Impact of Legal Requirements on Explainability in Machine Learning

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

Impact of Legal Requirements on Explainability in Machine Learning

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

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Résumé

The requirements on explainability imposed by European laws and their implications for machine learning (ML) models are not always clear. In that perspective, our research analyzes explanation obligations imposed for private and public decision-making, and how they can be implemented by machine learning techniques.

langue originale	Anglais
titre	Proceedings of the 37th International Conference on Machine Learning
Editeur	MLResearch Press
Nombre de pages	2
Etat de la publication	Publié - 2020
Evénement	Thirty-seventh International Conference on Machine Learning: ICML2020 - Durée: 13 juil. 2020 → 18 juil. 2020

Série de publications

Nom	Proceedings of Machine Learning Research
Editeur	MLResearch Press
Volume	108
ISSN (Electronique)	2640-3498

Une conférence

Une conférence	Thirty-seventh International Conference on Machine Learning
période	13/07/20 → 18/07/20

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Abstract_LML_Workshop_Explainability_AI_LawAccepted author manuscript, 92,4 KB

Interpretability and Explainability in Machine Learning and their Application to Nonlinear Dimensionality Reduction
Bibal, A. (Auteur)FRENAY, B. (Promoteur), VANHOOF, W. (Président), Cleve, A. (Jury), Dumas, B. (Jury), Lee, J. A. (Jury) & Galarraga, L. (Jury), 16 nov. 2020
Student thesis: Doc types › Docteur en Sciences

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Contient cette citation

@inproceedings{4c13974296c547278b93c7c46b0cda40,

title = "Impact of Legal Requirements on Explainability in Machine Learning",

abstract = "The requirements on explainability imposed by European laws and their implications for machine learning (ML) models are not always clear. In that perspective, our research analyzes explanation obligations imposed for private and public decision-making, and how they can be implemented by machine learning techniques. ",

author = "Adrien Bibal and Micha{\"e}l Lognoul and {De Streel}, Alexandre and Beno{\^i}t Fr{\'e}nay",

year = "2020",

language = "English",

series = "Proceedings of Machine Learning Research",

publisher = "MLResearch Press",

booktitle = "Proceedings of the 37th International Conference on Machine Learning",

note = "Thirty-seventh International Conference on Machine Learning : ICML2020 ; Conference date: 13-07-2020 Through 18-07-2020",

}

Impact of Legal Requirements on Explainability in Machine Learning. / Bibal, Adrien ; Lognoul, Michaël ; De Streel, Alexandre et al.
Proceedings of the 37th International Conference on Machine Learning. MLResearch Press, 2020. (Proceedings of Machine Learning Research; Vol 108).

Résultats de recherche: Contribution dans un livre/un catalogue/un rapport/dans les actes d'une conférence › Article dans les actes d'une conférence/un colloque

TY - GEN

T1 - Impact of Legal Requirements on Explainability in Machine Learning

AU - Bibal, Adrien

AU - Lognoul, Michaël

AU - De Streel, Alexandre

AU - Frénay, Benoît

PY - 2020

Y1 - 2020

N2 - The requirements on explainability imposed by European laws and their implications for machine learning (ML) models are not always clear. In that perspective, our research analyzes explanation obligations imposed for private and public decision-making, and how they can be implemented by machine learning techniques.

AB - The requirements on explainability imposed by European laws and their implications for machine learning (ML) models are not always clear. In that perspective, our research analyzes explanation obligations imposed for private and public decision-making, and how they can be implemented by machine learning techniques.

M3 - Conference contribution

T3 - Proceedings of Machine Learning Research

BT - Proceedings of the 37th International Conference on Machine Learning

PB - MLResearch Press

T2 - Thirty-seventh International Conference on Machine Learning

Y2 - 13 July 2020 through 18 July 2020

ER -

Impact of Legal Requirements on Explainability in Machine Learning

Résumé

Série de publications

Une conférence

Accès au document

Empreinte digitale

Thèses de l'étudiant

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

Contient cette citation