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Krzysztof Gajos is a Gordon McKay professor of Computer Science at the Harvard Paulson School of Engineering and Applied Sciences. Krzysztof’s current interests include 1. Principles and applications of intelligent interactive systems; 2. Tools and methods for behavioral research at scale (e.g., LabintheWild.org); and 3. Design for equity and social justice. He has also made contributions in the areas of accessible computing, creativity support tools, social computing, and health informatics.
Krzysztof received his Ph.D. from the University of Washington and his M.Eng. and B.Sc. degrees from MIT. He was a postdoctoral researcher at Microsoft Research at the Adaptive Systems and Interaction group. From 2013 to 2016 Krzysztof was a coeditor-in-chief of the ACM Transactions on Interactive Intelligent Systems (ACM TiiS), he was the general chair of ACM UIST 2017, and he was a program co-chair of the 2022 ACM Conference on Intelligent User Interfaces. His work was recognized with a Sloan Fellowship and with best paper awards at ACM CHI, ACM COMPASS, and ACM IUI. In 2019, he received the Most Impactful Paper Award at ACM IUI for his work on automatically generating personalized user interfaces.
Talk: The State of Design Knowledge in Human-AI Interaction
Abstract: AI-powered applications are exciting because of their potential to support people in unprecedented ways but they are also particularly challenging to design right. While some specialized design knowledge related to Human-AI Interaction already exists, the production of this knowledge is not keeping up with the pace at which new AI-powered applications are invented. Consequently, without much fanfare or deliberation (or recognition of the fact!), some critical knowledge gaps are getting filled with reasonable-sounding but unverified assumptions. I will present a series of experiments (related to predictive text entry and AI-supported decision making) demonstrating that several of the key assumptions, upon which a lot of research projects and products rest, are wrong. I will then describe recent projects that build on corrected knowledge foundations and share some early promising results. I conclude with two calls to action for our field. First, we need to engage in critical technical practice, i.e., explicitly name, assess and correct (if necessary) the hidden assumptions of our field. Second, with Human-AI Interaction being a relatively new field but one that many people depend on, we need a greater investment in systematic production, synthesis and dissemination of reliable design knowledge for Human-AI Interaction.