Designing for uncertainty in HCI: When does uncertainty help?

Miriam Greis, Michael Correll, Orit Shaer, Jessica Hullman, Matthew Kay

Research output: Chapter in Book/Report/Conference proceedingConference contribution

15 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationCHI 2017 Extended Abstracts - Proceedings of the 2017 ACM SIGCHI Conference on Human Factors in Computing Systems
Subtitle of host publicationExplore, Innovate, Inspire
PublisherAssociation for Computing Machinery
Number of pages8
ISBN (Electronic)9781450346566
StatePublished - May 6 2017
Event2017 ACM SIGCHI Conference on Human Factors in Computing Systems, CHI EA 2017 - Denver, United States
Duration: May 6 2017May 11 2017

Publication series

NameConference on Human Factors in Computing Systems - Proceedings
VolumePart F127655


Other2017 ACM SIGCHI Conference on Human Factors in Computing Systems, CHI EA 2017
Country/TerritoryUnited States


  • Data modeling & analysis
  • Design
  • Multidisciplinary
  • Uncertainty

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Graphics and Computer-Aided Design


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