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<p style="margin:0in"><span style="font-family:"Arial",sans-serif;color:black">Dear Colleagues/Friends,</span></p>
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<p style="margin:0in"><span style="font-family:"Arial",sans-serif;color:black">We are happy to invite submissions to the upcoming TREX workshop on TRust and EXpertise in Visualization, which will take place at IEEE VIS 2022. Please help us spread the word!</span></p>
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<p style="margin:0in"><span style="font-family:"Arial",sans-serif;color:black">Following the last two successful workshops, this year we will focus on **human-centered aspects of visualization and visual analytics systems that are powered by machine learning
and artificial intelligence algorithms, including, but not limited to user trust, cognitive biases, domain expertise, transparency, and human-in-the-loop considerations**.</span></p>
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<p style="margin:0in"><span style="font-family:"Arial",sans-serif;color:black">For more information and updates, please follow us on Twitter @TrexInVIS (https://twitter.com/TrexInVIS ) or visit our website:
<a href="https://trexvis.github.io/Workshop2022/index.html">https://trexvis.github.io/Workshop2022/index.html</a></span>.</p>
<p style="margin:0in"><span style="font-family:"Arial",sans-serif;color:black">Below is the formal call-for-papers for TREX 2022:</span></p>
<p style="margin:0in"><span style="font-family:"Arial",sans-serif;color:black">-------------------------------------------------</span></p>
<p style="margin:0in"><span style="font-family:"Arial",sans-serif;color:black">**IMPORTANT DATES (All times in PST)**</span></p>
<p style="margin:0in"><span style="font-family:"Arial",sans-serif;color:black">Submission Deadline: Jul 16, 2022</span></p>
<p style="margin:0in"><span style="font-family:"Arial",sans-serif;color:black">Author Notification: Aug 12, 2022</span></p>
<p style="margin:0in"><span style="font-family:"Arial",sans-serif;color:black">Camera-Ready Deadline: Aug 22, 2022</span></p>
<p style="margin:0in"><span style="font-family:"Arial",sans-serif;color:black">TREX 2021 Workshop: October 16 or 17, 2022 ( hybrid, online and in Oklahoma City, USA )</span></p>
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<p style="margin:0in"><span style="font-family:"Arial",sans-serif;color:black">**WORKSHOP GOALS**</span></p>
<p style="margin:0in"><span style="font-family:"Arial",sans-serif;color:black">Interactive visualization systems combine computational support, human cognitive, and perceptual skills to explore and analyze data. Many of these systems incorporate machine learning
(ML) and Artificial Intelligence (AI) algorithms to introduce some level of automation to the analytical process. However, there are aspects that can impact the effectiveness of the human-machine collaboration, including, but not limited to: 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. The goal of this workshop is to highlight and foster a conversation around the intersection of visualization and human-centered
AI research and practice by providing an interdisciplinary platform from relevant fields to communicate challenges and novel findings around such topics. </span></p>
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<p style="margin:0in"><span style="font-family:"Arial",sans-serif;color:black">**TOPICS OF INTEREST**</span></p>
<p style="margin:0in"><span style="font-family:"Arial",sans-serif;color:black"> 1. Trust considerations based on different areas of domain expertise (e.g., medical, security, scientific, financial domains)</span></p>
<p style="margin:0in"><span style="font-family:"Arial",sans-serif;color:black"> 2. Trust and bias considerations based on different levels of user familiarity with machine learning and visual analytics systems</span></p>
<p style="margin:0in"><span style="font-family:"Arial",sans-serif;color:black"> 3. Detecting and preventing cognitive biases in visual analytics and machine learning for users.</span></p>
<p style="margin:0in"><span style="font-family:"Arial",sans-serif;color:black"> 4. User trust in machine learning models and visual explanations of model decisions in visual analytics systems.</span></p>
