Legal requirements on explainability in machine learning

Adrien Bibal; Michael Lognoul; Alexandre De Streel; Benoît Frénay

doi:10.1007/s10506-020-09270-4

Legal requirements on explainability in machine learning

Adrien Bibal, Michael Lognoul, Alexandre De Streel, Benoît Frénay

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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.

Original language	English
Pages (from-to)	149-169
Number of pages	21
Journal	Artificial Intelligence and Law
Volume	29
Issue number	2
DOIs	https://doi.org/10.1007/s10506-020-09270-4 https://doi.org/10.1007/s10506-020-09270-4
Publication status	Published - Jun 2021

Keywords

Explainability
Interpretability
Law
Machine learning

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title = "Legal requirements on explainability in machine learning",

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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N2 - 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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KW - Explainability

KW - Interpretability

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KW - Machine learning

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Legal requirements on explainability in machine learning

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Interpretability and Explainability in Machine Learning and their Application to Nonlinear Dimensionality Reduction

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Legal requirements on explainability in machine learning

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Student theses

Interpretability and Explainability in Machine Learning and their Application to Nonlinear Dimensionality Reduction

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