Causal Evidentiary Governance for High-Risk Machine Learning Systems

A new framework, Causal Evidentiary Governance (CEG), is proposed for regulating high-risk Machine Learning (ML) systems, particularly those used in sensitive domains like credit, hiring, and resource distribution. Unlike current practices that rely on observational fairness metrics and post-hoc explainability, CEG focuses on causal attribution by requiring institutions to define and verify allowable versus disallowed causal pathways within their ML models. This aims to provide more robust evidentiary verification in response to growing regulatory oversight.

Why it matters

The increasing regulatory focus on ethical and fair deployment of high-risk Machine Learning systems necessitates advanced governance frameworks. Adopting methodologies like Causal Evidentiary Governance could become crucial for organizations to demonstrate compliance, manage algorithmic risk, and maintain public trust in their AI-driven operations.

Key insights

• Existing fairness governance practices for ML systems, such as observational metrics and post-hoc explainability, are deemed insufficient for causal attribution and efficient evidentiary verification.
• High-risk ML systems (e.g., in credit, hiring, resource distribution) are subject to increasing regulatory scrutiny, including from policies like the EU AI Act and GDPR.
• Causal Evidentiary Governance (CEG) is introduced as a framework where regulated institutions define versioned directed acyclic graphs (DAGs) to categorize causal pathways as allowable or disallowed.
• CEG proposes a 'Causal Harm Rate' to quantify prediction variation directly attributable to disallowed causal pathways.
• The framework mandates that each decision made by an ML system under CEG is accompanied by a 'signed Decisi' (likely 'Decision Report' or similar, though the source text is truncated).

Source

arXiv — Computers and Society — https://arxiv.org/abs/2609.01040