[CS] Rosa Zhou Dissertation Defense/Jun 10, 2025

[via cs]

This is an announcement of Rosa Zhou's Dissertation Defense.
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Candidate: Rosa Zhou

Date: Tuesday, June 10, 2025

Time: 12 pm CST


Location: JCL 298

Title:  Explainability-driven AI: Improving Model Robustness and Supporting Scientific Discovery

Abstract: The development of explainable artificial intelligence (AI) has become a crucial step in enhancing model transparency, robustness, and usability. This thesis explores the explainability in the context of natural language processing, proposing novel approaches to improve model performance and support scientific discovery. We investigate whether explainable AI techniques can be leveraged to improve out-of-distribution generalization and model decision-making. By incorporating natural language explanations and rationale-based models, we aim to address challenges in model interpretability and resilience, particularly in the face of adversarial attacks and misleading inputs. Additionally, we propose algorithms that leverage large language models to generate novel and robust scientific hypotheses in the social science domain. We further propose using mechanistic interpretability to understand what models have learned, providing insights into the internal workings of these models and aiding the generation of novel scientific hypotheses. This research contributes to advancing both the theoretical understanding of AI systems and their practical application in fields requiring high levels of reliability and transparency, such as scientific research and critical decision-making.

Advisors: Chenhao Tan

Committee members: Chenhao Tan, Ari Holtzman, Xuezhi Wang