From reality to world. A critical perspective on AI fairness

Jean-Marie John-Mathews (), Dominique Cardon () and Christine Balagué ()
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Jean-Marie John-Mathews: IMT-BS - MMS - Département Management, Marketing et Stratégie - TEM - Télécom Ecole de Management - IMT - Institut Mines-Télécom [Paris] - IMT-BS - Institut Mines-Télécom Business School - IMT - Institut Mines-Télécom [Paris], LITEM - Laboratoire en Innovation, Technologies, Economie et Management (EA 7363) - UEVE - Université d'Évry-Val-d'Essonne - Université Paris-Saclay - IMT-BS - Institut Mines-Télécom Business School - IMT - Institut Mines-Télécom [Paris], Sciences Po - Sciences Po
Dominique Cardon: Sciences Po - Sciences Po, médialab - médialab (Sciences Po) - Sciences Po - Sciences Po
Christine Balagué: CONNECT - Consommateur Connecté dans la Société Numérique - IMT-BS - Institut Mines-Télécom Business School - IMT - Institut Mines-Télécom [Paris], LITEM - Laboratoire en Innovation, Technologies, Economie et Management (EA 7363) - UEVE - Université d'Évry-Val-d'Essonne - Université Paris-Saclay - IMT-BS - Institut Mines-Télécom Business School - IMT - Institut Mines-Télécom [Paris], IMT-BS - MMS - Département Management, Marketing et Stratégie - TEM - Télécom Ecole de Management - IMT - Institut Mines-Télécom [Paris] - IMT-BS - Institut Mines-Télécom Business School - IMT - Institut Mines-Télécom [Paris]

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Abstract: Fairness of Artificial Intelligence (AI) decisions has become a big challenge for governments, companies, and societies. We offer a theoretical contribution to consider AI ethics outside of high-level and top-down approaches, based on the distinction between "reality" and "world" from Luc Boltanski. To do so, we provide a new perspective on the debate on AI fairness and show that criticism of ML unfairness is "realist", in other words, grounded in an already instituted reality based on demographic categories produced by institutions. Second, we show that the limits of "realist" fairness corrections lead to the elaboration of "radical responses" to fairness, that is, responses that radically change the format of data. Third, we show that fairness correction is shifting to a "domination regime" that absorbs criticism, and we provide some theoretical and practical avenues for further development in AI ethics. Using an ad hoc critical space stabilized by reality tests alongside the algorithm, we build a shared responsibility model which is compatible with the radical response to fairness issues. Finally, this paper shows the fundamental contribution of pragmatic sociology theories, insofar as they afford a social and political perspective on AI ethics by giving an active role to material actors such as database formats on ethical debates. In a context where data are increasingly numerous, granular, and behavioral, it is essential to renew our conception of AI ethics on algorithms in order to establish new models of responsibility for companies that take into account changes in the computing paradigm.

Keywords: Fairness; Machine learning; Pragmatic sociology; Big data; Business ethics; Artificial intelligence; Responsibility model; Sustainable Development Goals (search for similar items in EconPapers)
Date: 2022-07
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Citations: View citations in EconPapers (3)

Published in Journal of Business Ethics, 2022, Special Issue on Business Ethics in the Era of Artificial Intelligence, 178 (4), pp.945-959. ⟨10.1007/s10551-022-05055-8⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03549730

DOI: 10.1007/s10551-022-05055-8

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