Canadian Journal of Philosophy:1-17 (forthcoming)

Authors
Sina Fazelpour
Northeastern University
David Danks
University of California, San Diego
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 temporal dynamics, robustness, and representation.
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DOI 10.1017/can.2021.24
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References found in this work BETA

Ideal Vs. Non‐Ideal Theory: A Conceptual Map.Laura Valentini - 2012 - Philosophy Compass 7 (9):654-664.
The Epistemology of Democracy.Elizabeth Anderson - 2006 - Episteme 3 (1-2):8-22.
Some Varieties of Robustness.James Woodward - 2006 - Journal of Economic Methodology 13 (2):219-240.

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