2026-09-23T18:35:13Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/132559 2026-02-20 col_2142_5131 col_2142_10761 com_2142_5130 com_2142_10755 com_2142_234

application/pdf en text Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2026-02-19 without embargo terms The student, Ke Yang, accepted the attached license on 2025-12-02 at 20:58. The student, Ke Yang, submitted this Thesis for approval on 2025-12-03 at 09:48. This Thesis was approved for publication on 2025-12-03 at 14:34. DSpace SAF Submission Ingestion Package generated from Vireo submission #23031 on 2026-02-19 at 18:26:20 Malice, inequality, instability, or ignorance? Disentangling the mechanisms of LLM unfairness Yang, Ke 2025-12-03 Zhai, ChengXiang large language model unfairness measurement eng Ensuring fairness in large language models (LLMs) is critical as these models are increasingly deployed in sensitive domains. Traditional fairness metrics typically report a single scalar score, which conflates distinct sources of model failure and obscures underlying biases. In this work, we propose a Hierarchical Bias-Variance Decomposition framework—termed BDSU—that decomposes total discrimination risk into four interpretable components: Bias (systematic global error), Disparity (group-level variance), Sensitivity (context-level variance), and Uncertainty (stochastic or token-level variance). By applying the law of total variance recursively, BDSU provides a principled method to quantify and separate these failure modes, aligning each with ethical and reliability priorities. We further introduce a conditional micro-diagnosis to evaluate fairness at the group level, enabling fine-grained auditing and targeted interventions. Our theoretical framework lays the foundation for more transparent, actionable, and robust evaluation of LLM fairness, highlighting the distinct mechanisms by which models may perpetuate bias or exhibit instability. 2025-12 Thesis https://hdl.handle.net/2142/132559 Copyright © 2025 Ke Yang. All rights reserved. Siebel School Comp & Data Sci Computer Science University of Illinois Urbana-Champaign M.S. Thesis