<p style="margin:0in"><span style="font-family:"Arial",sans-serif;color:black"> 5. The correlation between trust, domain knowledge, and potential cognitive biases.</span></p>
<p style="margin:0in"><span style="font-family:"Arial",sans-serif;color:black"> 6. The relationship between domain expertise and trust with model transparency, human interpretability, and model understandability in visual data analysis.</span></p>
<p style="margin:0in"><span style="font-family:"Arial",sans-serif;color:black"> 7. The relationship between model interpretability, domain expertise, and trust.</span></p>
<p style="margin:0in"><span style="font-family:"Arial",sans-serif;color:black"> 8. Human-centered considerations in Human-in-the-loop visualization tools and interpretable models.</span></p>
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<p style="margin:0in"><span style="font-family:"Arial",sans-serif;color:black">**CONTRIBUTION TYPES**</span></p>
<p style="margin:0in"><span style="font-family:"Arial",sans-serif;color:black">We are looking for and open to diverse forms of research (as listed below) within the realm of visualization and machine learning with appropriate emphasis and relation to human
trust, expertise, and cognitive factors. We encourage submissions of research involving:</span></p>
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<p style="margin:0in"><span style="font-family:"Arial",sans-serif;color:black"> 1. Human empirical research/findings</span></p>
<p style="margin:0in"><span style="font-family:"Arial",sans-serif;color:black"> 2. Design guidelines and surveys</span></p>
<p style="margin:0in"><span style="font-family:"Arial",sans-serif;color:black"> 3. Technique papers</span></p>
<p style="margin:0in"><span style="font-family:"Arial",sans-serif;color:black"> 4. Position papers</span></p>
<p style="margin:0in"><span style="font-family:"Arial",sans-serif;color:black"> 5. Case studies</span></p>
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<p style="margin:0in"><span style="font-family:"Arial",sans-serif;color:black">**SUBMISSION FORMAT**</span></p>
<p style="margin:0in"><span style="font-family:"Arial",sans-serif;color:black">The workshop will accept papers with 2 to 6 pages in length, plus 2 pages of references. All submissions must be in IEEE VGTC Conference Style Template format (Download the templates
here: http://junctionpublishing.org/vgtc/Tasks/camera.html ). We also encourage authors to submit a short 30-second video of their work (for publication purposes) when submitting their work, if applicable. Depending on the conference format this year (i.e.,
virtual or hybrid), the authors of the accepted papers might be required to submit a longer video (e.g., 5 to 10 minutes) to substitute a live presentation. In such an event, we will send further instructions closer to the workshop. The submissions are made
through the PCS (Precision Conference Solutions) website: <a href="https://new.precisionconference.com/user/login?next=https%3A//new.precisionconference.com/submissions">
https://new.precisionconference.com/user/login?next=https%3A//new.precisionconference.com/submissions</a> . At least one author from each accepted paper needs to register for the conference (please refer to the IEEE VIS Website
<a href="http://ieeevis.org/">http://ieeevis.org/</a> for the registration requirements). The submissions are not required to be anonymized and may include the full name of authors, their email, and affiliation as well as an acknowledgment.</span></p>
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<p style="margin:0in"><span style="font-family:"Arial",sans-serif;color:black">**PUBLICATION**</span></p>
<p style="margin:0in"><span style="font-family:"Arial",sans-serif;color:black">The workshop accepts papers in two formats, as seen below. Upon submitting the papers, authors are asked to determine their desired paper format:</span></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p style="margin:0in"><span style="font-family:"Arial",sans-serif;color:black"> 1. Archival Short paper publication through IEEE Xplore. This means that the papers can only be published in a proper journal as an extended version with at least 30% new content.
