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.
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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} }