Effects of Sensemaking Translucence on Distributed Collaborative Analysis
By Nitesh Goyal, Susan R. Fussell. 2016
81 citations recorded by the source corpus.
Collaborative sensemaking requires that analysts share their information and insights with each other, but this process of sharing runs the risks of prematurely focusing the investigation on specific suspects. To address this tension, we propose and test an interface for collaborative crime analysis that aims to make analysts more aware of their sensemaking processes. We compare our sensemaking translucence interface to a standard interface without special sensemaking features in a controlled laboratory study. We found that the sensemaking translucence interface significantly improved clue finding and crime solving performance, but that analysts rated the interface lower on subjective measures than the standard interface. We conclude that designing for distributed sensemaking requires balancing task performance vs. user experience and real-time information sharing vs. data accuracy.
Subfields: Computer Science Applications, Computer Vision and Pattern Recognition, Epidemiology
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Science Applications
- Hanna M. Wallach
- Daniel G. Goldstein
- Jennifer Wortman Vaughan
- Jake M. Hofman
- Lihong Li
- Adith Swaminathan
- Thorsten Joachims
In Computer Vision and Pattern Recognition
Citations in the corpus
- Manipulating and Measuring Model Interpretability (2018, background): “Sensemaking research often involves designing tools to support rich interactions among people [39] or between people and computational systems.”
- ConsensUs (2018, background): “Conflict highlighting could also be useful for facilitating collective sensemaking (Goyal and Fussell 2016).”