arXiv:2605.28251v1 Announce Type: cross Abstract: We consider the problem of learning a counterfactually fair regressor. We adopt a causal uncertainty view in which counterfactual fairness is defined with resampled noise. We focus on obtaining theoretical fairness guarantees for a new post-processing estimator. We begin by showing that counterfactual fairness is equivalent to satisfying demographic parity conditional on the latent variable. This allows us to provide a closed-form expression of the optimal fair regressor via a barycentric quantile map. In order to handle continuous latent varia
This paper represents theoretical research in AI fairness, a continuing area of academic focus as AI systems become more prevalent.
While theoretical, work on fair AI algorithms is crucial for the long-term societal acceptance and ethical deployment of AI.
This specific paper introduces a new theoretical approach to counterfactual fairness in regression, offering a potential advancement in algorithm design.
- · AI ethicists
- · Academic researchers
- · AI developers
Improved theoretical understanding of AI fairness principles.
Potential for the development of more robust and unbiased AI models in the future.
Increased public trust in AI systems due to demonstrable fairness at a foundational level.
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