What transparency for machine learning algorithms?

Recently, the concept of “algorithmic transparency" has become of primary importance in the public and scientific debates. In the light of the proliferation of uses of the term “transparency", we distinguish two families of fundamental uses of the concept: a descriptive family relating to intrinsic epistemic properties of programs, the first of which are intelligibility and explicability, and a prescriptive family that concerns the normative properties of their uses, the first of which are loyalty and fairness. Because one needs to understand an algorithm in order to explain it and carry out its audit, intelligibility is logically first in the philosophical study of transparency. In order to better determine the challenges of intelligibility in the public use of algorithms, we introduce a distinction between the intelligibility of the procedure and the intelligibility of outputs. Finally, we apply this distinction to the case of machine learning. © 2018 Lavoisier.