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arXiv AI By Samah Kareem, Bar{\i}\c{s} \c{C}elikta\c{s}

Causal Evidentiary Governance for High-Risk Machine Learning Systems

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The paper proposes Causal Evidentiary Governance (CEG), a framework that requires regulated institutions to maintain a versioned directed acyclic graph (DAG) separating allowable from disallowed causal pathways in high‑risk machine learning systems. CEG introduces the Causal Harm Rate to quantify prediction variation due to disallowed pathways and pairs each decision with a signed Decision‑Evidence Packet (DEP) that cryptographically links the prediction to the DAG and path‑specific attributions, enabling efficient inclusion proofs via a Merkle tree. Empirical validation on synthetic credit data and the German Credit dataset demonstrates that CEG more clearly isolates causal effects than traditional fairness metrics and that a proof‑of‑concept implementation shows operational feasibility with manageable performance tradeoffs.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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