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New thesis tackles algorithmic fairness limitations in ML systems

By PulseAugur Editorial · [2 sources] ·

A new thesis by Antonio Ferrara explores limitations in current algorithmic fairness paradigms. It argues that relying on deterministic point estimates for auditing and treating individuals as isolated entities are fundamental weaknesses. The work proposes statistical and structural approaches to address these issues in complex socio-technical systems. AI

IMPACT Addresses fundamental limitations in how fairness is measured and implemented in machine learning systems.

RANK_REASON The cluster contains a submitted academic paper on arXiv.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New thesis tackles algorithmic fairness limitations in ML systems

COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Antonio Ferrara ·

    Statistical and Structural Approaches to Algorithmic Fairness

    arXiv:2606.26200v1 Announce Type: cross Abstract: Modern machine learning systems have outgrown their origins as isolated predictive constructs, evolving into complex socio-technical architectures that actively mediate human opportunity. As algorithms increasingly determine acces…

  2. arXiv stat.ML TIER_1 English(EN) · Antonio Ferrara ·

    Statistical and Structural Approaches to Algorithmic Fairness

    Modern machine learning systems have outgrown their origins as isolated predictive constructs, evolving into complex socio-technical architectures that actively mediate human opportunity. As algorithms increasingly determine access to economic and social opportunities, it has bec…

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