User-Based Experiment Guidelines for Measuring Interpretability in Machine Learning

Research output: Contribution in Book/Catalog/Report/Conference proceedingConference contribution

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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 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.
Original languageEnglish
Title of host publicationEGC Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence
Place of PublicationMetz
Publication statusPublished - 2019

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Learning systems
Human computer interaction
Experiments

Keywords

  • Machine learning
  • Interpretability
  • Guidelines

Cite this

Bibal, A., Dumas, B., & Frenay, B. (2019). User-Based Experiment Guidelines for Measuring Interpretability in Machine Learning. In 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.
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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. in 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.

Research output: Contribution in Book/Catalog/Report/Conference proceedingConference contribution

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