Legal requirements on explainability in machine learning

Random Forests

Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538

Regression shrinkage and selection via the lasso

Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482

Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization

Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.74 · Cited by 24,513

UCI Machine Learning Repository

Asuncion, A. (2007) · Medical Entomology and Zoology · Cited by 24,190

"Why Should I Trust You?": Explaining the Predictions of Any Classifier

Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939778 · Cited by 17,555

Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

Rudin, C. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0048-x · Cited by 10,561

Wrappers for feature subset selection

Kohavi, R., & John, G. H. (1997) · Artificial Intelligence · doi:10.1016/s0004-3702(97)00043-x · Cited by 9,096

Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Simonyan, K., Vedaldi, A., & Zisserman, A. (2013) · arXiv · doi:10.48550/arxiv.1312.6034 · Cited by 5,370

The Black Box Society: The Secret Algorithms That Control Money and Information

Pasquale, F. (2015) · doi:10.4159/harvard.9780674736061 · Cited by 3,796

Irrelevant Features and the Subset Selection Problem

John, G. H., Kohavi, R., & Pfleger, K. (1994) · Elsevier eBooks · doi:10.1016/b978-1-55860-335-6.50023-4 · Cited by 2,403

Efficient Feature Selection via Analysis of Relevance and Redundancy

Yu, L., & Liu, H. (2004) · Cited by 1,969

The Black Box Society: The Secret Algorithms That Control Money and Information

(2016) · Contemporary Sociology: A Journal of Reviews · doi:10.1177/0094306116641409c · Cited by 1,334

Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation

Wachter, S., Mittelstadt, B., & Floridi, L. (2017) · International Data Privacy Law · doi:10.1093/idpl/ipx005 · Cited by 1,308

All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously

Fisher, A., Rudin, C., & Dominici, F. (2018) · arXiv · doi:10.48550/arxiv.1801.01489 · Cited by 1,165

Predicting judicial decisions of the European Court of Human Rights: a Natural Language Processing perspective

Αλέτρας, Ν., Tsarapatsanis, D., Preoțiuc-Pietro, D., & Lampos, V. (2016) · PeerJ Computer Science · doi:10.7717/peerj-cs.93 · Cited by 813

Explaining Explanations in AI

Mittelstadt, B., Russell, C., & Wachter, S. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287574 · Cited by 794

Fair, Transparent, and Accountable Algorithmic Decision-making Processes: The Premise, the Proposed Solutions, and the Open Challenges

Lepri, B., Oliver, N., Letouzé, E., Pentland, A., & Vinck, P. (2018) · Philosophy & Technology · doi:10.1007/s13347-017-0279-x · Cited by 709

A Survey of Methods for Explaining Black Box Models

Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., & Pedreschi, D. (2019) · ACM Computing Surveys · doi:10.1145/3236009 · Cited by 629

Meaningful information and the right to explanation

Selbst, A. D., & Powles, J. (2017) · International Data Privacy Law · doi:10.1093/idpl/ipx022 · Cited by 516

Accountability of AI Under the Law: The Role of Explanation

Doshi-Velez, F., Kortz, M., Budish, R., Bavitz, C., Gershman, S. J., O'Brien, D., Scott, K., Shieber, S., Waldo, J., Weinberger, D., et al. (2017) · doi:10.2139/ssrn.3064761 · Cited by 457 · cited 3×

The Intuitive Appeal of Explainable Machines

Selbst, A. D., & Barocas, S. (2018) · doi:10.2139/ssrn.3126971 · Cited by 392

Legal Judgment Prediction via Topological Learning

Zhong, H., Guo, Z., Tu, C., Xiao, C., Liu, Z., & Sun, M. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1390 · Cited by 377

Learning to Predict Charges for Criminal Cases with Legal Basis

Luo, B., Feng, Y., Xu, J., Zhang, X., & Zhao, D. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1289 · Cited by 348

Argumentation mining

Palau, R. M., & Moens, M. (2009) · International Conference on Artificial Intelligence and Law · doi:10.1145/1568234.1568246 · Cited by 321

Supersparse linear integer models for optimized medical scoring systems

Ustun, B., & Rudin, C. D. (2015) · Machine Learning · doi:10.1007/s10994-015-5528-6 · Cited by 316

Why a Right to Legibility of Automated Decision-Making Exists in the General Data Protection Regulation

Malgieri, G., & Comandé, G. (2017) · International Data Privacy Law · doi:10.1093/idpl/ipx019 · Cited by 271

Discriminative training of Markov logic networks

Singla, P., & Domingos, P. (2005) · Cited by 247

Automatically classifying case texts and predicting outcomes

Ashley, K. D., & Brüninghaus, S. (2009) · Artificial Intelligence and Law · doi:10.1007/s10506-009-9077-9 · Cited by 159

Interpretable Charge Predictions for Criminal Cases: Learning to Generate Court Views from Fact Descriptions

Ye, H., Jiang, X., Luo, Z., & Chao, W. (2018) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n18-1168 · Cited by 159

Enslaving the Algorithm: From a “Right to an Explanation” to a “Right to Better Decisions”?

