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

Pratinav Seth, Vinay Kumar Sankarapu

TL;DR

The paper argues that reliable, governance-aligned XAI metrics are essential for private oversight and regulatory compliance in high-stakes AI. It introduces the Governance-by-Metrics framework, a hierarchical model linking Transparency, Tamper-Resistance, Scalability, and Regulatory Alignment, and situates explainability metrics as auditable primitives within continuous AI assurance pipelines. The authors highlight three novel contributions—tamper-resistant metrics, explicit regulatory alignment, and cross-modality integration—and present a three-phase roadmap (Metric Integrity, Private Assurance, Regulatory Interoperability) to operationalize these metrics. The work envisions GPAI governance where interpretability not only explains behavior but also enforces behavioral integrity across jurisdictions, enabling scalable, auditable oversight, liability assessment, and procurement-based governance.

Abstract

Reliable explainability is not only a technical goal but also a cornerstone of private AI governance. As AI models enter high-stakes sectors, private actors such as auditors, insurers, certification bodies, and procurement agencies require standardized evaluation metrics to assess trustworthiness. However, current XAI evaluation metrics remain fragmented and prone to manipulation, which undermines accountability and compliance. We argue that standardized metrics can function as governance primitives, embedding auditability and accountability within AI systems for effective private oversight. Building upon prior work in XAI benchmarking, we identify key limitations in ensuring faithfulness, tamper resistance, and regulatory alignment. Furthermore, interpretability can directly support model alignment by providing a verifiable means of ensuring behavioral integrity in General Purpose AI (GPAI) systems. This connection between interpretability and alignment positions XAI metrics as both technical and regulatory instruments that help prevent alignment faking, a growing concern among oversight bodies. We propose a Governance by Metrics paradigm that treats explainability evaluation as a central mechanism of private AI governance. Our framework introduces a hierarchical model linking transparency, tamper resistance, scalability, and legal alignment, extending evaluation from model introspection toward systemic accountability. Through conceptual synthesis and alignment with governance standards, we outline a roadmap for integrating explainability metrics into continuous AI assurance pipelines that serve both private oversight and regulatory needs.

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

TL;DR

The paper argues that reliable, governance-aligned XAI metrics are essential for private oversight and regulatory compliance in high-stakes AI. It introduces the Governance-by-Metrics framework, a hierarchical model linking Transparency, Tamper-Resistance, Scalability, and Regulatory Alignment, and situates explainability metrics as auditable primitives within continuous AI assurance pipelines. The authors highlight three novel contributions—tamper-resistant metrics, explicit regulatory alignment, and cross-modality integration—and present a three-phase roadmap (Metric Integrity, Private Assurance, Regulatory Interoperability) to operationalize these metrics. The work envisions GPAI governance where interpretability not only explains behavior but also enforces behavioral integrity across jurisdictions, enabling scalable, auditable oversight, liability assessment, and procurement-based governance.

Abstract

Reliable explainability is not only a technical goal but also a cornerstone of private AI governance. As AI models enter high-stakes sectors, private actors such as auditors, insurers, certification bodies, and procurement agencies require standardized evaluation metrics to assess trustworthiness. However, current XAI evaluation metrics remain fragmented and prone to manipulation, which undermines accountability and compliance. We argue that standardized metrics can function as governance primitives, embedding auditability and accountability within AI systems for effective private oversight. Building upon prior work in XAI benchmarking, we identify key limitations in ensuring faithfulness, tamper resistance, and regulatory alignment. Furthermore, interpretability can directly support model alignment by providing a verifiable means of ensuring behavioral integrity in General Purpose AI (GPAI) systems. This connection between interpretability and alignment positions XAI metrics as both technical and regulatory instruments that help prevent alignment faking, a growing concern among oversight bodies. We propose a Governance by Metrics paradigm that treats explainability evaluation as a central mechanism of private AI governance. Our framework introduces a hierarchical model linking transparency, tamper resistance, scalability, and legal alignment, extending evaluation from model introspection toward systemic accountability. Through conceptual synthesis and alignment with governance standards, we outline a roadmap for integrating explainability metrics into continuous AI assurance pipelines that serve both private oversight and regulatory needs.

Paper Structure

This paper contains 21 sections, 2 figures.

Table of Contents

  1. Introduction
  2. Position:
  3. Background and Context
  4. Explainability Methods
  5. The Role of Evaluation Metrics in XAI
  6. Existing Evaluation Frameworks and Benchmarking Tools for Model Explainability
  7. Distinctive Contributions and Novelty
  8. Challenges in XAI Metrics
  9. Neglect of Modern AI Models
  10. Fragmentation and Manipulation Risks
  11. Mechanistic Interpretability and Alignment for Governance
  12. Key Requirements for Reliable Metrics
  13. Alternative Views
  14. Governance-by-Design: A Three-Phase Roadmap
  15. Policy Integration: Private and Public Governance Mechanisms
  16. ...and 6 more sections

Figures (2)

  • Figure 1: Governance Hierarchy for XAI Evaluation. This pyramid illustrates foundational Transparency & Robustness, building up through Adaptability & Tamper-Resistance and Scalability, to Regulatory & Legal Aspects.
  • Figure 2: The Iterative Feedback Loop in XAI Regulation and Governance Compliance. Regulatory standards influence XAI evaluation metrics and AI models; subsequent real-world adoption and challenges then refine these standards and metrics to align with evolving AI capabilities.