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Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants

arXiv 2508.08337 ↗First posted 10 Aug 2025cs.CY cs.AI cs.LGcited by 0

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Abstract (arXiv)

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.

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References · 44

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32 more
  1. 2017Avoiding Discrimination through Causal Reasoningcited by 194
  2. 2018Path-Specific Counterfactual Fairnesscited by 181
  3. 2019Fair Regression: Quantitative Definitions and Reduction-based Algorithmscited by 168
  4. 2017Fair Inference On Outcomescited by 144
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  25. 2022Algorithmic Fairness and Structural Injustice: Insights from Feminist Political Philosophycited by 15
  26. 2022Domain Adaptation meets Individual Fairness. And they get alongcited by 13
  27. 2023Generalized Independent Noise Condition for Estimating Causal Structure with Latent Variablescited by 11
  28. 2019Optimal Training of Fair Predictive Modelscited by 10
  29. 2022Learning Relational Causal Models with Cycles through Relational Acyclificationcited by 6
  30. 2024Beyond Model Interpretability: Socio-Structural Explanations in Machine Learningcited by 6
  31. 2023Procedural Fairness Through Decoupling Objectionable Data Generating Componentscited by 3
  32. 2024Explanation Hacking: The perils of algorithmic recoursecited by 3

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