Innovation always happens at the edges.

“Research papers from the AAAI User Experience of Machine Learning Symposium ~ Consumer-facing predictive systems paint a seductive picture: espresso machines that start brewing just as you think it’s a good time for coffee; office lights that dim when it’s sunny and office workers don’t need them; just in time diaper delivery. The value proposition is of a better user experience, but how will that experience actually be delivered when the systems involved regularly behave in unpredictable, often inscrutable, ways? Past machine learning systems in predictive maintenance and finance were designed by and for specialists, while recommender systems suggested, but rarely acted autonomously. Semi-autonomous machine learning-driven predictive systems are now in consumer-facing domains from smart homes to self-driving vehicles. Such systems aim to do everything from keeping plants healthy and homes safe to “nudging” people to change their behavior. However, despite all the promise of a better user experience there’s been little formal discussion about how design of such learning, adaptive, predictive systems will actually deliver. This symposium aims to bridge the worlds of user experience design, service design, HCI, HRI and AI to discuss common challenges, identify key constituencies, and compare approaches to designing such systems.”

Mike Kuniavsky a.k.a. @mikekuniavsky, Elizabeth Churchill a.k.a. @xeeliz, and Molly Wright Steenson a.k.a. @maximolly