Collaborative Research: HCC: Medium: Modeling and Mitigating Confirmation Bias in Visual Data Analysis

People can easily be overwhelmed with data when making decisions, such as deciding which healthcare treatment is appropriate or which political candidate to vote for. When overwhelmed by data, people tend to seek and interpret information in a way that supports their preexisting beliefs. This phenomenon is often referred to as confirmation bias. In data communication and visual analytics, confirmation bias can be especially nefarious, even for experienced analysts. Although there is a misconception that statistical models and visualizations present objective truths, in reality, choices in the collection, handling, analysis, and presentation of data can bias people into overly relying on their pre-existing beliefs. This project will closely examine confirmation bias in data analysis by (1) creating models that show how existing beliefs and analytic goals can impact data-driven decision-making, and (2) designing novel analytic interfaces that help analysts make less biased decisions by intelligently suggesting evidence that may disprove a belief. The project team will also create educational materials and collect empirical datasets to help data analysts, researchers, and members of the public think about confirmation bias in visual data communication and interpretation. This project aims to increase understanding of how confirmation bias manifests in real-world visual data analysis tasks and to develop and evaluate bias-mitigation interventions. The project is structured around four research thrusts. As measuring confirmation bias requires capturing an individual’s beliefs, the researchers will first investigate experimental methods to accurately capture a person's beliefs and mental representations about data patterns and trends in an analytic setting (Thrust I). The researchers will then leverage these methodological findings to measure and model the effect of confirmation bias in low-level visual analytic tasks such as finding correlations (Thrust II), then examine higher-level compositions of these tasks in more realistic analysis settings (Thrust III). Finally, the researchers will design and develop four bias-mitigation interventions to be incorporated into real-world visual analytic tools such as Data Voyager and Jupyter Notebooks, recruiting professional analysts to evaluate them in digital field studies (Thrust IV). This research agenda will advance the understanding of confirmation bias and provide promising interventions to empower data analysts to make better decisions. The researchers will also develop coursework and initiatives that bring together computer science, psychology, and ethics to advance practice and education around visual data analytics. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.