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<!DOCTYPE html>
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<title>5th VISxAI Workshop at IEEE VIS 2022</title>
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VISxAI is back! Join us at <a href="http://visxai.io">VISxAI 2023 at IEEE VIS</a> in Melbourne, Australia!
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<a class="nav-link" href="2019.html">2019</a>
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<h2>5<sup>th</sup> Workshop on <br> <b>Visualization for AI Explainability</b></h2>
<p>October 17th, 2022 at IEEE VIS in Oklahoma City, Oklahoma</p>
<!-- <p class="text-center" style="font-size: 14pt;">
<b>PROGRAM IS ONLINE. <a href="program.html"> CLICK HERE !!!</a> </b>
</p> -->
<p>
The role of visualization in artificial intelligence (AI) gained
significant attention in recent years. With the growing complexity of AI
models, the critical need for understanding their inner-workings has
increased. Visualization is potentially a powerful technique to fill
such a critical need.
</p>
<p>
The goal of this workshop is to initiate a call for <i>"explainables" / "explorables"</i> that
explain how AI techniques work using visualization. We believe the VIS
community can leverage their expertise in creating visual narratives to
bring new insight into the often obfuscated complexity of AI systems.
</p>
<!-- <p class="text-center" style="font-size: 14pt;"> -->
<!-- <b>PROGRAM IS ONLINE. <a href="program.html"> CLICK HERE !!!</a> </b> -->
<!-- </p> -->
<p class="text-center">
<img class="img-fluid" src="img/examples-2022.png">
<div class="figure-caption">Example interactive visualization articles that explain general concepts and communicate experimental insights when playing with AI models.
(a) <a href="https://distill.pub/2019/visual-exploration-gaussian-processes/" target="_blank">A Visual Exploration of Gaussian Processes</a> by Görtler, Kehlbeck, and Deussen (<a href="https://visxai.io/2018">VISxAI 2018</a>);
(b) <a href="https://pair.withgoogle.com/explorables/fill-in-the-blank/" target="_blank">What Have Language Models Learned?</a> by Adam Pearce (<a href="https://visxai.io/2021">VISxAI 2021</a>);
(c) <a href="https://theo-jaunet.github.io/MemoryReduction/" target="_blank">What if we Reduce the Memory of an Artificial Doom Player?</a> by Jaunet, Vuillemot, and Wolf (<a href="https://visxai.io/2019">VISxAI 2019</a>);
(d) <a href="https://tiga1231.github.io/umap-tour/" target="_blank">Comparing DNNs with UMAP Tour</a> by Li and Scheidegger (<a href="https://visxai.io/2020">VISxAI 2020</a>);
(e) <a href="https://parametric.press/issue-01/the-myth-of-the-impartial-machine/" target="_blank">The Myth of the Impartial Machine</a> by Feng and Wu (<a href="https://parametric.press/">Parametric Press</a>);
(f) <a href="http://formafluens.io/client/mix10.html">FormaFluens Data Experiment</a> by Strobelt, Phibbs, and Martino.
</div>
</p>
<h2 id="dates">Important Dates</h2>
<p><i>Note: Dates are approximate and will be finalized soon. Also, dates could be revised due to the ongoing <a href="https://www.cdc.gov/coronavirus/2019-ncov/index.html">COVID-19 outbreak</a>.</i></p>
<pre>
<s>July 22, 2022</s> July 29, 2022, anywhere: Explainables Submission
<s>August 22, 2022</s> August 29, 2022: Author Notification
October 17th, 2022 -- Workshop in Oklahoma City at IEEE VIS 2022
</pre>
<!-- September 1, 2021: Camera-ready Copy for Accepted Submissions -->
<!-- September ?, 2021: VIS Registration for 2021 -->
<h2 id="program">Program Overview</h2>
<p>
All times in CDT (UTC -5) on Monday, October 17, 2022.
