A Harm-Reduction Framework for Algorithmic Fairness
Name
algorithmicharm.pdf
Description
Accepted version
Size
604.04 KB
Format
Adobe PDF
Checksum (MD5)
039d09cd8ee5c75233fa438cd56b68ad
Author(s)
Altman, Micah
Wood, Alexandra
Vayena, Effy
Date Issued
May 2018
Journal
IEEE Security and Privacy
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Altman, Micah et al. "A Harm-Reduction Framework for Algorithmic Fairness." IEEE Security and Privacy 16, 3 (May 2018) © 2018 IEEE
Version
Author's final manuscript
Abstract
In this article, we recognize the profound effects that algorithmic decision making can have on people's lives and propose a harm-reduction framework for algorithmic fairness. We argue that any evaluation of algorithmic fairness must take into account the foreseeable effects that algorithmic design, implementation, and use have on the well-being of individuals. We further demonstrate how counterfactual frameworks for causal inference developed in statistics and computer science can be used as the basis for defining and estimating the foreseeable effects of algorithmic decisions. Finally, we argue that certain patterns of foreseeable harms are unfair. An algorithmic decision is unfair if it imposes predictable harms on sets of individuals that are unconscionably disproportionate to the benefits these same decisions produce elsewhere. Also, an algorithmic decision is unfair when it is regressive, that is, when members of disadvantaged groups pay a higher cost for the social benefits of that decision.
MIT Department
Massachusetts Institute of Technology. Libraries
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/msp.2018.2701149
