Bridging the Gap in XAI-Why Reliable Metrics Matter for Explainability and Compliance

Standardized explainability metrics are essential for AI oversight: Current XAI (explainable AI) evaluation methods are fragmented and inconsistent, making it difficult for auditors, insurers, and certification bodies to reliably assess whether AI systems are trustworthy—a critical gap that standardized metrics could address.

Explainability metrics can prevent "alignment faking": The paper identifies a growing concern that AI systems could appear aligned with human values while actually behaving differently, and argues that robust interpretability measures provide a verifiable way to ensure AI systems genuinely behave as intended, not just appear to.

Current metrics are vulnerable to manipulation: Existing explainability evaluation methods lack tamper resistance, meaning they can be gamed or manipulated to make AI systems appear more trustworthy than they actually are—undermining accountability and compliance efforts.

Explainability should be treated as a governance tool, not just a technical feature: Rather than viewing explainability solely as a way to understand how models work, the paper proposes treating standardized XAI metrics as formal governance mechanisms that embed auditability and accountability directly into AI systems for private sector oversight.

A hierarchical framework linking transparency, tamper resistance, and legal compliance is needed: The paper proposes connecting explainability evaluation across multiple levels—from technical model inspection to systemic accountability—to ensure AI systems meet both technical standards and regulatory requirements.