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Beyond Explainability: The Case for AI Validation

Dalit Ken-Dror Feldman, Daniel Benoliel

TL;DR

The paper argues that opacity in artificial knowledge systems creates governance gaps in high-stakes domains, and explainability alone is insufficient. It introduces a validity–explainability matrix to classify AK into four quadrants and analyzes regulatory approaches from the EU, US, UK, and China. It proposes a policy framework centered on pre- and post-deployment validation, independent auditing, harmonized standards, and liability incentives to improve trust, safety, and fairness. The work outlines a practical roadmap for responsibly integrating opaque, high-performing AK systems into society while preserving accountability.

Abstract

Artificial Knowledge (AK) systems are transforming decision-making across critical domains such as healthcare, finance, and criminal justice. However, their growing opacity presents governance challenges that current regulatory approaches, focused predominantly on explainability, fail to address adequately. This article argues for a shift toward validation as a central regulatory pillar. Validation, ensuring the reliability, consistency, and robustness of AI outputs, offers a more practical, scalable, and risk-sensitive alternative to explainability, particularly in high-stakes contexts where interpretability may be technically or economically unfeasible. We introduce a typology based on two axes, validity and explainability, classifying AK systems into four categories and exposing the trade-offs between interpretability and output reliability. Drawing on comparative analysis of regulatory approaches in the EU, US, UK, and China, we show how validation can enhance societal trust, fairness, and safety even where explainability is limited. We propose a forward-looking policy framework centered on pre- and post-deployment validation, third-party auditing, harmonized standards, and liability incentives. This framework balances innovation with accountability and provides a governance roadmap for responsibly integrating opaque, high-performing AK systems into society.

Beyond Explainability: The Case for AI Validation

TL;DR

The paper argues that opacity in artificial knowledge systems creates governance gaps in high-stakes domains, and explainability alone is insufficient. It introduces a validity–explainability matrix to classify AK into four quadrants and analyzes regulatory approaches from the EU, US, UK, and China. It proposes a policy framework centered on pre- and post-deployment validation, independent auditing, harmonized standards, and liability incentives to improve trust, safety, and fairness. The work outlines a practical roadmap for responsibly integrating opaque, high-performing AK systems into society while preserving accountability.

Abstract

Artificial Knowledge (AK) systems are transforming decision-making across critical domains such as healthcare, finance, and criminal justice. However, their growing opacity presents governance challenges that current regulatory approaches, focused predominantly on explainability, fail to address adequately. This article argues for a shift toward validation as a central regulatory pillar. Validation, ensuring the reliability, consistency, and robustness of AI outputs, offers a more practical, scalable, and risk-sensitive alternative to explainability, particularly in high-stakes contexts where interpretability may be technically or economically unfeasible. We introduce a typology based on two axes, validity and explainability, classifying AK systems into four categories and exposing the trade-offs between interpretability and output reliability. Drawing on comparative analysis of regulatory approaches in the EU, US, UK, and China, we show how validation can enhance societal trust, fairness, and safety even where explainability is limited. We propose a forward-looking policy framework centered on pre- and post-deployment validation, third-party auditing, harmonized standards, and liability incentives. This framework balances innovation with accountability and provides a governance roadmap for responsibly integrating opaque, high-performing AK systems into society.

Paper Structure

This paper contains 9 sections, 1 table.