Deep learning and other black-box models are becoming more and more popular today. Despite their high performance, they may not be accepted ethically or legally because of their lack of explainability. This paper presents the increasing number of legal requirements on machine learning model interpretability and explainability in the context of private and public decision making. It then explains how those legal requirements can be implemented into machine-learning models and concludes with a call for more inter-disciplinary research on explainability.
Interpretability and Explainability in Machine Learning and their Application to Nonlinear Dimensionality ReductionAuthor: Bibal, A., 16 nov. 2020
Thèse de l'étudiant: Doc types › Docteur en Sciences