Abstract
Deep learning and other black-box models are becoming more and more popular today. Despite their high performance, they may not be accepted ethically or legally because of their lack of explainability. This paper presents the increasing number of legal requirements on machine learning model interpretability and explainability in the context of private and public decision making. It then explains how those legal requirements can be implemented into machine-learning models and concludes with a call for more inter-disciplinary research on explainability.
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References
Aletras N, Tsarapatsanis D, Preoţiuc-Pietro D, Lampos V (2016) Predicting judicial decisions of the European court of human rights: a natural language processing perspective. PeerJ Comput Sci 2:e93
Alonso C (2012) La motivation des décisions juridictionnelles : exigence(s) du droit au procès équitable. In Regards sur le droit au procès équitable. Presses de l’Université Toulouse 1 Capitole
Ashley KD, Brüninghaus S (2009) Automatically classifying case texts and predicting outcomes. Artif Intell Law 17(2):125–165
Autin J-L (2011) La motivation des actes administratifs unilatéraux, entre tradition nationale et évolution des droits européens. Revue française d’administration publique 1:85–99
Bibal A, Frénay B (2016) Interpretability of machine learning models and representations: an introduction. In: Proceedings of the European symposium on artificial neural networks, computational intelligence and machine learning (ESANN), Bruges, Belgium, pp 77–82
Branting LK (2017) Data-centric and logic-based models for automated legal problem solving. Artif Intell Law 25(1):5–27
Breiman L (2001) Random forests. Mach Learn 45(1):5–32
Doshi-Velez F, Kortz M (2017) Accountability of AI under the law: the role of explanation. arXiv preprint arXiv:1711.01134
Dua D, Graff C (2017) UCI machine learning repository. http://archive.ics.uci.edu/ml
Edwards L, Veale M (2018) Enslaving the algorithm: from a “right to an explanation” to a “right to better decisions”? IEEE Secur Priv 16(3):46–54
Fisher A, Rudin C, Dominici F (2018) All models are wrong but many are useful: variable importance for black-box, proprietary, or misspecified prediction models, using model class reliance. arXiv preprint arXiv:1801.01489
Frénay B, Hofmann D, Schulz A, Biehl M, Hammer B (2014) Valid interpretation of feature relevance for linear data mappings. In: IEEE Symposium on Computational Intelligence and Data Mining (CIDM), pp 149–156
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
Guidotti R, Monreale A, Ruggieri S, Turini F, Giannotti F, Pedreschi D (2018) A survey of methods for explaining black box models. ACM Comput Surv 51(5):1–42
John G. H, Kohavi R, Pfleger K (1994) Irrelevant features and the subset selection problem. In: International conference on machine learning (ICML), pp 121–129
Kodratoff Y (1994) The comprehensibility manifesto. AI Commun 7(2):83–85
Kohavi R, John GH (1997) Wrappers for feature subset selection. Artif Intell 97(1–2):273–324
Lepri B, Oliver N, Letouzé E, Pentland A, Vinck P (2018) Fair, transparent, and accountable algorithmic decision-making processes. Philos Technol 31(4):611–627
Li J, Zhang G, Yu L, Meng T (2018) Research and design on cognitive computing framework for predicting judicial decisions. J Signal Process Syst 91:1159–1167
Lipton ZC (2016) The mythos of model interpretability. In: ICML workshop on human interpretability of machine learning, New York, USA
Luo B, Feng Y, Xu J, Zhang X, Zhao D (2017) Learning to predict charges for criminal cases with legal basis. In: Proceedings of the conference on empirical methods in natural language processing (EMNLP), pp. 2727–2736
Malgieri G, Comandé G (2017) Why a right to legibility of automated decision-making exists in the general data protection regulation. Int Data Priv Law 7(4):243–265
Mittelstadt B, Russell C, Wachter S (2019) Explaining explanations in AI. In: Proceedings of the conference on fairness, accountability, and transparency (FAT), pp 279–288
Palau RM, Moens MF (2009) Argumentation mining: the detection, classification and structure of arguments in text. In: Proceedings of the international conference on artificial intelligence and law (ICAIL), pp 98–107
Pasquale F (2015) Black box society, the secret algorithms that control money and information. Harvard University Press, New York
Ribeiro MT, Singh S, Guestrin C (2016) “Why should I trust you?”: explaining the predictions of any classifier. In: Proceedings of the ACM SIGKDD, pp. 1135–1144
Rudin C (2019) Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat Mach Intell 1(5):206–2015
Selbst AD, Barocas S (2018) The intuitive appeal of explainable machines. Fordham Law Rev 87:1085–1139
Selbst AD, Powles J (2017) Meaningful information and the right to explanation. Int Data Privacy Law 7(4):233–242
Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2017) Grad-CAM: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE international conference on computer vision (ICCV), pp 618–626
Simonyan K, Vedaldi A, Zisserman A (2013) Deep inside convolutional networks: visualising image classification models and saliency maps. arXiv preprint aDrXiv:1312.6034
Singla P, Domingos P (2005) Discriminative training of markov logic networks. In: National conference on artificial intelligence (AAAI), pp 868–873
Tibshirani R (1996) Regression shrinkage and selection via the lasso. J Roy Stat Soc Ser B (Methodol) 58(1):267–288
Ustun B, Rudin C (2016) Supersparse linear integer models for optimized medical scoring systems. Mach Learn 102(3):349–391
Ustun B, Traca S, Rudin C (2013a) Supersparse linear integer models for interpretable classification. arXiv preprint arXiv:1306.6677
Ustun B, Traca S, Rudin C (2013b) Supersparse linear integer models for predictive scoring systems. In: Proceedings of AAAI late breaking track
Wachter S, Mittelstadt B, Floridi L (2017) Why a right to explanation of automated decision-making does not exist in the general data protection regulation. Int Data Privacy Law 7(2):76–99
Wiener C (1969) La motivation des décisions administratives en droit comparé. Revue internationale de droit comparé 21(21):779–795
Ye H, Jiang X, Luo Z, Chao W (2018) Interpretable charge predictions for criminal cases: learning to generate court views from fact descriptions. In: Proceedings of the conference of the North American chapter of the association for computational linguistics: human language technologies, pp 1854–1864
Yu L, Liu H (2004) Efficient feature selection via analysis of relevance and redundancy. J Mach Learn Res 5:1205–1224
Zhong H, Zhipeng G, Tu C, Xiao C, Liu Z, Sun M (2018) Legal judgment prediction via topological learning. In: Proceedings of the conference on empirical methods in natural language processing (EMNLP), pp 3540–3549
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Bibal, A., Lognoul, M., de Streel, A. et al. Legal requirements on explainability in machine learning. Artif Intell Law 29, 149–169 (2021). https://doi.org/10.1007/s10506-020-09270-4
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DOI: https://doi.org/10.1007/s10506-020-09270-4