New framework details AI forensics across different access levels

A new research paper proposes a framework for investigating incidents involving AI systems, categorizing approaches based on the level of access investigators have to the system. The paper distinguishes between white box, grey box, and black box access, outlining how each affects the collection, preservation, and analysis of evidence. It also introduces an order of volatility for AI system components, from runtime state to training lineage, and identifies key research challenges such as black box preservation and model version attestation. AI

IMPACT Establishes a structured approach for investigating AI incidents, potentially improving accountability and safety.

RANK_REASON The cluster contains a single academic paper detailing a new research agenda and process model for AI forensics. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• AI Forensics
• arXiv
• context windows
• grey box model
• logs
• model artifacts
• retrieval stores
• runtime state
• training lineage
• white box

• paper
• safety

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