Statistical and Structural Approaches to Algorithmic Fairness

Short summary

This arXiv paper identifies two fundamental limitations in algorithmic fairness paradigms: over-reliance on deterministic point estimates for audits and treatment of individuals without structural context. As ML systems increasingly control access to economic and social opportunities, early bias-mitigation strategies prove ineffective in complex environments. The research proposes moving beyond these fragile simplifications toward more robust fairness approaches.

• •Paper addresses two key fairness research limitations: deterministic auditing and lack of structural context
• •Modern ML systems mediate economic access, making bias mitigation critical for equity
• •Proposes moving beyond fragile simplifications to handle fairness in socio-technical systems

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