Edwards, L., & Veale, M. (2018) · IEEE Security & Privacy · doi:10.1109/msp.2018.2701152 · Cited by 141

All Models are Wrong but many are Useful: Variable Importance for Black-Box, Proprietary, or Misspecified Prediction Models, using Model Class ReliancePreprint

Fisher, A., Rudin, C. D., & Dominici, F. (2018) · arXiv (Cornell University) · Cited by 86

Interpretability of machine learning models and representations: an introduction.

Bibal, A., & Frénay‬, B. (2016) · Repository of the University of Namur · Cited by 77

Data-centric and logic-based models for automated legal problem solving

Branting, L. K. (2017) · Artificial Intelligence and Law · doi:10.1007/s10506-017-9193-x · Cited by 68

Enslaving the Algorithm: From a 'Right to an Explanation' to a 'Right to Better Decisions'?

Edwards, L., & Veale, M. (2018) · Strathprints: The University of Strathclyde institutional repository (University of Strathclyde) · Cited by 53

Research and Design on Cognitive Computing Framework for Predicting Judicial Decisions

Li, J., Zhang, G., Yu, L., & Meng, T. (2018) · Journal of Signal Processing Systems · doi:10.1007/s11265-018-1429-9 · Cited by 31

Supersparse Linear Integer Models for Interpretable Classification

Ustun, B., Tracà, S., & Rudin, C. (2013) · arXiv · doi:10.48550/arxiv.1306.6677 · Cited by 26

La motivation des actes administratifs unilatéraux, entre tradition nationale et évolution des droits européens

Autin, J.-L. (2011) · Revue française d administration publique · doi:10.3917/rfap.137.0085 · Cited by 20

La motivation des décisions juridictionnelles : exigence(s) du droit au procès équitable

Alonso, C. (2012) · Presses de l'Université Toulouse Capitole eBooks · doi:10.4000/books.putc.589 · Cited by 12

Valid interpretation of feature relevance for linear data mappings

Frénay‬, B., Hofmann, D., Schulz, A., Biehl, M. L., & Hammer, B. (2014) · IEEE Symposium on Computational Intelligence and Data Mining (CIDM) · doi:10.1109/cidm.2014.7008661 · Cited by 10

Guest Editor's Introduction

Kodratoff, Y. (1994) · AI Communications · doi:10.3233/aic-1994-7201 · Cited by 9

Supersparse Linear Integer Models for Predictive Scoring SystemsPreprint

Ustun, B., Tracà, S., & Rudin, C. D. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1306.5860 · Cited by 8

La motivation des décisions administratives en droit comparé

Wiener, C. (1969) · Revue internationale de droit comparé · doi:10.3406/ridc.1969.17535 · Cited by 6

Regards sur le droit au procès équitable

Lavergne, B., & Mezaguer, M. (2012) · Presses de l'Université Toulouse Capitole eBooks · doi:10.4000/books.putc.564 · Cited by 5

Interpretable machine learning: Fundamental principles and 10 grand challenges

Rudin, C., Chen, C., Chen, Z., Huang, H., Semenova, L., & Zhong, C. (2022) · Statistics Surveys · doi:10.1214/21-ss133 · Cited by 958

Transparency of AI in Healthcare as a Multilayered System of Accountabilities: Between Legal Requirements and Technical Limitations

Kiseleva, A., Kotzinos, D., & De Hert, P. (2022) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2022.879603 · Cited by 278

Bridging the Gap Between AI and Explainability in the GDPR: Towards Trustworthiness-by-Design in Automated Decision-Making

Hamon, R., Junklewitz, H., Sanchez, I., Malgieri, G., & Hert, P. D. (2022) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2021.3129960 · Cited by 177

Choice modelling in the age of machine learning - Discussion paper

van Cranenburgh, S., Wang, S., Vij, A., Pereira, F. C., & Walker, J. L. (2021) · Journal of Choice Modelling · doi:10.1016/j.jocm.2021.100340 · Cited by 183

Deep learning in insurance: Accuracy and model interpretability using TabNet

McDonnell, K., Murphy, F., Sheehan, B., Masello, L., & Castignani, G. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.119543 · Cited by 156

Impact of Legal Requirements on Explainability in Machine LearningPreprint

Bibal, A., Lognoul, M., de Streel, A., & Frénay‬, B. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.05479 · Cited by 4

The coming of age of interpretable and explainable machine learning models

Lisboa, P. J. G., Saralajew, S., Vellido, A., Fernández-Domenech, R., & Villmann, T. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.02.040 · Cited by 167

How should an explanation be? A mapping of technical and legal desiderata of explanations for machine learning models

Colmenarejo, A. B., State, L., & Comandé, G. (2025) · International Review of Law Computers & Technology · doi:10.1080/13600869.2025.2497633 · Cited by 5

explainy: A Toolkit for Legal-XAI

Kesari, A., Bechtold, S., Ash, E., & Luzzatto, M. (2025) · Repository for Publications and Research Data (ETH Zurich) · doi:10.3929/ethz-c-000794264

Interpretable AI models for judicial decisionmaking: beyond explicability towards legal due process

Canalli, R. L. (2024) · e-Publica · doi:10.47345/v11n1art6 · Cited by 5