DUE 2017 - AAAI Spring Symposium on Designing the User Experience of Machine Learning Systems

AAAI Spring Symposium: Designing the User Experience of Machine Learning Systems
March 27?29, 2017
Palo Alto, CA
Submission deadline: October 28, 2016, but we'll take late submissions through the end of November if you send us a note by October 28 that you plan on submitting.
https://mikek-parc.github.io/AAAI-UX-ML/
DESCRIPTION
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.
TOPICS
- Application- and domain-specific UX challenges vs. general UX design challenges
- Communication of machine learning to end users, explanation of predictive behavior and expectation setting
- Potential constituencies of ML UX
- Designing for a machine-learning world of multiple predictive systems
- Multi-device, multi-touchpoint behavior
- Service design and machine learning
- Design deals in material properties, what are the material properties of predictive machine learning systems?
FORMAT
The symposium will be a combination of presentations, posters, invited talks, plenary sessions, and breakouts, to maximize participant interaction. All attendees will be required to present a short (20 minute) presentation on their work or a subject of interest. We will alternate between these short presentations design explorations in small groups, and large group discussions.
CALL FOR PAPERS/PROJECTS
Prospective participants are invited to submit one or more of the following: Short position papers (2-4 pages) in PDF format. Please follow AAAI style guidelines. Your position statement should include a short description motivating your interest in the topic, and a short bio that includes a description of your current area of research or practice.
- A poster in pdf format. The poster should be printable as a 30” x 40”/A0. Initial submissions can include a draft of the poster.
- A 3m or shorter video in a common file format (AVI, MP4, etc.).
- An interactive demo. Interactive demos should be clear, interactive and focus on research and practice demonstrations illustrating an aspect of machine learning, machine intelligence and user experience. No product pitches will be accepted.
- A panel proposal. Panel proposals should include a 400-word description of the topic and potential and agreed panel members. Video and interactive demos should be accompanied by an extended abstract (1-2 pages, PDF) of up to 2000 words. Initial submission can include a draft/rough cut/storyboard of the video/interactive with a text description of its contents.
Please indicate your preferred presentation style: presentation, poster, demo. Submissions should not be anonymized.
QUESTIONS
Mike Kuniavsky
mikek parc.com
Palo Alto Research Center
3333 Coyote Hill Road
Palo Alto, CA 94304
+1 650 812 4847
ORGANIZING COMMITTEE
Mike Kuniavsky, PARC
Elizabeth Churchill, Google
Molly Wright Steenson, Carnegie Mellon University