<br>
<br>→ To attend, register at <a href="http://ieeevis.org/year/2022/info/registration/conference-registration">IEEE VIS</a>.
<!-- <br>→ <a href="calendar/VISxAI2020.ics">Add to your calendar.</a> -->
<!-- <br>→ <a href="https://virtual.ieeevis.org/year/2021/session_a-visxai.html">Join the virtual even here!</a> -->
</p>
<table style="padding: 5pt;">
<tr>
<td class="schedule">2:00 -- 2:05</td>
<td><b>Welcome from the Organizers</b></td>
</tr>
<tr>
<td class="schedule">2:05 -- 2:55</td>
<td><b>Keynote: Ian Johnson (Observable) - <a href="https://twitter.com/enjalot" target="_blank">@enjalot</a></b>
<br>
<b>Towards a Pattern Language for Visualizing AI</b>
<br>
Building visualizations for better understanding machine learning models is important but challenging work that seems to call for learning an entirely new discipline. Machine learning practitioners and data visualization developers share an important foundation: an intimate relationship with data. This talk will present the beginnings of a pattern language that will allow us to build on that foundation together. Drawing from my experiences working on Distill, creating data visualization tools at Observable as well as surveying the excellent work presented in the last 4 years at VISxAI, we will examine patterns that can be practically applied to building your next visualization.
</td>
</tr>
<tr>
<td class="schedule">2:55 -- 3:15</td>
<td><b>Session I</b>
<br>
<a href="https://k-means-explorable.vercel.app/">
K-Means Clustering: An Explorable Explainer
</a>
-- Yi Zhe Ang
<br>
<a href="https://pbizopoulos.github.io/action-as-information/">
Action as Information
</a>
-- Paschalis Bizopoulos
<br>
</td>
</tr>
<tr>
<td class="schedule">3:15 -- 3:45</td>
<td><b>Break</b></td>
</tr>
<tr>
<td class="schedule">3:45 -- 4:15</td>
<td><b>Session II</b>
<br>
<a href="https://lm-bias.lingvis.io/">
How is Real-World Gender Bias Reflected in Language Models?
</a>
-- Javier Rando, Alexander Theus, Rita Sevastjanova, Mennatallah El-Assady
<br>
<a href="https://julheg.github.io/waveletexplainability/">
Explaining Image Classifiers with Wavelets
</a>
-- Julius Hege
<br>
<a href="https://ibrahimalhazwani.github.io/distill-xai/">
What should we watch tonight?
</a>
-- Ibrahim Al-Hazwani, Gabriela Morgenshtern, Yves Rutishauser, Mennatallah El-Assady, Jürgen Bernard
</td>
</tr>
<tr>
<td class="schedule">4:15 -- 4:45</td>
<td><b>Session III</b>
<br>
<a href="https://uvasrg.github.io/poisoning/">
Poisoning Attacks and Subpopulation Susceptibility
</a>
-- Evan Rose, Fnu Suya, David Evans
<br>
<a href="http://nomic.ai/visxwiki">
Mapping Wikipedia with BERT and UMAP
</a>
-- Brandon Duderstadt
<br>
<a href="https://lbynum.github.io/interactive-causal-inference/">
An Interactive Introduction to Causal Inference
</a>
-- Lucius E.J. Bynum, Falaah Arif Khan, Oleksandra Konopatska, Joshua Loftus, Julia Stoyanovich
<br>
</td>
</tr>
<tr>
<td class="schedule">4:45 -- 5:00</td>
<td><b>Closing Session</b></td>
</tr>
</table>
<br>
<h2 id="call">Call for Participation</h2>
<!-- <p><strong>SUBMISSION CLOSED</strong></p> -->
<!-- <p> -->
<!-- To make our work more accessible to the general audience, we are soliciting submissions in a novel format:
blog-style posts and jupyter-like notebooks. In addition we also accept position papers in a more
traditional form.
