ICML 2022oral65 citations
Causal Conceptions of Fairness and their Consequences
Hamed Nilforoshan, Johann D Gaebler, Ravi Shroff, Sharad Goel
Abstract
Recent work highlights the role of causality in designing equitable decision-making algorithms. It is not immediately clear, however, how existing causal conceptions of fairness relate to one another, or what the consequences are of using these definitions as design principles. Here, we first assemble and categorize popular causal definitions of algorithmic fairness into two broad families: (1) those that constrain the effects of decisions on counterfactual disparities; and (2) those that constrain the effects of legally protected characteristics, like race and gender, on decisions. We then show, analytically and empirically, that both families of definitions
BibTeX
@InProceedings{pmlr-v162-nilforoshan22a,
title = {Causal Conceptions of Fairness and their Consequences},
author = {Nilforoshan, Hamed and Gaebler, Johann D and Shroff, Ravi and Goel, Sharad},
booktitle = {Proceedings of the 39th International Conference on Machine Learning},
pages = {16848--16887},
year = {2022},
editor = {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan},
volume = {162},
series = {Proceedings of Machine Learning Research},
month = {17--23 Jul},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v162/nilforoshan22a/nilforoshan22a.pdf},
url = {https://proceedings.mlr.press/v162/nilforoshan22a.html},
abstract = {Recent work highlights the role of causality in designing equitable decision-making algorithms. It is not immediately clear, however, how existing causal conceptions of fairness relate to one another, or what the consequences are of using these definitions as design principles. Here, we first assemble and categorize popular causal definitions of algorithmic fairness into two broad families: (1) those that constrain the effects of decisions on counterfactual disparities; and (2) those that constrain the effects of legally protected characteristics, like race and gender, on decisions. We then show, analytically and empirically, that both families of definitions