UCLA NLP Seminar Series

Speaker Bio:Liwei Jiang is a final-year PhD candidate in the Paul G. Allen School of Computer Science and Engineering at the University of Washington, where her research centers on humanistic, pluralistic, and coevolutionary AI safety and alignment. She develops computational frameworks for modeling human morality, advances culturally grounded and pluralistic value alignment, designs holistic safeguards for large language models, and explores future-oriented coevolutionary paradigms that integrate human-AI and AI-AI learning dynamics. Her work appears in top venues across AI, ML, and NLP, earning multiple Oral, Spotlight, Outstanding Paper, and Best Paper distinctions. She received her BA in Computer Science and Mathematics from Colby College, graduating summa cum laude.

Abstract:"In this talk, I will explore a central challenge for the next generation of AI systems: how to ensure that AI aligns with human morality, values, and needs in a world where those values are complex, diverse, and constantly evolving. As AI becomes deeply embedded in social, creative, and safety-critical settings, alignment must move beyond high-level principles toward concrete scientific frameworks and deployable technical methods. My work develops such a foundation and advances alignment across three complementary directions. I will begin by discussing how we can model human morality and pluralistic values in ways that are computationally grounded yet deeply informed by philosophy and cognitive science. This includes building representations of commonsense morality, cultural variation, and individual preferences, and showing how these learned structures can guide decision-making in large language models. Next, I will describe a line of research focused on putting value alignment into practice by developing holistic safety frameworks for language models. These include integrated red-teaming and defense pipelines, multilingual moderation tools, and system-level mechanisms that make LLMs steerable, controllable, and robust to evolving risks in real-world environments. Finally, I will turn to a forward-looking perspective on alignment: the idea of human–AI coevolution. I will outline how AI can augment human capabilities, how AI-to-AI interaction can drive iterative self-improvement, and how humans and AI together can form synergistic feedback loops that enable more capable, adaptive, and beneficial systems. Across these threads, the talk presents a unified vision of humanistic, pluralistic, and coevolutionary AI alignment—one that integrates moral reasoning, technical safety, and human–AI collaboration to support a future where AI systems act in ways that are meaningfully aligned with human values and ultimately contribute to human flourishing."