A Note on the Interpretability of Machine Learning Algorithms

Dominique Guegan ()
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Dominique Guegan: UP1 - Université Paris 1 Panthéon-Sorbonne, CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, University of Ca’ Foscari [Venice, Italy]

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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.

Keywords: Interpretability; Counterfactual approach; Artificial Intelligence; Agnostic models; LIME method; Machine learning (search for similar items in EconPapers)
Date: 2020-07
New Economics Papers: this item is included in nep-big and nep-cmp
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Published in 2020

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