User-Based Experiment Guidelines for Measuring Interpretability in Machine Learning

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

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  • Contient cette citation

    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