This means each paper will be published and available through IEEE Xplore with an assigned DOI and are citable. While the papers published in this format can be extended to a journal publication (with 30% new content, subject to the rules of that journal),
this content cannot be re-used for a future conference submission, such as IEEE VIS, EuroVis, and CHI.</span></p>
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<p style="margin:0in"><span style="font-family:"Arial",sans-serif;color:black"> 1. Short paper statements or Work-in-progress notes, which will be non-archival. In other words, the authors maintain the copyright of their paper and are allowed to reuse their
material in future publications.</span></p>
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<p style="margin:0in"><span style="font-family:"Arial",sans-serif;color:black">**ORGANIZERS**</span></p>
<p style="margin:0in"><span style="font-family:"Arial",sans-serif;color:black">Mahsan Nourani -- University of Florida</span></p>
<p style="margin:0in"><span style="font-family:"Arial",sans-serif;color:black">Eric Ragan -- University of Florida</span></p>
<p style="margin:0in"><span style="font-family:"Arial",sans-serif;color:black">Alireza Karduni -- Northwestern University</span></p>
<p style="margin:0in"><span style="font-family:"Arial",sans-serif;color:black">Cindy Xiong – University of Massachusetts at Amherst</span></p>
<p style="margin:0in"><span style="font-family:"Arial",sans-serif;color:black">Brittaney Davis – Pacific Northwest National Laboratory </span></p>
<p class="MsoNormal" style="margin-bottom:12.0pt"><o:p> </o:p></p>
<p style="margin:0in"><span style="font-family:"Arial",sans-serif;color:black">**PROGRAM COMMITTEE**</span></p>
<p style="margin:0in"><span style="font-family:"Arial",sans-serif;color:black">TBD</span></p>
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<p style="margin:0in"><span style="font-family:"Arial",sans-serif;color:black">**CONTACT**</span></p>
<p style="margin:0in"><span style="font-family:"Arial",sans-serif;color:black">For more information, you can email us at trex@ieeevis.org, follow us on Twitter @TrexInVIS, or visit our workshop website
<a href="https://trexvis.github.io/Workshop2022/">https://trexvis.github.io/Workshop2022/</a>.
</span></p>
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<p style="margin:0in"><span style="color:#333333;border:none windowtext 1.0pt;padding:0in">Best,</span></p>
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<p class="MsoNormal" style="vertical-align:baseline"><span style="color:#333333;border:none windowtext 1.0pt;padding:0in">Mahsan</span><span style="font-size:10.5pt;font-family:"Segoe UI",sans-serif;color:black"><o:p></o:p></span></p>
<p class="MsoNormal" style="vertical-align:baseline"><i><span style="font-family:"Candara",sans-serif;color:#333333;border:none windowtext 1.0pt;padding:0in">---</span></i><span style="font-size:12.0pt;color:black"><o:p></o:p></span></p>
<p class="MsoNormal" style="vertical-align:baseline"><i><span style="font-family:"Candara",sans-serif;color:#333333;border:none windowtext 1.0pt;padding:0in">Mahsan Nourani,</span></i><span style="font-size:12.0pt;color:black"><o:p></o:p></span></p>
<p class="MsoNormal" style="vertical-align:baseline"><i><span style="font-family:"Candara",sans-serif;color:#333333;border:none windowtext 1.0pt;padding:0in">Graduate Research Assistant, INDIE Lab,</span></i><span style="font-size:12.0pt;color:black"><o:p></o:p></span></p>
<p class="MsoNormal" style="vertical-align:baseline"><i><span style="font-family:"Candara",sans-serif;color:#333333;border:none windowtext 1.0pt;padding:0in">Department of Computer and Information Science and Engineering,</span></i><span style="font-size:12.0pt;color:black"><o:p></o:p></span></p>
<p class="MsoNormal" style="vertical-align:baseline"><i><span style="font-family:"Candara",sans-serif;color:#333333;border:none windowtext 1.0pt;padding:0in">University of Florida</span></i><span style="font-size:12.0pt;color:black"><o:p></o:p></span></p>
<p class="MsoNormal" style="vertical-align:baseline"><a href="https://mahsan.page/" title="https://mahsan.page/"><span style="font-family:"Candara",sans-serif;border:none windowtext 1.0pt;padding:0in">mahsan.page</span></a><span style="font-size:12.0pt;color:black"><o:p></o:p></span></p>
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