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PhD Thesis Proposal - Tejas Srinivasan

Title of Thesis Proposal: Facilitating Reliable Use of AI Systems Under Uncertainty
 
Date and Time: April 14, 2025, 4--5pm
 
Location: GCS 402C
 
Committee Members: Jesse Thomason (Chair), Robin Jia, Heather Culbertson, Morteza Dehghani, Diyi Yang
 
Abstract: AI systems are increasingly assisting humans with decision-making tasks. Effective human-AI collaboration requires AI assistants to be reliable by not only being accurate but also knowing when they don’t know and acting appropriately when uncertain. Popular strategies for handling uncertainty include abstaining from answering, providing prediction sets using conformal prediction, communicating uncertaintyto users, and asking clarification questions to resolve uncertainty. However, these mechanisms do not always facilitate appropriate reliance on and utilization of AI systems by users. In this thesis, we explore methods for proactively mitigating under- and over-reliance in human-AI collaboration under uncertainty. In selective prediction, always abstaining when uncertain can lead to under-utilization by the user, so we develop an algorithm to reduce over-abstention in multimodal selective prediction systems without increasing the error rate of the system’s predictions. When communicating uncertainty, we find that user trust can bias how users rely on AI confidence estimates and lead to inappropriate reliance, which we mitigate by adapting AI assistants’ behavior to user trust levels. Finally, we propose reducing over-reliance on LLM agents by modeling and proactively resolving uncertainty about user goals through frictive dialogue. Our works highlight the importance of modeling uncertainty about AI predictions and the user-AI interaction itself, and the benefits of responding to uncertainty through AI introspection and adaptive AI behaviors

Location: Ginsburg Hall (GCS) - 402C

Audiences: Everyone Is Invited

Contact: Tejas Srinivasan

This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor.

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