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Artificial Intelligence and Law

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30.07.2020 | Original Research

Legal requirements on explainability in machine learning

verfasst von: Adrien Bibal, Michael Lognoul, Alexandre de Streel, Benoît Frénay

Erschienen in: Artificial Intelligence and Law | Ausgabe 2/2021

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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.

Vorheriger Artikel Evaluating causes of algorithmic bias in juvenile criminal recidivism

Nächster Artikel Populating legal ontologies using semantic role labeling

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Titel: Legal requirements on explainability in machine learning
verfasst von: Adrien Bibal
Michael Lognoul
Alexandre de Streel
Benoît Frénay
Publikationsdatum: 30.07.2020
Verlag: Springer Netherlands
Erschienen in: Artificial Intelligence and Law / Ausgabe 2/2021
Print ISSN: 0924-8463
Elektronische ISSN: 1572-8382
DOI: https://doi.org/10.1007/s10506-020-09270-4

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