About The Workshop

This workshop brings together researchers and practitioners from HCI and related fields who want to better understand when communicating uncertainty to end-users helps, versus when it may confuse or overwhelm.

End-users are often exposed to uncertain data in interactive systems such as personal health apps, intelligent navigation systems, and systems driven by machine learning. On one hand, communicating uncertainty may improve the understanding of data and predictions. On the other hand, communicating uncertainty can greatly confuse users and decrease trust. While some specialized guidelines for dealing with uncertainty exist within particular fields such as information visualization or context-aware computing, HCI lacks general design guidelines around the more basic question of "will communicating uncertainty rather help or confuse my users?" The goal of this workshop is to bring together researchers and practitioners from across HCI and related fields to establish a better understanding of when and how to design for uncertainty. The outcome of the workshop will be a set of real-world application scenarios with descriptions of the impact of presenting uncertainty in that scenario. Additionally, we will create a set of design guidelines that supports designers and researchers in this emerging space in evaluating whether and how to present uncertainty.
Keynote Speaker: Susan Joslyn

Keynote: Uncertainty Information for Everyday Users



Susan Joslyn is a Professor of Psychology at the University of Washington. Her research program concerns decision-making in real world situations. Current work focuses on decisions for which the outcome is uncertain, such as those based on weather forecasts. One of her lab's main research questions is whether people can understand numeric uncertainty estimates. An example is a forecast for 30% chance of winds greater than 20 mph. Her research has shown that people can understand this and several other kinds of uncertainty information, when it is carefully presented. Moreover such information improves decisions compared to decisions based on conventional deterministic forecasts.
Call for Papers

Prospective participants are invited to submit 2-4 page position papers in extended abstract format about their interest in uncertainty and the challenges that they experience.

Across its many subfields (e.g., personal informatics, information visualization, machine learning, big data analytics), HCI increasingly deals with uncertain predictions. The goal of this workshop is to address common challenges that occur in designing for uncertainty across HCI-related domains, such as when and how to communicate uncertainty to users without confusing them. The workshop will include a keynote and panel and group discussions, with the goal of developing a set of application scenarios demonstrating successful and unsuccessful examples of presenting uncertainty, and resulting design guidelines.

Attendees from all backgrounds dealing with uncertainty (inside and outside of HCI) are invited to submit 2-4 page position papers in extended abstract format explaining their interest in uncertainty and the challenges that they experience. Participants are encouraged to ground their positions in real application scenarios. Suggested contribution types include, but are not limited to:
  • Critical reflections about the choice of when to present or support interaction with uncertain data
  • Implementation, development, and evaluation of systems which communicate uncertainty
  • Design recommendations for interfaces handling uncertain data
  • Future scenarios for designing for uncertainty

Papers should be sent to designingforuncertainty@gmail.com before or on January 31st and will be reviewed by two organizers of the workshop on how well they fit to the topic, describe concrete scenarios, and contribute to the overall discussion. An early acceptance round will be completed by December 21st. Up to 15 papers will be accepted. At least one author of each accepted position paper must attend the workshop. All attendees must register for the workshop and at least one day of the CHI conference.
If you have any questions
contact the workshop organizers here!
Important Dates
Paper Submission
January 31, 2017* (Deadline extended!)
Acceptance Notification
February 24, 2017
Workshop
May 7, 2017
*An early acceptance round will be completed on the 21st of December for all papers submitted before or on the 16th of December. Please indicate the need for early acceptance in your submission e-mail. Papers submitted for the early acceptance round will be either accepted, rejected or deferred for a consideration at the deadline next year.
Organizers
Miriam Greis
is a Ph.D. candidate and a member of the HCI group at the University of Stuttgart. Her research mainly concentrates on how to communicate uncertain data (e.g., health and weather data) to laymen in everyday life and the handling of uncertain input in interactive systems. She is also part of the interdisciplinary research cluster Simulation Technology, where she works together with sociologists, engineers, and mathematicians. She therefore has a broad view on the topic of uncertainty and can contribute diverse aspects from different disciplines.
Jessica Hullman
is an Assistant Professor in Information and adjunct Assistant Professor in Computer Science & Engineering at University of Washington. Her research aims to develop techniques and tools that make inherent, yet difficult, aspects of data more understandable to non-analysts, often using information visualization. She has worked on topics including automated generation of visualizations to support news understanding, on-demand analogies to make unfamiliar measurements more understandable, and depiction of uncertainty as a finite set of data samples rather than a more conventional model representation (e.g., confidence interval).
Michael Correll
is a postdoctoral Research Associate at the Interactive Data Lab, Computer Science & Engineering Department at the University of Washington. His research focus is on the interplay between statistics and information visualization as sources of knowledge, as well as the rhetoric of visualization. He has conducted numerous crowdsourced experimental studies to examine how the general audience perceives uncertainty in visualizations, and has deployed uncertainty-aware visualization tools for collaborators in fields from genomics to the humanities.
Matthew Kay
is an Assistant Professor in the School of Information at the University of Michigan. His research centers on usable statistics and communicating uncertainty in everyday predictive systems. He has studied the expectations people have for data accuracy in personal informatics applications including sleep and weight tracking, and designed and tested novel ways of communicating uncertainty in predictive systems such as real-time bus arrival. He has also developed methods for assessing users' desired trade-offs in types of errors in predictive systems, allowing for machine learning systems to be tuned to users' expectations.
Orit Shaer
is an associate professor of Computer Science and Media Arts and Sciences at Wellesley College. Her research focuses on the application of tangible and embodied interaction to scientific discovery, collaborative learning, and health informatics. She is a primary investigator on NSF funded projects, which explore the role of HCI in personal genomics and in synthetic biology. She has developed and evaluated interactive tools that visualize uncertainty in domains including genomics, bio-design, and strategic planning.
Tentative Program

