Fairness, in machine learning research, is often conceived as an exercise in constrained optimization, based on a predefined fairness metric . While many cases of problematic systems appear in popular literature, e.g., , only a small number of studies of deployed systems exist, e.g., . 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 fairness 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. Our work so far in this area is ongoing and comprises both studies of user aware fairness in recommendation  and evaluating social choice mechanisms .
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