Statistical and Structural Approaches to Algorithmic Fairness
For researchers and practitioners in algorithmic fairness, this work critiques current paradigms and suggests directions for improvement, but it is conceptual rather than empirical.
The thesis identifies two limitations in algorithmic fairness—reliance on deterministic point estimates and treating individuals as isolated entities—and proposes new approaches to address them, though no concrete results or numbers are provided.
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.