Algorithmic Fairness and the Situated Dynamics of Justice. Issue 1 (20th January 2022)

• Record Type: Journal Article
• Authors: Fazelpour, Sina; Lipton, Zachary C.; Danks, David
• Is Part Of: Canadian journal of philosophy. Volume 52:Issue 1(2022)
• Page Start: 44
• Page End: 60
• Publication Date: 2022-01-20
• Subjects: Algorithmic fairness -- algorithmic bias -- politics of artificial intelligence -- distributive justice -- fairness; Philosophy -- Periodicals; 105
• DOI: 10.1017/can.2021.24
• Languages: English
• ISSNs: 0045-5091

Abstract

Abstract: Machine learning algorithms are increasingly used to shape high-stake allocations, sparking research efforts to orient algorithm design towards ideals of justice and fairness. In this research on algorithmic fairness, normative theorizing has primarily focused on identification of "ideally fair" target states. In this paper, we argue that this preoccupation with target states in abstraction from the situated dynamics of deployment is misguided. We propose a framework that takes dynamic trajectories as direct objects of moral appraisal, highlighting three respects in which such trajectories can be subject to evaluation in relation to their (i) temporal dynamics, (ii) robustness, and (iii) representation.