# Assessing Model-Agnostic XAI Methods against EU AI Act Explainability Requirements

arXiv: https://arxiv.org/abs/2604.09628 · HTML: https://mlsanity.com/paper/2604.09628/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 45 references that are in the map.
- First posted on arXiv 19 Mar 2026.

## Builds on (most cited first, 17 of 17)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations](https://mlsanity.com/paper/1905.07697/) (2019)
- [From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI](https://mlsanity.com/paper/2201.08164/) (2022)
- [Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives](https://mlsanity.com/paper/1802.07623/) (2018)
- [Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions](https://mlsanity.com/paper/2310.19775/) (2023)
- [Interpreting Blackbox Models via Model Extraction](https://mlsanity.com/paper/1705.08504/) (2017)
- [Question-Driven Design Process for Explainable AI User Experiences](https://mlsanity.com/paper/2104.03483/) (2021)
- [What Makes a Good Explanation?: A Harmonized View of Properties of Explanations](https://mlsanity.com/paper/2211.05667/) (2022)
- [How should AI decisions be explained? Requirements for Explanations from the Perspective of European Law](https://mlsanity.com/paper/2404.12762/) (2024)
- [Why Should I Choose You? AutoXAI: A Framework for Selecting and Tuning eXplainable AI Solutions](https://mlsanity.com/paper/2210.02795/) (2022)
- [Bridging the Transparency Gap: What Can Explainable AI Learn From the AI Act?](https://mlsanity.com/paper/2302.10766/) (2023)
- [Modelling GDPR-Compliant Explanations for Trustworthy AI](https://mlsanity.com/paper/2109.04165/) (2021)
- [A Survey on Methods and Metrics for the Assessment of Explainability under the Proposed AI Act](https://mlsanity.com/paper/2110.11168/) (2021)
- [Can Global XAI Methods Reveal Injected Behaviours in LLMs? SHAP vs Rule Extraction vs RuleSHAP](https://mlsanity.com/paper/2505.11189/) (2025)
- [Aligning XAI with EU Regulations for Smart Biomedical Devices: A Methodology for Compliance Analysis](https://mlsanity.com/paper/2408.15121/) (2024)

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

- [Security and Privacy in Agentic AI: Grand Challenges and Future Directions](https://mlsanity.com/paper/2607.06608/) (2026)

## Abstract (arXiv)

Explainable AI (XAI) has evolved in response to expectations and regulations, such as the EU AI Act, which introduces regulatory requirements on AI-powered systems. However, a persistent gap remains between existing XAI methods and society's legal requirements, leaving practitioners without clear guidance on how to approach compliance in the EU market. To bridge this gap, we study model-agnostic XAI methods and relate their interpretability features to the requirements of the AI Act. We then propose a qualitative-to-quantitative scoring framework: qualitative expert assessments of XAI properties are aggregated into a regulation-specific compliance score. This helps practitioners identify when XAI solutions may support legal explanation requirements while highlighting technical issues that require further research and regulatory clarification.