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: Université Paris-Saclay, Univ Evry, IMT-BS, LITEM
Dominique Cardon: Sciences Po
Christine Balagué: Université Paris-Saclay, Univ Evry, IMT-BS, LITEM

Journal of Business Ethics, 2022, vol. 178, issue 4, No 5, 945-959

Abstract: 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 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (7)

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DOI: 10.1007/s10551-022-05055-8

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