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AI fairness research must address structural injustice via social determinants

By PulseAugur Editorial · [1 sources] ·

A new position paper argues that the field of algorithmic fairness needs to expand its focus beyond sensitive attributes to quantify structural injustice through social determinants. The authors contend that current technical approaches often overlook how contextual variables can create systemic unfairness, treating them as noise rather than signal. They propose a shift towards auditing structural injustice via social determinants before implementing mitigation strategies, highlighting potential new forms of injustice introduced by attribute-centric methods. AI

IMPACT This research could lead to more robust and equitable AI systems by addressing systemic biases beyond individual attributes.

RANK_REASON The cluster contains an academic paper discussing a novel approach to algorithmic fairness. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Zeyu Tang, Alex John London, Atoosa Kasirzadeh, Sarah Stewart de Ramirez, Peter Spirtes, Kun Zhang, Sanmi Koyejo ·

    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 determi…

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