Toward a Bias-Aware Future for Mixed-Initiative Visual Analytics

TL;DR

This paper advocates for integrating cognitive bias mitigation directly into mixed-initiative visual analytics systems, emphasizing real-time bias detection and addressing biases as they occur to improve decision-making accuracy.

Contribution

It introduces the concept of bias-aware mixed-initiative design, proposing embedded mitigation strategies and exploring expert perspectives to enhance visual analytics systems.

Findings

Current systems address bias statically, not dynamically.

Expert insights highlight opportunities for real-time bias mitigation.

Open research directions for bias-aware visual analytics are outlined.

Abstract

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.