mlsanityData to 7 Oct 2026

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

arXiv 2404.12762 ↗First posted 19 Apr 2024cs.AI cs.CYcited by 10

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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 .

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 20 of its 43 references that are in the map. First posted on arXiv 19 Apr 2024.

Builds on

20 of 43 references.

References · 20

  1. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,690
  2. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
  3. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,221
  4. 2017Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)cited by 1,309
  5. 2017Explanation in Artificial Intelligence: Insights from the Social Sciencescited by 1,201
  6. 2017Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPRcited by 1,086
  7. 2018Towards Robust Interpretability with Self-Explaining Neural Networkscited by 505
  8. 2019The many Shapley values for model explanationcited by 341
  9. 2022From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AIcited by 299
  10. 2020Problems with Shapley-value-based explanations as feature importance measurescited by 198
  11. 2021Explainable AI (XAI): A Systematic Meta-Survey of Current Challenges and Future Opportunitiescited by 125
  12. 2019Sanity Checks for Saliency Metricscited by 119
8 more
  1. 2017Explainable AI: Beware of Inmates Running the Asylum Or: How I Learnt to Stop Worrying and Love the Social and Behavioural Sciencescited by 84
  2. 2023Who Should I Trust: AI or Myself? Leveraging Human and AI Correctness Likelihood to Promote Appropriate Trust in AI-Assisted Decision-Makingcited by 67
  3. 2021Introduction to Neural Network Verificationcited by 63
  4. 2018Interpretable to Whom? A Role-based Model for Analyzing Interpretable Machine Learning Systemscited by 47
  5. 2023Bridging the Transparency Gap: What Can Explainable AI Learn From the AI Act?cited by 10
  6. 2019The Explanation Game: Explaining Machine Learning Models Using Shapley Valuescited by 8
  7. 2020Towards Interpretable ANNs: An Exact Transformation to Multi-Class Multivariate Decision Treescited by 6
  8. 2023Model Reporting for Certifiable AI: A Proposal from Merging EU Regulation into AI Developmentcited by 6

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 5; 2026: 4.

10 papers.

  1. 2025Bridging the Gap in XAI-Why Reliable Metrics Matter for Explainability and Compliancecited by 6
  2. 2026Explanation Multiplicity in SHAP: Characterization and Assessmentcited by 5
  3. 2025SHAP-based Explanations are Sensitive to Feature Representationcited by 4
  4. 2025NomicLaw: Emergent Trust and Strategic Argumentation in LLMs During Collaborative Law-Makingcited by 4
  5. 2024The Contribution of XAI for the Safe Development and Certification of AI: An Expert-Based Analysiscited by 2
  6. 2025Engineering the Law-Machine Learning Translation Problem: Developing Legally Aligned Modelscited by 2
  7. 2026Assessing Model-Agnostic XAI Methods against EU AI Act Explainability Requirementscited by 1
  8. 2026An End-to-End Decision-Aware Multi-Scale Attention-Based Model for Explainable Autonomous Drivingcited by 1
  9. 2025WebXAII: an open-source web framework to study human-XAI interactioncited by 0
  10. 2026Explainable AI for the EU Right to Explanation: A Systematic Review of the Law-XAI Translation Gapcited by 0