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A Note on the Interpretability of Machine Learning Algorithms

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Abstract

We are interested in the analysis of the concept of interpretability associated with a ML algorithm. We distinguish between the "How", i.e., how a black box or a very complex algorithm works, and the "Why", i.e. why an algorithm produces such a result. These questions appeal to many actors, users, professions, regulators among others. Using a formal standardized framework, we indicate the solutions that exist by specifying which elements of the supply chain are impacted when we provide answers to the previous questions. This presentation, by standardizing the notations, allows to compare the different approaches and to highlight the specificities of each of them: both their objective and their process. The study is not exhaustive and the subject is far from being closed

Suggested Citation

  • Dominique Guégan, 2020. "A Note on the Interpretability of Machine Learning Algorithms," Documents de travail du Centre d'Economie de la Sorbonne 20012, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    • Handle: RePEc:mse:cesdoc:20012
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    References listed on IDEAS

    as
    1. Alexis Bogroff & Dominique Guegan, 2019. "Artificial Intelligence, Data, Ethics: An Holistic Approach for Risks and Regulation," Post-Print halshs-02181597, HAL.
    2. Igor Linkov & Benjamin D. Trump & Kelsey Poinsatte-Jones & Marie-Valentine Florin, 2018. "Governance Strategies for a Sustainable Digital World," Sustainability, MDPI, vol. 10(2), pages 1-8, February.
    3. Alexis Bogroff & Dominique Guégan, 2019. "Artificial Intelligence, Data, Ethics: An Holistic Approach for Risks and Regulation," Documents de travail du Centre d'Economie de la Sorbonne 19012, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    4. Alexis Bogroff & Dominique Guégan, 2019. "Artificial Intelligence, Data, Ethics. An Holistic Approach for Risks and Regulation," Working Papers 2019: 19, Department of Economics, University of Venice "Ca' Foscari".
    5. Alexis Bogroff & Dominique Guegan, 2019. "Artificial Intelligence, Data, Ethics: An Holistic Approach for Risks and Regulation," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-02181597, HAL.
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    More about this item

    Keywords

    Agnostic models; Artificial Intelligence; Counterfactual approach; Interpretability; LIME method; Machine learning;
    All these keywords.

    JEL classification:

    • C - Mathematical and Quantitative Methods
    • K - Law and Economics

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