Year: 2025
Type: conference-paper
Abstract: My thesis develops methods for safe and explainable machine learning in high-impact domains, with a focus on fairness. I first addressed bias mitigation through multi-task learning and uncertainty estimation, balancing fairness and accuracy using Pareto optimization. I then developed a framework using large language models to improve causal discovery of bias pathways. My ongoing work focuses on translating high-level fairness policies into causal model constraints, enabling automated enforcement of legal fairness requirements in machine learning pipelines. This interdisciplinary approach bridges technical fairness methods with policy-aligned model design.
Author: Khadija Zanna
Institution: Rice University
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
Open Access status: bronze
OpenAlex ID: W4415230886
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