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research-article

Legal requirements on explainability in machine learning

Authors:

Michael Lognoul,

Alexandre de Streel,

Benoît FrénayAuthors Info & Claims

Artificial Intelligence and Law, Volume 29, Issue 2

Pages 149 - 169

https://doi.org/10.1007/s10506-020-09270-4

Published: 01 June 2021 Publication History

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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Cited By

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Published In

cover image Artificial Intelligence and Law

Artificial Intelligence and Law Volume 29, Issue 2

Jun 2021

171 pages

ISSN:0924-8463

Issue’s Table of Contents

© Springer Nature B.V. 2020.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 June 2021

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Cited By

Gilman EBugiotti FKhalid AMehmood HKostakos PTuovinen LYlipulli JSu XFerreira D(2024)Addressing Data Challenges to Drive the Transformation of Smart CitiesACM Transactions on Intelligent Systems and Technology10.1145/366348215:5(1-65)Online publication date: 7-Nov-2024
https://dl.acm.org/doi/10.1145/3663482
Sovrano FLognoul MBacchelli ARoychoudhury PPaiva AAbreu RStorey MSharif BXia X(2024)An Empirical Study on Compliance with Ranking Transparency in the Software Documentation of EU Online PlatformsProceedings of the 46th International Conference on Software Engineering: Software Engineering in Society10.1145/3639475.3640112(46-56)Online publication date: 14-Apr-2024
https://dl.acm.org/doi/10.1145/3639475.3640112
Lu QZhu LXu XWhittle JZowghi DJacquet A(2024)Responsible AI Pattern Catalogue: A Collection of Best Practices for AI Governance and EngineeringACM Computing Surveys10.1145/362623456:7(1-35)Online publication date: 9-Apr-2024
https://dl.acm.org/doi/10.1145/3626234
Bertrand AEagan JMaxwell WBrand J(2024)AI is Entering Regulated Territory: Understanding the Supervisors' Perspective for Model Justifiability in Financial Crime DetectionProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642326(1-21)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642326
Barrera JReina-Reina ALavalle AMaté ATrujillo J(2024)An extension of iStar for Machine Learning requirements by following the PRISE methodologyComputer Standards & Interfaces10.1016/j.csi.2023.10380688:COnline publication date: 1-Mar-2024
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Sovrano FVitali F(2024)Explanatory artificial intelligence (YAI): human-centered explanations of explainable AI and complex dataData Mining and Knowledge Discovery10.1007/s10618-022-00872-x38:5(3141-3168)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1007/s10618-022-00872-x
Greco CTagarelli A(2024)Bringing order into the realm of Transformer-based language models for artificial intelligence and lawArtificial Intelligence and Law10.1007/s10506-023-09374-732:4(863-1010)Online publication date: 1-Dec-2024
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Brożek BFurman MJakubiec MKucharzyk B(2024)The black box problem revisited. Real and imaginary challenges for automated legal decision makingArtificial Intelligence and Law10.1007/s10506-023-09356-932:2(427-440)Online publication date: 1-Jun-2024
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Mentzingen HAntonio NLobo V(2024)Joining metadata and textual features to advise administrative courts decisions: a cascading classifier approachArtificial Intelligence and Law10.1007/s10506-023-09348-932:1(201-230)Online publication date: 1-Mar-2024
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Xue AAlur RWong EOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Stability guarantees for feature attributions with multiplicative smoothingProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3668846(62388-62413)Online publication date: 10-Dec-2023
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