Measuring and Modulating the Social Impact of Generative AI

The Hong Kong University of Science and Technology
Department of Computer Science and Engineering


PhD Thesis Defence


Title: "Measuring and Modulating the Social Impact of Generative AI"

By

Miss Yueqi XIE


Abstract:

Generative AI, especially Large Language Models (LLMs), has seen widespread 
adoption, bringing with it far-reaching social impacts. Measuring and 
modulating these impacts are critical steps toward socially responsible AI 
development and informed policy-making. While the AI community and the social 
science community have developed well-established instruments within their 
respective traditional domains, integrated approaches remain lacking for 
understanding and modulating the emerging social effects of generative AI. 
This thesis approaches the problem from two complementary fronts: (1) 
evaluating and improving generative AI models, and (2) analyzing human-AI 
interactions, with a focus on domains of significant societal relevance.

In the first part, we focus on identifying and mitigating critical safety 
risks associated with state-of-the-art LLMs. We conduct a systematic 
evaluation of jailbreak attacks—adversarial prompts designed to bypass 
ethical safeguards and elicit harmful outputs. Inspired by the psychological 
concept of self-reminders, we propose a simple yet effective defense 
mechanism called System-Mode Self-Reminder, which helps LLMs maintain 
alignment with their intended behavior with negligible cost. To further 
understand the safety mechanisms inherent in LLMs and provide more robust, 
multi- layered protection, we study the internal parameters of LLMs. We 
observe that unsafe prompts trigger distinctive patterns in safety-critical 
parameters. Leveraging this observation, we introduce GradSafe, a novel 
detection method that accurately and reliably identifies jailbreak attempts 
without requiring additional model fine-tuning.

In the second part, we turn to the challenge of analyzing human-AI 
interactions as a foundation for understanding how generative AI shapes 
content creation as a whole. The widespread application of generative AI in 
content creation presents notable challenges for delineating the originality 
of AI-assisted content. We raise the research question of how to quantify 
human contribution in AI-assisted content generation, moving beyond the 
binary detection of AI-generated output. We propose an information- theoretic 
framework that quantifies human contribution and demonstrate its 
effectiveness across diverse domains. Altogether, this thesis aims to 
contribute to this emerging field by identifying critical issues, developing 
measurement instruments, and proposing actionable strategies for socially 
responsible generative AI.


Date:                   Thursday, 31 July 2025

Time:                   10:00am - 12:00noon

Venue:                  Room 3494
                        Lifts 25/26

Chairman:               Prof. Ross MURCH (ECE)

Committee Members:      Dr. Qifeng CHEN (Supervisor)
                        Dr. Shuai WANG
                        Dr. Binhang YUAN
                        Dr. Jun ZHANG (ECE)
                        Prof. Haibo HU (PolyU)