Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3027063.3027091 | CHI Extended Abstracts |
Field | DocType | Citations |
Multidisciplinary approach,Information visualization,Computer science,Visualization,Uncertain data,Human–computer interaction,Personal health | Conference | 3 |
PageRank | References | Authors |
0.38 | 14 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Miriam Greis | 1 | 38 | 4.21 |
| Jessica Hullman | 2 | 477 | 26.51 |
| Michael Correll | 3 | 211 | 15.40 |
| Matthew Kay | 4 | 451 | 30.42 |
| Orit Shaer | 5 | 1019 | 90.56 |