The tentative program consists of a keynote, panel discussions, group activities and group work.

Time Activity
9.00 - 9.05 Welcome and Introduction
9.05 - 9.50 Keynote: Susan Joslyn, Professor, University of Washington Psychology.
9.50 - 10.20 Session 1 - Talks (4 x 5 min) + Panel Discussion: Design Theory, Statistics
  • Stefan P. Carmien. Assume the Worst: Design for Failure
  • Bran Knowles. Intelligibility in the Face of Uncertainty
  • Motahhare Eslami and Karrie Karhalios. Embracing Seamfulness and Uncertainty in Designing around Hidden Algorithms
  • Amelia McNamara. Lessons from statistics for presenting uncertainty in data journalism
10.20 - 10.55 Talks (5 x 5 min) + Panel Discussion: Visualizaton, Machine Learning and Artificial Intelligence
  • Dominik Moritz and Danyel Fisher. Lessons from Pangloss: User Encounters with Uncertainty
  • Markus John, Steffen Koch, and Thomas Ertl. Uncertainty in Visual Text Analysis in the Context of the Digital Humanities
  • Nadia Boukhelifa, Marc-Emmanuel Perrin, Samuel Huron, and James Eagan. Towards Uncertainty-Aware Data Analytics for Data Workers: Promoting and Limiting Factors
  • Jennifer Wortman Vaughan and Hanna Wallach. The Inescapability of Uncertainty
  • Alison Smith, Varun Kumar, Jordan Boyd-Graber, Kevin Seppi, and Leah Findlater. Accounting for Input Uncertainty in Human-in-the-Loop Systems
10.55 - 11.25 Coffee Break
11.25 - 12.00 Talks (5 x 5 min) + Panel Discussion: Personal Informatics and Social Computing
  • Lauren Westendorf, Christina Pollalis, Clarissa Verish, Orit Shaer, Panagiotis Takis Metaxas, Madeleine Ball, and Oded Nov. From Personal Genomics to Twitter: Visualizing the Uncertainty of Evidence
  • Herbert P. Susmann, Katherine E. Boronow, and Julia G. Brody. Sense-Making Under Uncertainty: Using Expert Systems to Help Users Navigate Uncertain Implications of Chemical Exposure Data
  • Halimat Alabi and Marek Hatala. How Does This Visualization Say I’m Doing? Handling Uncertainty in Learning Analytics
  • Brian Y. Lim, Oshrat Ayalon, and Eran Toch. Reducing Communication Uncertainty with Social Intelligibility: Challenges and Opportunities
  • Di Lu, Rosta Farzan, and Claudia Lopez. The Impact of Uncertainty Reduction on Newcomers in Hybrid online-offline Communities
12.00 - 12.20 Wrap-Up of Morning Session/ Preparation for Afternoon Session
12.20 - 14.00 Lunch
14.00 - 14.30 Introduction and Group Forming
14.30 - 15.30 Group Activity - Application Scenarios
15.30 - 16.00 Coffee Break
16.00 - 16.20 Discussion on Group Activity
16.20 - 17.00 Group Activity - Deriving Design Principles from Scenarios
17.00 - 17.20 Discussion on Group Activity
17.20 - 17.30 Conclusions + Wrap-Up
20.00 (approx.) Workshop Dinner
Papers

Here you can download and read all accepted position papers for the workshop.

Contact Us

If you have any questions regarding the workshop or need more information, please do not hesistate to contact us.