Automation or Augmentation? Optimizing Human-AI Collaboration
Abstract:
Recent advances in large language models (LLMs) have revolutionized human-AI interaction, but their success depends on addressing key challenges like privacy and effective collaboration. In this talk, we first share how language agents can help empower humans to learn diverse social skills such as listening skills and conflict resolution to demonstrate the societal impact of human-AI interaction. We then present Co-Gym, a novel platform for studying human-agent collaboration. Our findings reveal that collaborative agents consistently outperform their fully autonomous counterparts in multiple human-AI interaction tasks. Finally, we audit the U.S. workforce to assess the impact of automation and augmentation on the future of work, in order to guide the development of AI agents that reflect and incorporate workers’ perspectives. Overall, this talk highlights how to develop AI systems that are trustworthy and capable of fostering meaningful collaboration with human users.
BIO:
Diyi Yang is an assistant professor in the Computer Science Department at Stanford University, also affiliated with the Stanford NLP Group, Stanford HCI Group and Stanford Human Centered AI Institute. Her research focuses on human-centered natural language processing and human-AI interaction. She is a recipient of IEEE “AI 10 to Watch” (2020), Microsoft Research Faculty Fellowship (2021), NSF CAREER Award (2022), an ONR Young Investigator Award (2023), and a Sloan Research Fellowship (2024). Her work has received multiple paper awards or nominations at top NLP and HCI conferences.