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Mixed-initiative visual analytics systems incorporate well-established design principles that improve users' abilities to solve problems. As these systems consider whether to take initiative towards achieving user goals, many current systems address the potential for cognitive bias in human initiatives statically, relying on fixed initiatives they can take instead of identifying, communicating and addressing the bias as it occurs. We argue that mixed-initiative design principles can and should incorporate cognitive bias mitigation strategies directly through development of mitigation techniques embedded in the system to address cognitive biases in situ. We identify domain experts in machine learning adopting visual analytics techniques and systems that incorporate existing mixed-initiative principles and examine their potential to support bias mitigation strategies. This examination considers the unique perspective these experts bring to visual analytics and is situated in existing user-centered systems that make exemplary use of design principles informed by cognitive theory. We then suggest informed opportunities for domain experts to take initiative toward addressing cognitive biases in light of their existing contributions to the field. Finally, we contribute open questions and research directions for designers seeking to adopt visual analytics techniques that incorporate bias-aware initiatives in future systems. 

The use of cognitive heuristics often leads to fast and effective decisions. However, they can also systematically and predictably lead to errors known as cognitive biases. Strategies for minimizing or mitigating these biases, however, remain largely non-technological (e.g., training courses). The growing use of visual analytic (VA) tools for analysis and decision making enables a new class of bias mitigation strategies. In this work, we explore the ways in which the design of visualizations (vis) may be used to mitigate cognitive biases. We derive a design space comprised of 8 dimensions that can be manipulated to impact a user's cognitive and analytic processes and describe them through an example hiring scenario. This design space can be used to guide and inform future vis systems that may integrate cognitive processes more closely. 

Interaction is the cornerstone of how people perform tasks and gain insight in visual analytics. However, people’s inherent cognitive biases impact their behavior and decision making during their interactive visual analytic process. Understanding how bias impacts the visual analytic process, how it can be measured, and how its negative effects can be mitigated is a complex problem space. Nonetheless, recent work has begun to approach this problem by proposing theoretical computational metrics that are applied to user interaction sequences to measure bias in real-time. In this paper, we implement and apply these computational metrics in the context of anchoring bias. We present the results of a formative study examining how the metrics can capture anchoring bias in real-time during a visual analytic task. We present lessons learned in the form of considerations for applying the metrics in a visual analytic tool. Our findings suggest that these computational metrics are a promising approach for characterizing bias in users’ interactive behaviors.