Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants
arXiv:2508.08337v3 Announce Type: replace-cross 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 techn
The increasing deployment of AI in critical societal functions is exposing limitations in current fairness frameworks, prompting a re-evaluation of how algorithmic injustice is defined and measured.
This shift in perspective forces a deeper, more systemic understanding of AI's societal impact, moving beyond individual bias to structural inequities exacerbated by algorithms.
The focus of algorithmic fairness research and regulation will likely broaden from specific 'sensitive attributes' to the more complex interplay of social determinants.
- · Social scientists
- · Ethical AI researchers
- · Regulatory bodies focused on systemic inequality
- · AI developers with narrow fairness metrics
- · Firms deploying AI without considering broad societal context
- · Purely technical 'bias mitigation' solutions
Algorithmic fairness research will incorporate more socio-economic and public health frameworks.
New metrics and auditing standards for AI systems will emerge, specifically targeting structural injustice.
Legislation may mandate 'social impact assessments' for AI, requiring developers to deeply analyze contextual variables.
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Read at arXiv cs.LG