Comment limiter les biais des algorithmes ?

Charles-Albert With , Driss Lehalle , Marie Lamrani , David Brière , Winston Bounie , Patrice Bertail (1) , Stéphan Clémençon (2, 3, 4) , Patrick Waelbroeck (5) , David Bounie (6, 7, 8)

1 MODAL'X - Modélisation aléatoire de Paris X
2 LTCI - Laboratoire Traitement et Communication de l'Information
3 S2A - Signal, Statistique et Apprentissage
4 IDS - Département Images, Données, Signal
5 Télécom ParisTech
6 IP Paris - Institut Polytechnique de Paris
7 ECOGE - Economie Gestion
8 SES - Département Sciences Economiques et Sociales

Although decision-support algorithms are now part of everyday life and are used in all sectors, especially finance, they are still criticized for their lack of transparency and their biased results. Researchers have studied this issue and make recommendations to reverse the situation.

Domains: Quantitative Finance [q-fin]

Charles-Albert With, Driss Lehalle, Marie Lamrani, David Brière, Winston Bounie, et al.. Comment limiter les biais des algorithmes ?. Les cahiers Louis Bachelier, 2020, The challenges of new technologies in finance, 36. ⟨hal-02878302⟩