  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.
