The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.
Legal requirements on explainability in machine learning
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Cites
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
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Wrappers for feature subset selection
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Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
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The Black Box Society: The Secret Algorithms That Control Money and Information
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Irrelevant Features and the Subset Selection Problem
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Research works printed but not linked: 2
- Goodman B, Flaxman S (2016) EU regulations on algorithmic decision-making and a “right to explanation”. In: ICML workshop on human interpretability in machine learning, New York, USA
- Lipton ZC (2016) The mythos of model interpretability. In: ICML workshop on human interpretability of machine learning, New York, USA
Cited by
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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
Kiseleva, A., Kotzinos, D., & De Hert, P. (2022) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2022.879603 · Cited by 278
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
Metadata
- Authors
- Bibal, A., Lognoul, M., de Streel, A., & Frénay, B.
- Affiliations
- University of Namur (BE)
- Corresponding author
- Bibal, Adrien
- ORCID
- Bibal, Adrien
- Venue
- Artificial Intelligence and Law
- Published
- 2020-07-30
- Volume
- 29
- Issue
- 2
- Pages
- 149-169
- ISSN
- 0924-8463 (ISSN-L)
- 1572-8382
- OpenAlex
- W3046377295
- Semantic Scholar
- f2929a51055394f22542202500345b31b3791b12
- Cited by
- 165
- References
- 42
- Citations by year
- 2026: 21 · 2025: 20 · 2024: 28 · 2023: 42 · 2022: 34 · 2021: 18 · 2020: 2
- FWCI
- 9.42
- Type
- journal article
- Language
- en
- Open access
- Licence
- Locations
- Artificial Intelligence and Law (publishedVersion)
- Repository of the University of Namur (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Social Sciences › Law › Digitalization, Law, and Regulation
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Keywords
- explainability, model interpretability, legal requirements, black-box models, deep learning, algorithmic accountability, ethical use of AI, public decision-making, private decision-making, interdisciplinary research
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