Yueqi Xie
Princeton University
yueqixie@princeton.edu
Bio
Yueqi Xie is a Postdoctoral Research Associate at Princeton University, working with Prof. Yu Xie on AI and society. She received her Ph.D. in Computer Science from HKUST, advised by Prof. Qifeng Chen and Prof. Sunghun Kim, and her B.S. in Intelligence Science and Technology from Peking University. Her research lies at the intersection of AI and social science, focusing on the social impact of AI, socially responsible AI (safety, security, and privacy), and computational social science.
Areas of Research
- Human-Computer Interaction
Measuring and Modulating the Social Impact of AI: From Models to Humans
The widespread adoption of AI worldwide has led, and will continue to lead, to profound and rapidly evolving social impacts across various domains. I envision building an interdisciplinary framework that integrates perspectives from both social science and AI to measure and modulate the social impact of AI, ensuring that AI advancements benefit society as a whole. This vision lies at the core of my research, which is built upon two complementary frontiers:
(1) Evaluating and advancing AI for social responsibility.
Ensuring the responsibility of AI models is fundamental to shaping their societal outcomes. I have extensively studied the safety, security, and fairness of prevalent AI/ML systems, including large language models (LLMs), multimodal LLMs, and federated learning systems. By identifying fairness and safety challenges and proposing practical mitigation strategies, I aim to advance AI toward greater social responsibility, establishing it as a foundation for its beneficial integration into society.
(2) Understanding human engagement with AI and its societal outcomes.
How people perceive and interact with AI critically shapes its broader consequences. To examine this, I have developed computational infrastructure, survey instruments, and experimental designs to investigate people’s knowledge, attitudes, and usage of AI across various domains, including content creation, the labor market, and education. By understanding and quantifying AI adoption and its heterogeneous outcomes, I aim to provide empirical evidence that informs policymaking and promotes broad societal benefit in the AI era.