Details

Autor(en) / Beteiligte

• Guegan, Dominique

Titel

A Note on the Interpretability of Machine Learning Algorithms

Ist Teil von

• Documents de travail du Centre d'Économie de la Sorbonne, 2020

Ort / Verlag

Centre d'Économie de la Sorbonne (CES - UMR8174)

Erscheinungsjahr

2020

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• 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.