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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 Ainsi Qu'un Entretien Avec 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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Journal articles
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https://hal.telecom-paris.fr/hal-02878302
Contributor : David Bounie <>
Submitted on : Wednesday, July 8, 2020 - 5:00:19 PM
Last modification on : Thursday, December 10, 2020 - 4:49:11 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 | UNIV-PARIS10 | SES | ECOGE | I3-TPT | LTCI | CNRS | IP_PARIS | PSL | UNIV-PARIS-LUMIERES | IDS | S2A

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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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