VQV Signal
The paper introduces Human-Agent Audit Collaboration (HAAC), a system designed to structure collaboration between humans and AI agents in auditing generative AI. HAAC aims to expand audit coverage while preserving human judgment by clearly dividing auditing tasks.
The paper introduces Human-Agent Audit Collaboration (HAAC), a system designed to structure collaboration between humans and AI agents in auditing generative AI. HAAC aims to expand audit coverage while preserving human judgment by clearly dividing auditing tasks.
AI-assisted summary based on the listed source.
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...
As AI auditing scales, balancing AI agent involvement with human oversight is crucial to maintain audit quality and trust. HAAC provides a framework to optimize this collaboration, addressing a key challenge in auditing generative AI systems.
VQV organizes public signals from inspectable sources. It does not independently verify the underlying report.
Signal Strength reflects source quality, relevance, freshness and evidence. Public Interest helps organize discovery; it is not proof of truth.
VQV surfaced this signal because it is recent, relevant to AI Agents, connected to arXiv.
No login, cookies, social SDKs, or automatic posting.