Evidence Without Injustice: A New Counterfactual Test for Fair Algorithms
Abstract
The growing philosophical literature on algorithmic fairness has examined statistical criteria such as equalized odds and calibration, causal and counterfactual approaches, and the role of structural and compounding injustices. Yet an important dimension has been overlooked: whether the evidential value of an algorithmic output itself depends on structural injustice. We contrast a predictive policing algorithm, which relies on historical crime data, with a camera-based system that records ongoing offenses, where both are designed to guide police deployment. In evaluating the moral acceptability of acting on a piece of evidence, we must ask not only whether the evidence is probative in the actual world, but also whether it would remain probative in nearby worlds without the relevant injustices. The predictive policing algorithm fails this test, but the camera-based system passes it. When evidence fails the test, it is morally problematic to use it punitively, more so than evidence that passes the test.
Keywords
Cite
@article{arxiv.2510.12822,
title = {Evidence Without Injustice: A New Counterfactual Test for Fair Algorithms},
author = {Michele Loi and Marcello Di Bello and Nicolò Cangiotti},
journal= {arXiv preprint arXiv:2510.12822},
year = {2025}
}
Comments
13 pages. Disclaimer on AI use: the authors used Claude 3.7 in the early stages of this project to help in the formulation of the Counterfactual Independence Principle presented in this paper. The model provided preliminary suggestions pertaining to the principle's articulation, case studies, and the structure of the argument. Screenshots of the initial output are on file with the authors