Statistical and Structural Approaches to Algorithmic Fairness
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Computer Science > Machine Learning
Title:Statistical and Structural Approaches to Algorithmic Fairness
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 access to economic and social opportunities, it has become widely recognized that these systems are deeply embedded with the structural inequalities and prejudices of their environments. The field of algorithmic fairness emerged in response to the growing recognition that models optimized for predictive accuracy can systematically disadvantage marginalized groups. Early mitigation strategies, however, rested on fragile simplifications that limited their effectiveness in complex socio-technical environments. This thesis identifies and addresses two fundamental limitations of contemporary fairness paradigms: the reliance on deterministic point estimates for auditing and the treatment of individuals as isolated entities devoid of structural context.
| Comments: | Doctoral thesis |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML) |
| Cite as: | arXiv:2606.26200 [cs.LG] |
| (or arXiv:2606.26200v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.26200
arXiv-issued DOI via DataCite (pending registration)
|
Submission history
From: Antonio Ferrara [view email][v1] Wed, 24 Jun 2026 16:23:40 UTC (24,923 KB)
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