In multiple watershed planning and design problems, such as conservation planning, quantitative estimates of costs, and environmental benefits of proposed conservation decisions may not be the only criteria that influence stakeholders' preferences for those decisions. Their preferences may also be influenced by the conservation decision itself—specifically, the type of practice, where it is being proposed, existing biases, and previous experiences with the practice. While human‐in‐the‐loop type search techniques, such as Interactive Genetic Algorithms (IGA), provide opportunities for stakeholders to incorporate their preferences in the design of alternatives, examination of user‐preferred conservation design alternatives for patterns in Decision Space can provide insights into which local decisions have higher or lower agreement among stakeholders. In this paper, we explore and compare spatial patterns in conservation decisions (specifically involving cover crops and filter strips) within design alternatives generated by IGA and noninteractive GA. Methods for comparing patterns include nonvisual as well as visualization approaches, including a novel visual analytics technique. Results for the study site show that user‐preferred designs generated by all participants had strong bias for cover crops in a majority (50%–83%) of the subbasins. Further, exploration with heat maps visualization indicate that IGA‐based search yielded very different spatial patterns of user‐preferred decisions in subbasins in comparison to decisions within design alternatives that were generated without the human‐in‐the‐loop. Finally, the proposed coincident‐nodes, multiedge graph visualization was helpful in visualizing disagreement among participants in local subbasin scale decisions, and for visualizing spatial patterns in local subbasin scale costs and benefits.
Toward a Bias-Aware Future for Mixed-Initiative Visual Analytics
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.
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- Award ID(s):
- 1813281
- NSF-PAR ID:
- 10226726
- Date Published:
- Journal Name:
- Workshop on Trust and Expertise in Visual Analytics (TREX)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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