User-Based Experiment Guidelines for Measuring Interpretability in Machine Learning

Adrien Bibal; Bruno Dumas; Benoît Frenay

User-Based Experiment Guidelines for Measuring Interpretability in Machine Learning

Adrien Bibal, Bruno Dumas, Benoît Frenay

Research output: Contribution in Book/Catalog/Report/Conference proceeding › Conference 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 language	English
Title of host publication	EGC Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence
Place of Publication	Metz
Publication status	Published - 2019

Keywords

Machine learning
Interpretability
Guidelines

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2019-Bibal-User_Experiment_Guidelines_for_Measuring_Interpretability_in_Machine_LearningFinal published version, 67.5 KB

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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

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