# How should AI decisions be explained? Requirements for Explanations from the Perspective of European Law

arXiv: https://arxiv.org/abs/2404.12762 · HTML: https://mlsanity.com/paper/2404.12762/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 43 references that are in the map.
- First posted on arXiv 19 Apr 2024.

## Builds on (most cited first, 20 of 20)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Membership Inference Attacks against Machine Learning Models](https://mlsanity.com/paper/1610.05820/) (2016)
- [Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)](https://mlsanity.com/paper/1711.11279/) (2017)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR](https://mlsanity.com/paper/1711.00399/) (2017)
- [Towards Robust Interpretability with Self-Explaining Neural Networks](https://mlsanity.com/paper/1806.07538/) (2018)
- [The many Shapley values for model explanation](https://mlsanity.com/paper/1908.08474/) (2019)
- [From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI](https://mlsanity.com/paper/2201.08164/) (2022)
- [Problems with Shapley-value-based explanations as feature importance measures](https://mlsanity.com/paper/2002.11097/) (2020)
- [Explainable AI (XAI): A Systematic Meta-Survey of Current Challenges and Future Opportunities](https://mlsanity.com/paper/2111.06420/) (2021)
- [Sanity Checks for Saliency Metrics](https://mlsanity.com/paper/1912.01451/) (2019)
- [Explainable AI: Beware of Inmates Running the Asylum Or: How I Learnt to Stop Worrying and Love the Social and Behavioural Sciences](https://mlsanity.com/paper/1712.00547/) (2017)
- [Who Should I Trust: AI or Myself? Leveraging Human and AI Correctness Likelihood to Promote Appropriate Trust in AI-Assisted Decision-Making](https://mlsanity.com/paper/2301.05809/) (2023)
- [Introduction to Neural Network Verification](https://mlsanity.com/paper/2109.10317/) (2021)
- [Interpretable to Whom? A Role-based Model for Analyzing Interpretable Machine Learning Systems](https://mlsanity.com/paper/1806.07552/) (2018)
- [Bridging the Transparency Gap: What Can Explainable AI Learn From the AI Act?](https://mlsanity.com/paper/2302.10766/) (2023)
- [The Explanation Game: Explaining Machine Learning Models Using Shapley Values](https://mlsanity.com/paper/1909.08128/) (2019)
- [Towards Interpretable ANNs: An Exact Transformation to Multi-Class Multivariate Decision Trees](https://mlsanity.com/paper/2003.04675/) (2020)
- [Model Reporting for Certifiable AI: A Proposal from Merging EU Regulation into AI Development](https://mlsanity.com/paper/2307.11525/) (2023)

## Built on by (most cited first, 11 of 11)

- [Bridging the Gap in XAI-Why Reliable Metrics Matter for Explainability and Compliance](https://mlsanity.com/paper/2502.04695/) (2025)
- [Explanation Multiplicity in SHAP: Characterization and Assessment](https://mlsanity.com/paper/2601.12654/) (2026)
- [SHAP-based Explanations are Sensitive to Feature Representation](https://mlsanity.com/paper/2505.08345/) (2025)
- [NomicLaw: Emergent Trust and Strategic Argumentation in LLMs During Collaborative Law-Making](https://mlsanity.com/paper/2508.05344/) (2025)
- [Engineering the Law-Machine Learning Translation Problem: Developing Legally Aligned Models](https://mlsanity.com/paper/2504.16969/) (2025)
- [The Contribution of XAI for the Safe Development and Certification of AI: An Expert-Based Analysis](https://mlsanity.com/paper/2408.02379/) (2024)
- [Assessing Model-Agnostic XAI Methods against EU AI Act Explainability Requirements](https://mlsanity.com/paper/2604.09628/) (2026)
- [An End-to-End Decision-Aware Multi-Scale Attention-Based Model for Explainable Autonomous Driving](https://mlsanity.com/paper/2605.00291/) (2026)
- [WebXAII: an open-source web framework to study human-XAI interaction](https://mlsanity.com/paper/2506.14777/) (2025)
- [Explainable AI for the EU Right to Explanation: A Systematic Review of the Law-XAI Translation Gap](https://mlsanity.com/paper/2608.02699/) (2026)
- [Adapting Vision-Language Models for Human-Readable XAI in Industrial Object Detection](https://mlsanity.com/paper/2609.31690/) (2026)

## Abstract (arXiv)

This paper investigates the relationship between law and eXplainable Artificial Intelligence (XAI). While there is much discussion about the AI Act, for which the trilogue of the European Parliament, Council and Commission recently concluded, other areas of law seem underexplored. This paper focuses on European (and in part German) law, although with international concepts and regulations such as fiduciary plausibility checks, the General Data Protection Regulation (GDPR), and product safety and liability. Based on XAI-taxonomies, requirements for XAI-methods are derived from each of the legal bases, resulting in the conclusion that each legal basis requires different XAI properties and that the current state of the art does not fulfill these to full satisfaction, especially regarding the correctness (sometimes called fidelity) and confidence estimates of XAI-methods. Published in the Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society https://doi.org/10.1609/aies.v7i1.31648 .