Please contact us, if you want to submit a original work in another format. Email: <a
href="mailto:[email protected]">orga.visxai at gmail.com</a> -->
<!-- </p> -->
<div class="submit-button">
<a href="/submit.html">Submission instructions</a>
</div>
<br>
<p>
Explainable submissions (e.g., interactive articles, markup, and notebooks) are the core element of the workshop, as this
workshop aims to be a platform for explanatory visualizations focusing
on AI techniques.
</p>
<p>
Authors have the freedom to use whatever templates and formats they like. However, the narrative has to be
visual and interactive, and walk readers through a keen understanding on the ML technique or application.
Authors may wish to write a <a href="https://distill.pub">Distill-style</a> blog post (format), interactive
<a href="https://idyll-lang.org/">Idyll</a> markup, or a <a href="http://jupyter.org">Jupyter</a> or <a
href="https://beta.observablehq.com/">Observable</a> notebook that integrates code, text, and
visualization to tell the story.
</p>
<p>
Here are a few examples of visual explanations of AI methods in these types of formats:
<ul>
<li>[interactive article]
<a href="https://distill.pub/2019/visual-exploration-gaussian-processes/" target="_blank">A Visual Exploration of
Gaussian Processes</a>
</li>
<li>[interactive article]
<a href="https://distill.pub/2017/momentum/" target="_blank">Why Momentum Really Works</a>
</li>
<li>[interactive article]
<a href="http://www.r2d3.us/visual-intro-to-machine-learning-part-1/" target="_blank">A Visual Introduction to Machine Learning</a>
</li>
<li>[interactive article]
<a href="http://formafluens.io/client/mix10.html" target="_blank">Art-Inspired Data Experiments on Neural Network Model Decay</a>
</li>
<li>[interactive article]
<a href="https://research.google.com/bigpicture/attacking-discrimination-in-ml/" target="_blank">Attacking Discrimination with Smarter Machine Learning</a>
</li>
<li>[markup]
<a href="https://parametric.press/issue-01/the-myth-of-the-impartial-machine/" target="_">The Myth of the Impartial Machine</a>
</li>
<li>[markup]
<a href="https://idyll.pub/post/visxai-dimensionality-reduction-1dbad0a67a092b007c526a45/" target="_">The Beginner's Guide to Dimensionality Reduction</a>
</li>
<li>[notebook]
<a href="https://beta.observablehq.com/@nstrayer/t-sne-explained-in-plain-javascript" target="_blank">t-SNE Explained in Plain JavaScript</a>
</li>
<li>[notebook]
<a href="https://observablehq.com/@nsthorat/how-to-build-a-teachable-machine-with-tensorflow-js?collection=@observablehq/explorables" target="_blank">How to build a Teachable Machine with TensorFlow.js</a>
</li>
<li>[notebook]
<a href="http://nbviewer.jupyter.org/github/agconti/kaggle-titanic/blob/master/Titanic.ipynb" target="_blank">Titanic Machine Learning from Disaster</a>
</li>
</ul>
</p>
<p>
While these examples are informative and excellent, we hope the
Visualization & ML community will think about ways to creatively expand on
such foundational work to explain AI methods using novel interactions
and visualizations often present at IEEE VIS.
Please contact us, if you want to submit a original work in another
format. Email: <a href="mailto:[email protected]">orga.visxai (at) gmail.com</a>.
</p>
<p>
Note: We also accept more traditional papers that accompany an explainable.
Be aware that we require that the explainable must stand on its own.
The reviewers will evaluate the explainable (and might chose to ignore the paper).
</p>
<p>
In previous years, the best works were invited to submit their extended work to the
online publishing platform distill.pub to generate a cite-able
publication for authors. See <a href="https://distill.pub/2019/visual-exploration-gaussian-processes/">https://distill.pub/2019/visual-exploration-gaussian-processes/</a>.
