

• Human-Agent Audit Collaboration
• AI auditing
• human oversight