Events

Event Information:

  • Mon
    24
    Sep
    2018

    [Talk] Eytan Adar - Bounced Checks at the UI/AI Intersection

    11:30 amErhlicher Room(NQ 3100), 105 S. State St. Ann Arbor

    Eytan Adar is an Associate Professor in the School of Information & Computer Science and Engineering at the University of Michigan. He works broadly at the intersection of HCI and IR/Data Mining and ranges from empirical studies of large-scale online behaviors to building new systems, tools and methods. He completed his doctoral work in the Computer Science at the University of Washington and has Masters and Bachelors degrees from MIT. Before going back for his Ph.D., Eytan was a researcher at HP Labs and Xerox PARC for a number of years (spinning out a company called Outride somewhere in there). Eytan is co-founder of ICWSM and has served as general chair for ICWSM and WSDM. His website is at http://www.cond.org

    Overview:

    Despite significant progress, there is a persistent mismatch between the characteristics of AI-driven features and end-user expectations. In simpler times we could joke that UI's were writing a check the AI couldn't cash. AI-based sub-systems deliver wonderful services but also failure and uncertainty. The result is that intelligent systems couldn't quite behave in the way UI designers wanted—or promised—they would. In this talk, I'll illustrate how the problem has also flipped: AI designers are now overpromising things interfaces can't deliver. Decision-theoretic constructs such as `mixed-initiative' provide broad strategies—but not tactics—for addressing this problem. While we have evolved a set of examples for masking, controlling, and explaining, as ways to manage the AI-UI bridge, a more generic design language (patterns & UI style) is elusive. As more AI-driven features become integrated into user-facing systems, the development of a modern design language is critical. I'll try to lay out the value (and challenges) of such a language. If done correctly, both end-users navigating the uncertainty/noise of AI-features and data-hungry AI subsystems will benefit.