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                <term xml:lang="en">algorithms</term>
                <term xml:lang="en">fairness</term>
                <term xml:lang="en">learning</term>
                <term xml:lang="en">employment</term>
                <term xml:lang="en">disparate impact</term>
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                <term xml:lang="fr">apprentissage</term>
                <term xml:lang="fr">algorithme</term>
                <term xml:lang="fr">impact disproportionné</term>
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              <p>This paper examines the issue of statistical proof of discrimination at the junction between the social sciences and artificial intelligence. It deals with situations of indirect discrimination in connection with employment, known as disparate impact, which is at the heart of AI research aimed at designing non-discriminatory algorithms. It is possible to find in these studies statistical considerations relating to the definition and measurement of fairness, which are useful for reconsidering the issue of employment discrimination. However, the concept of disparate impact is a legal concept whose history, judicial interpretations and contextual components must be understood. Finally, recent interdisciplinary research, conducted in the United States and Europe, shows the way to structuring the issue of fair algorithms through the social values conveyed by case law on the application of anti-discrimination law. This paves the way for legally sound and non-discriminatory algorithms.</p>
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              <p>Ce texte problématise la question de la preuve statistique de la discrimination à l’intersection des sciences sociales et de l’intelligence artificielle. Il traite des situations de discrimination indirecte liée à l’emploi, dénommée « disparate impact » (ou impact disproportionné) et qui est au cœur de la recherche en IA visant à concevoir des algorithmes non discriminatoires. Il est possible de trouver dans ces travaux des considérations statistiques, relatives à la définition et à la mesure de la fairness, qui sont utiles pour reconsidérer la question des discriminations liées à l’emploi. Toutefois, le concept de disparate impact est un concept juridique dont il importe de comprendre l’histoire, les interprétations judiciaires, donc les composantes contextuelles. Enfin, des recherches interdisciplinaires récentes, menées aux États-Unis et en Europe, montrent la voie à une structuration de la problématique d’algorithmes équitables par les valeurs sociales portées par la jurisprudence sur l’application du droit anti-discrimination. Cela ouvre la voie à des algorithmes juridiquement fondés et non discriminatoires.</p>
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