Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants
Zeyu Tang, Alex John London, Atoosa Kasirzadeh, Sarah de Ramirez, Peter Spirtes, Kun Zhang, Sanmi Koyejo
Abstract
Algorithmic fairness research has largely framed _unfairness as discrimination_ along _sensitive attributes_. However, this approach limits visibility into _unfairness as structural injustice_ instantiated through _social determinants_, which are contextual variables that shape attributes and outcomes without pertaining to specific individuals. **This position paper argues that the field should quantify structural injustice via social determinants, beyond sensitive attributes.** Drawing on cross-disciplinary insights, we argue that prevailing technical paradigms fail to adequately capture unfairness as structural injustice, because contexts are potentially treated as noise to be normalized rather than signal to be audited. We further demonstrate the practical urgency of this shift through a theoretical model of college admissions, a demographic study using U.S. census data, and a high-stakes domain application regarding breast cancer screening within an integrated U.S. healthcare system. Our results indicate that mitigation strategies centered solely on sensitive attributes can introduce new forms of structural injustice. We contend that auditing structural injustice through social determinants must precede mitigation, and call for new technical developments that move beyond sensitive-attribute-centered notions of fairness as non-discrimination.
BibTeX
@inproceedings{icml2026_positionbeyondse,
title = {Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants},
author = {Zeyu Tang and Alex John London and Atoosa Kasirzadeh and Sarah de Ramirez and Peter Spirtes and Kun Zhang and Sanmi Koyejo},
booktitle = {ICML 2026},
year = {2026}
}