</p>
<h2 id="hall-of-fame">Hall of Fame</h2>
Each year we award Best Submissions and Honorable Mentions. <i>Congrats to our winners!</i>
<br><br>
<h5>VISxAI 2021</h5>
<ul>
<li>
<a href="https://pair.withgoogle.com/explorables/fill-in-the-blank/">
What Have Language Models Learned?
</a>
-- Adam Pearce
</li>
<li>
<a href="http://www.cs.umd.edu/~amin/apps/visxai/sonification/">
Feature Sonification: An investigation on the features learned for Automatic Speech Recognition
</a>
-- Amin Ghiasi, Hamid Kazemi, W. Ronny Huang, Emily Liu, Micah Goldblum, Tom Goldstein
</li>
</ul>
<h5>VISxAI 2020</h5>
<ul>
<li>
<a href="https://tiga1231.github.io/umap-tour/">
Comparing DNNs with UMAP Tour
</a> -- Mingwei Li and Carlos Scheidegger
</li>
<li>
<a href="https://www.pewresearch.org/interactives/how-does-a-computer-see-gender/">
How Does a Computer "See" Gender?
</a> -- Stefan Wojcik, Emma Remy, and Chris Baronavski
</li>
</ul>
<h5>VISxAI 2019</h5>
<ul>
<li>
<a href="https://theo-jaunet.github.io/MemoryReduction/">
What if we Reduce the Memory of an Artificial Doom Player?
</a> -- Theo Jaunet, Romain Vuillemot, and Christian Wolf
</li>
<li>
<a href="https://qnkxsovc.gitlab.io/prob-vis/">
Statistical Distances and Their Implications to GAN Training
</a> -- Max Daniels
</li>
<li>
<a href="https://mybinder.org/v2/gh/KrishnaswamyLab/visualization_selection/master?filepath=Selecting_the_right_tool_for_the_job.ipynb">
Selecting the right tool for the job: a comparison of visualization algorithms
</a> -- Daniel Burkhardt, Scott Gigante, and Smita Krishnaswamy
</li>
</ul>
<h5>VISxAI 2018</h5>
<ul>
<li>
<a href="https://www.jgoertler.com/visual-exploration-gaussian-processes/">
A Visual Exploration of Gaussian Processes
</a> -- Jochen Görtler, Rebecca Kehlbeck and Oliver Deussen
</li>
<li>
<a href="https://idyll.pub/post/visxai-dimensionality-reduction-1dbad0a67a092b007c526a45/">
The Beginner's Guide to Dimensionality Reduction
</a> -- Matthew Conlen and Fred Hohman
</li>
<li>
<a href="https://roadsfromabove.netlify.com/">
Roads from Above
</a> -- Greg More, Slaven Marusic and Caihao Cui
</li>
</ul>
<!-- <p> <strong>SUBMISSION CLOSED</strong></p> -->
<h2 id="orga">Organizers <span style="font-size: small">(alphabetic)</span>
</h2>
<p>
Adam Perer - Carnegie Mellon University<br />
Angie Boggust - Massachusetts Institute of Technology<br />
Fred Hohman - Apple<br />
Hendrik Strobelt - MIT-IBM Watson AI Lab<br />
Mennatallah El-Assady - ETH AI Center<br />
Zijie Jay Wang - Georgia Tech<br />
</p>
<h5>Steering Committee</h5>
<p>
Duen Horng (Polo) Chau - Georgia Tech<br />
Fernanda Viégas - Google Brain<br />
</p>
<h2 id="pc">Program Committee</h2>
<p>
Soumya Dutta<br/>
Zhixuan Zhou<br/>
Marco Angelini<br/>
Jürgen Bernard<br/>
Alex Bäuerle<br/>
Jaegul Choo<br/>
Angus Forbes<br/>
Iris Howley<br/>
Romain Vuillemot<br/>
James Wexler<br/>
Donald Bertucci<br/>
Ángel Cabrera<br/>
Victor Dibia<br/>
Denis Parra<br/>
Rita Sevastjanova<br/>
Yang Wang<br/>
</p>
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