User-Based Experiment Guidelines for Measuring Interpretability in Machine Learning

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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

Keywords

  • Machine learning
  • Interpretability
  • Guidelines

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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