# Beyond Explainability: The Case for AI Validation

arXiv: https://arxiv.org/abs/2505.21570 · HTML: https://mlsanity.com/paper/2505.21570/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 86 references that are in the map.
- First posted on arXiv 27 May 2025.

## Builds on (most cited first, 13 of 13)

- [Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods](https://mlsanity.com/paper/1911.02508/) (2019)
- [Monitoring Reasoning Models for Misbehavior and the Risks of Promoting Obfuscation](https://mlsanity.com/paper/2503.11926/) (2025)
- [Manipulating and Measuring Model Interpretability](https://mlsanity.com/paper/1802.07810/) (2018)
- [Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening](https://mlsanity.com/paper/1903.08297/) (2019)
- [Evaluation of post-hoc interpretability methods in time-series classification](https://mlsanity.com/paper/2202.05656/) (2022)
- [Fairness and Bias in Algorithmic Hiring: a Multidisciplinary Survey](https://mlsanity.com/paper/2309.13933/) (2023)
- [Systematic Literature Review of Validation Methods for AI Systems](https://mlsanity.com/paper/2107.12190/) (2021)
- [AI Failures: A Review of Underlying Issues](https://mlsanity.com/paper/2008.04073/) (2020)
- [Painting the black box white: experimental findings from applying XAI to an ECG reading setting](https://mlsanity.com/paper/2210.15236/) (2022)
- [A Blueprint for Auditing Generative AI](https://mlsanity.com/paper/2407.05338/) (2024)
- [Engineering Trustworthy AI: A Developer Guide for Empirical Risk Minimization](https://mlsanity.com/paper/2410.19361/) (2024)
- [Beware of "Explanations" of AI](https://mlsanity.com/paper/2504.06791/) (2025)
- [The Case Against Explainability](https://mlsanity.com/paper/2305.12167/) (2023)

## Built on by (most cited first, 1 of 1)

- [Policy-Driven AI in Dataspaces: Taxonomy, Explainability, and Pathways for Compliant Innovation](https://mlsanity.com/paper/2507.20014/) (2025)

## Abstract (arXiv)

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.