Counterfactual fairness formulates fair machine learning within a causal inference perspective, motivated by the intuition that defining or verifying fairness is a similar challenge to determining causality.
This work approaches fair machine learning from a causal inference perspective, arguing that determining what is fair is a similar challenge to determining causality.
Some of this work was covered in the New Scientist and 36Kr.
L. E. J. Bynum, J. R. Loftus, J. Stoyanovich, Disaggregated Interventions to Reduce Inequality. Equity and Access in Algorithms, Mechanisms, and Optimization, 2021. [link]
K. Yang, J. R. Loftus, J. Stoyanovich, Causal intersectionality and fair ranking. Foundations of Responsible Computing, 2021. [link]
M. J. Kusner, C. Russell, J. R. Loftus, R. Silva. Making Decisions that Reduce Discriminatory Impact. International Conference on Machine Learning, 2019. [link]
M. J. Kusner, J. R. Loftus, C. Russell, R. Silva. Counterfactual fairness. Advances in Neural Information Processing Systems, 2017. [link]
C. Russell, M. J. Kusner, J. R. Loftus, R. Silva. When worlds collide: integrating different counterfactual assumptions in fairness. Advances in Neural Information Processing Systems, 2017. [link]
J. R. Loftus, C. Russell, M. J. Kusner, R. Silva. Causal reasoning for algorithmic fairness. Preprint.
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For attribution, please cite this work as
Loftus (2022, Feb. 17). neurath's speedboat: Fairness and causality in machine learning. Retrieved from http://joshualoftus.com/research/fairness-and-causality-in-machine-learning/
BibTeX citation
@misc{loftus2022fairness,
author = {Loftus, Joshua},
title = {neurath's speedboat: Fairness and causality in machine learning},
url = {http://joshualoftus.com/research/fairness-and-causality-in-machine-learning/},
year = {2022}
}