[Infovis] CfP, 3rd IEEE VIS TREX wokshop on TRust and EXpertise in Visualization

[Nourani,Mahsan]

Dear Colleagues/Friends,


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!


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**.


For more information and updates, please follow us on Twitter @TrexInVIS (https://twitter.com/TrexInVIS ) or visit our website: https://trexvis.github.io/Workshop2022/index.html.

Below is the formal call-for-papers for TREX 2022:

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**IMPORTANT DATES (All times in PST)**

Submission Deadline: Jul 16, 2022

Author Notification: Aug 12, 2022

Camera-Ready Deadline: Aug 22, 2022

TREX 2021 Workshop: October 16 or 17, 2022 ( hybrid, online and in Oklahoma City, USA )


**WORKSHOP GOALS**

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.


**TOPICS OF INTEREST**

  1.  Trust considerations based on different areas of domain expertise (e.g., medical, security, scientific, financial domains)

  2.   Trust and bias considerations based on different levels of user familiarity with machine learning and visual analytics systems

  3.  Detecting and preventing cognitive biases in visual analytics and machine learning for users.

  4.  User trust in machine learning models and visual explanations of model decisions in visual analytics systems.

  5.  The correlation between trust, domain knowledge, and potential cognitive biases.

  6.  The relationship between domain expertise and trust with model transparency, human interpretability, and model understandability in visual data analysis.

  7.  The relationship between model interpretability, domain expertise, and trust.

  8.  Human-centered considerations in Human-in-the-loop visualization tools and interpretable models.


**CONTRIBUTION TYPES**

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:


  1.  Human empirical research/findings

  2.  Design guidelines and surveys

  3.  Technique papers

  4.  Position papers

  5.  Case studies


**SUBMISSION FORMAT**

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: https://new.precisionconference.com/user/login?next=https%3A//new.precisionconference.com/submissions . At least one author from each accepted paper needs to register for the conference (please refer to the IEEE VIS Website http://ieeevis.org/ 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.


**PUBLICATION**

The workshop accepts papers in two formats, as seen below. Upon submitting the papers, authors are asked to determine their desired paper format:


  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.


  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.


**ORGANIZERS**

Mahsan Nourani -- University of Florida

Eric Ragan -- University of Florida

Alireza Karduni -- Northwestern University

Cindy Xiong – University of Massachusetts at Amherst

Brittaney Davis – Pacific Northwest National Laboratory


**PROGRAM COMMITTEE**

TBD


**CONTACT**

For more information, you can email us at trex at ieeevis.org, follow us on Twitter @TrexInVIS, or visit our workshop website https://trexvis.github.io/Workshop2022/.


Best,
Mahsan
---
Mahsan Nourani,
Graduate Research Assistant, INDIE Lab,
Department of Computer and Information Science and Engineering,
University of Florida
mahsan.page<https://mahsan.page/>