2021/08/31 by Emily Wall, Arpit Narechania, Adam Coscia +2 · 36 citations
Computer Science · Neuroscience · #Aesthetic Perception and Analysis #Cognition #Cognitive bias #Data Visualization and Analytics #Data visualization #Generalizability theory #Innovative Human-Technology Interaction #Interaction design #Interactive visualization #Summative assessment #Visualization #cs.HC
paper · pdf · doi:10.1109/tvcg.2021.3114862
published in IEEE Transactions on Visualization and Computer Graphics 28(1), 966-975 (Institute of Electrical and Electronics Engineers) · 10 pages, 7 figures, TVCG Special Issue on the 2021 IEEE Visualization Conference (VIS)
openalex created_date 2021/08/16 · arxiv created 2021/09/22 · arxiv updated 2021/09/23 · openalex publication_date 2021/10/02 · openalex updated_date 2026/08/06
Human biases impact the way people analyze data and make decisions. Recent work has shown that some visualization designs can better support cognitive processes and mitigate cognitive biases (i.e., errors that occur due to the use of mental "shortcuts"). In this work, we explore how visualizing a user's interaction history (i.e., which data points and attributes a user has interacted with) can be used to mitigate potential biases that drive decision making by promoting conscious reflection of one's analysis process. Given an interactive scatterplot-based visualization tool, we showed interaction history in real-time while exploring data (by coloring points in the scatterplot that the user has interacted with), and in a summative format after a decision has been made (by comparing the distribution of user interactions to the underlying distribution of the data). We conducted a series of in-lab experiments and a crowd-sourced experiment to evaluate the effectiveness of interaction history interventions toward mitigating bias. We contextualized this work in a political scenario in which participants were instructed to choose a committee of 10 fictitious politicians to review a recent bill passed in the U.S. state of Georgia banning abortion after 6 weeks, where things like gender bias or political party bias may drive one's analysis process. We demonstrate the generalizability of this approach by evaluating a second decision making scenario related to movies. Our results are inconclusive for the effectiveness of interaction history (henceforth referred to as interaction traces) toward mitigating biased decision making. However, we find some mixed support that interaction traces, particularly in a summative format, can increase awareness of potential unconscious biases.