User-Based Experiment Guidelines for Measuring Interpretability in Machine Learning

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

4 Downloads (Pure)

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 originaleAnglais
titreEGC Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence
Lieu de publicationMetz
étatPublié - 2019

Empreinte digitale

Learning systems
Human computer interaction
Experiments

Citer ceci

Bibal, A., Dumas, B., & Frenay, B. (2019). User-Based Experiment Guidelines for Measuring Interpretability in Machine Learning. Dans EGC Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence Metz.
Bibal, Adrien ; Dumas, Bruno ; Frenay, Benoît. / User-Based Experiment Guidelines for Measuring Interpretability in Machine Learning. EGC Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence. Metz, 2019.
@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 Frenay",
year = "2019",
language = "English",
booktitle = "EGC Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence",

}

Bibal, A, Dumas, B & Frenay, B 2019, User-Based Experiment Guidelines for Measuring Interpretability in Machine Learning. Dans EGC Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence. Metz.

User-Based Experiment Guidelines for Measuring Interpretability in Machine Learning. / Bibal, Adrien; Dumas, Bruno; Frenay, Benoît.

EGC Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence. Metz, 2019.

Résultats de recherche: Contribution dans un livre/un catalogue/un rapport/dans les actes d'une conférenceArticle 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 - Frenay, 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

ER -

Bibal A, Dumas B, Frenay B. User-Based Experiment Guidelines for Measuring Interpretability in Machine Learning. Dans EGC Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence. Metz. 2019