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Comment limiter les biais des algorithmes ?

Charles-Albert With Driss Lehalle Marie Lamrani David Brière Winston Bounie Patrice Bertail 1 Stéphan Clémençon 2 Patrick Waelbroeck 3 David Bounie 4, 5, 6
1 MODAL'X - Modélisation aléatoire de Paris X
2 LTCI - Laboratoire Traitement et Communication de l'Information
3 Télécom ParisTech
4 IP Paris - Institut Polytechnique de Paris
5 ECOGE - Economie Gestion
I3, une unité mixte de recherche CNRS (UMR 9217) - Institut interdisciplinaire de l’innovation
6 SES - Département Sciences Economiques et Sociales
Abstract : 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.
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https://hal.telecom-paris.fr/hal-02878302
Contributor : David Bounie Connect in order to contact the contributor
Submitted on : Wednesday, July 8, 2020 - 5:00:19 PM
Last modification on : Wednesday, November 17, 2021 - 12:33:39 PM
Long-term archiving on: : Friday, November 27, 2020 - 10:28:56 AM

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  • HAL Id : hal-02878302, version 1

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INSTITUT-TELECOM | X | MODALX | ENSMP | SES | ECOGE | I3-TPT | LTCI | CNRS | IP_PARIS | PSL | UNIV-PARIS-LUMIERES | IDS | S2A | UNIV-PARIS-NANTERRE

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

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