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Comment limiter les biais des algorithmes ?

Author

Listed:
  • Charles-Albert With
  • Driss Lehalle
  • Marie Lamrani
  • David Brière
  • Winston Bounie
  • Patrice Bertail

    (MODAL'X - Modélisation aléatoire de Paris X - UPN - Université Paris Nanterre - CNRS - Centre National de la Recherche Scientifique)

  • Stéphan Clémençon

    (LTCI - Laboratoire Traitement et Communication de l'Information - IMT - Institut Mines-Télécom [Paris] - Télécom Paris, S2A - Signal, Statistique et Apprentissage - LTCI - Laboratoire Traitement et Communication de l'Information - IMT - Institut Mines-Télécom [Paris] - Télécom Paris, IDS - Département Images, Données, Signal - Télécom ParisTech)

  • Patrick Waelbroeck

    (Télécom ParisTech)

  • David Bounie

    (IP Paris - Institut Polytechnique de Paris, ECOGE - Economie Gestion - I3 SES - Institut interdisciplinaire de l’innovation de Telecom Paris - Télécom ParisTech - I3 - Institut interdisciplinaire de l’innovation - CNRS - Centre National de la Recherche Scientifique, SES - Département Sciences Economiques et Sociales - Télécom ParisTech)

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.

Suggested Citation

  • Charles-Albert With & Driss Lehalle & Marie Lamrani & David Brière & Winston Bounie & Patrice Bertail & Stéphan Clémençon & Patrick Waelbroeck & David Bounie, 2020. "Comment limiter les biais des algorithmes ?," Post-Print hal-02878302, HAL.
    • Handle: RePEc:hal:journl:hal-02878302
    Note: View the original document on HAL open archive server: https://telecom-paris.hal.science/hal-02878302
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