Detecting and Mitigating Biased Visual Analysis Strategies


People make important decisions based on data every day, from simple choices such as which restaurant to dine at when visiting a city, to important and complex decisions in healthcare about which course of treatment to pursue, and even decisions that impact national security and policy. Visual analytic systems play a critical role in these decision-making processes. They allow people to interact with their data and analytic models to view different perspectives of data and gain insights. This interactive data analysis process consists of people incrementally guiding analytic models to produce alternate views of the data in support of their tasks. In most cases, such human-in-the-loop processes have successful, insightful outcomes. However, the cognitive sciences tell us that people can exhibit innate biased behavior. As a result, their data analysis behaviors and strategies may suffer. Ultimately, this could lead to decisions made from incomplete information and limited perspectives on how the data can be interpreted. Biased analysis processes can lead to biased results and misinformation. This project will perform fundamental research to discover how to detect such potential bias and develop visual analytic systems that mitigate it. It will also produce educational impacts for graduate and undergraduate students from groups underrepresented in STEM fields, in part through outreach workshops with instructors from minority-serving institutions and historically black colleges and universities to help them integrate visual analytics and general data literacy learning objectives into course curricula.

The proposed multi-disciplinary research will develop techniques that enhance mixed-initiative visual analytic analysis processes by intervening and providing guidance when necessary. To accomplish this goal, three primary lines of research are proposed. First, the team will develop and evaluate computational metrics to detect poor and potentially biased analysis strategies from user interaction patterns and system parameters. These metrics consist of probabilistic computational models that take into consideration metrics such as data coverage over the duration of the data exploration. Second, the team will develop and study different visual analytic system designs to guide and improve people's analysis processes. Each prototype will give people guidance using the metrics, but display information to users via different interface designs (e.g., dialog boxes, visual overlays of coverage, etc.) They will be developed, evaluated, and made available via publications and open-source code. Third, the studies proposed will generate empirical results and design guidelines for future mixed-initiative visual analytic systems.

  • Emily Wall has been awarded the D.E. Shaw Exploration Fellowship
  • Emily Wall has been awarded the Siemens FutureMaker Fellowship

  • Toward a Design Space for Mitigating Cognitive Bias in Vis
    Emily Wall, John Stasko, and Alex Endert
    IEEE Information Visualization (VIS) Short Papers, 2019
  • A Markov Model of Users’ Interactive Behavior in Scatterplots
    Emily Wall, Arup Arcalgud, Kuhu Gupta, and Andrew Jo
    IEEE Information Visualization (VIS) Short Papers, 2019
  • A Formative Study of Interactive Bias Metrics in Visual Analytics Using Anchoring Bias
    Emily Wall, Leslie Blaha, Celeste Paul, and Alex Endert
    Proceedings of the 17th IFIP TC 13 International Conference on Human-Computer Interaction (INTERACT'19), 2019
    PDF | Video
  • Four Perspectives on Human Bias in Visual Analytics
    Emily Wall, Leslie Blaha, Celeste Paul, Kristin Cook, and Alex Endert
    Cognitive Biases in Visualizations (Chapter 3, pp. 29-42). Springer, 2018
    Book | Chapter
  • Warning, Bias May Occur: A Proposed Approach to Detecting Cognitive Bias in Interactive Visual Analytics
    Emily Wall, Leslie M. Blaha, Lyndsey Franklin, and Alex Endert
    IEEE Visual Analytics Science and Technology (VAST), 2017
    PDF | Talk (VAST 17)
  • Four Perspectives on Human Bias in Visual Analytics
    Emily Wall, Leslie Blaha, Celeste Paul, Kristin Cook, and Alex Endert
    DECISIVe: Workshop on Dealing with Cognitive Biases in Visualizations (at IEEE VIS'17), 2017

Award Number: IIS-1813281
Title: CHS: Small: Enhancing Data Analysis Strategies with Mixed-Initiative Visual Analytics
Duration: 8/2018 - 8/2021
PI: Alex Endert
Students: Emily Wall

This material is based upon work supported by the National Science Foundation under Grant No. IIS-1813281. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Last updated: November 1, 2019