Machine Learning and Friends Lunch: Sherry Wu, Making AI Systems Work for Imperfect Humans
Content
Speaker
Sherry Wu (Carnegie Mellon University)
Abstract
General-purpose AI systems are increasingly envisioned to support users on any tasks. However, a prerequisite for this vision is that the user need is clearly “communicated” to the AI, which is in itself a nontrivial step: users often begin with vague or unformed goals, and even when they have a clear idea of what they want, their instructions may be ambiguous or misaligned with how the AI interprets them. Simply put, humans are not perfect oracles of their own intentions. How can we design AI systems that better support imperfect users? In this talk, I will share some of our recent work aimed at making AI more practically useful. This involves reflections on the right representations and metrics to capture user needs and task utility, and methods for improving the goal capturing, either by training the model to better guess the user need, or training the human to better express themselves.
Speaker Bio
Sherry Wu is an Assistant Professor in the Human-Computer Interaction Institute at Carnegie Mellon University. Her research lies at the intersection of Human-Computer Interaction and Natural Language Processing, and primarily focuses on how humans (AI experts, lay users, domain experts) can practically interact with (debug, audit, and collaborate with) AI systems. To this end, she has worked on assessing NLP model capabilities, supporting human-in-the-loop NLP model debugging and correction, as well as facilitating human-AI collaboration. She has authored award-winning papers in top-tier NLP and HCI conferences and journals such as ACL, CHI, TOCHI, etc. You can find out more about her here.