This is a third-time workshop that is happening in conjunction with the IEEE Visualization Conference, scheduled to take place in Oklahoma City, USA in October 2022. We will share all the relevant news and updates about the workshop on this website.
This workshop invites contributions that provide a user-centered perspective on how human-machine trust, domain expert knowledge, and familiarity with data science methods influence the use and adoption of visual analytics techniques and systems. The goal is to discuss and discover challenges and future directions regarding these issues by proposing design guidelines, empirical findings, and visual analytic techniques.
Sponsor Type:1
Mahsan Nourani
University of Florida
Eric Ragan
University of Florida
Alireza Karduni
Northwestern University
Cindy Xiong
University of Massachusetts Amherst
Brittany Davis
Pacific Northwest National Lab
Trust considerations based on different areas of domain expertise (e.g., medical, security, scientific, financial domains).
Trust and bias considerations based on different levels of user familiarity with machine learning and visual analytics systems.
Detecting and preventing cognitive biases in visual analytics and machine learning for users.
User trust in machine learning models and visual explanations of model decisions in visual analytics systems.
The correlation between trust, domain knowledge, and potential cognitive biases.
The relationship between domain expertise and trust with model transparency, human interpretability.
The relationship between model interpretability, domain expertise, and trust.
Human-centered considerations in Human-in-the-loop visualization tools and interpretable models.
10月16日
2022
会议日期
初稿截稿日期
注册截止日期
2021年10月24日 美国 Online
2021 IEEE Workshop on TRust and EXpertise in Visual Analytics
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