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- W4286379617 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." @default.
- W4286379617 created "2022-07-22" @default.
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- W4286379617 date "2021-10-20" @default.
- W4286379617 modified "2023-09-26" @default.
- W4286379617 title "Algorithmic Fairness and the Situated Dynamics of Justice" @default.
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- W4286379617 doi "https://doi.org/10.1017/can.2021.24" @default.
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