TREX: Workshop on TRust and EXpertise in Visual Analytics

https://trexvis.github.io/Workshop2020/index.html

2020-10-25T18:00:00Z – 2020-10-25T21:30:00Z

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Visual analytics (VA) systems combine computational support and human cognitive and perceptual skills to explore and analyze data. Many of these systems have been incorporating machine learning (ML) models and algorithms to introduce some level of automation to the analytical process. However, within this relationship, there are a number of aspects that can impact the effectiveness of the human-machine teaming, including: 1) People’s domain and system expertise; 2) Human biases, including cognitive and perceptual biases; 3) Trust in ML models and visual representation of data. Expertise, bias, and trust are intrinsically intertwined. Additionally, visual analytics systems are used in different fields and by people from various backgrounds, with different levels of domain expertise and experience with machine learning and visual analytics tools. This variety of experience and domain expertise among human users has opened the door for new research directions and challenges in the fields of visual analytics and machine learning. Designers who fail to consider the aforementioned diversities might introduce problems to the analysis effectiveness and user experience. Furthermore, experience and domain expertise might affect user trust in visual analytics tools; although, how and why they affect trust is still an open question. Trust will eventually affect how much the users would rely on and use the tool. While users will take advantage of their prior experiences to make better decisions with the assistance of analytic support, they might carry many cognitive biases that can negatively influence their decision-making or analysis process. Recent research shows trust in and reliance on the visual analytics systems/tools as well as user strategies and biases can be directly influenced by domain and system expertise (or lack of expertise). The goal of this workshop is to bring together researchers and practitioners from different disciplines to discuss and discover challenges in ML supported visual analytics tools and set the stage for future research directions and collaborations regarding these issues by proposing design guidelines, empirical findings, and VA techniques.