User-Based Experiment Guidelines for Measuring Interpretability in Machine Learning

Adrien Bibal; Bruno Dumas; Benoît Frénay

User-Based Experiment Guidelines for Measuring Interpretability in Machine Learning

Adrien Bibal, Bruno Dumas, Benoît Frénay

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

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Résumé

With the advent of high-performance black-box models, interpretability is becoming a hot topic today in machine learning. While a lot of research is
done on interpretability, machine learning researchers do not have precise guidelines for setting up user-based experiments. This paper provides well-established guidelines from the human-computer interaction community.

langue originale	Anglais
titre	EGC Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence
Lieu de publication	Metz, France
Etat de la publication	Publié - 2019

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2019-Bibal-User_Experiment_Guidelines_for_Measuring_Interpretability_in_Machine_LearningVersion finale publiée, 67,5 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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@inproceedings{25b098bd11124ba9bf3344c69c646289,

title = "User-Based Experiment Guidelines for Measuring Interpretability in Machine Learning",

abstract = "With the advent of high-performance black-box models, interpretability is becoming a hot topic today in machine learning. While a lot of research isdone on interpretability, machine learning researchers do not have precise guidelines for setting up user-based experiments. This paper provides well-established guidelines from the human-computer interaction community.",

keywords = "Machine learning, Interpretability, Guidelines",

author = "Adrien Bibal and Bruno Dumas and Beno{\^i}t Fr{\'e}nay",

year = "2019",

language = "English",

booktitle = "EGC Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence",

}

User-Based Experiment Guidelines for Measuring Interpretability in Machine Learning. / Bibal, Adrien ; Dumas, Bruno ; Frénay, Benoît.
EGC Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence. Metz, France, 2019.

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 - User-Based Experiment Guidelines for Measuring Interpretability in Machine Learning

AU - Bibal, Adrien

AU - Dumas, Bruno

AU - Frénay, Benoît

PY - 2019

Y1 - 2019

N2 - With the advent of high-performance black-box models, interpretability is becoming a hot topic today in machine learning. While a lot of research isdone on interpretability, machine learning researchers do not have precise guidelines for setting up user-based experiments. This paper provides well-established guidelines from the human-computer interaction community.

AB - With the advent of high-performance black-box models, interpretability is becoming a hot topic today in machine learning. While a lot of research isdone on interpretability, machine learning researchers do not have precise guidelines for setting up user-based experiments. This paper provides well-established guidelines from the human-computer interaction community.

KW - Machine learning

KW - Interpretability

KW - Guidelines

M3 - Conference contribution

BT - EGC Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence

CY - Metz, France

ER -

User-Based Experiment Guidelines for Measuring Interpretability in Machine Learning

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Thèses de l'étudiant

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

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