Algorithmic Fairness, Institutional Logics, and Social Choice

Citation:

Burke R, Voida A, Mattei N, Sonboli N, Eskandanian F. Algorithmic Fairness, Institutional Logics, and Social Choice , in IJCAI 2021 Workshop on AI for Social Good. ; 2021.

Abstract:

Fairness, in machine learning research, is often conceived as an exercise in constrained optimization, based on a predefined fairness metric. We argue that this abstract model of algorithmic fairness is a poor match for the real world, in which applications are likely to be embedded within a larger context involving multiple classes of stakeholders as well as multiple social and technical systems. We may expect multiple, competing claims around fair- ness coming from various stakeholders, especially in applications oriented towards social good. We propose computational social choice as a promising framework for the integration of multiple perspectives on system outcomes in fairness-aware systems and provide an example in the application of personalized recommendation for a non-profit.