6th Workshop on
Visualization for AI Explainability

October 18th, 2023 Online at 8:00am PT / 3:00pm UTC (+ meetup at IEEE VIS 2023 in Melbourne, Australia)

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.

The goal of this workshop is to initiate a call for "explainables" / "explorables" 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.

Example interactive visualization articles that explain general concepts and communicate experimental insights when playing with AI models. (a) A Visual Exploration of Gaussian Processes by Görtler, Kehlbeck, and Deussen (VISxAI 2018); (b) What Have Language Models Learned? by Adam Pearce (VISxAI 2021); (c) What if we Reduce the Memory of an Artificial Doom Player? by Jaunet, Vuillemot, and Wolf (VISxAI 2019); (d) K-Means Clustering: An Explorable Explainer by Yi Zhe Ang (VISxAI 2022); (e) Comparing DNNs with UMAP Tour by Li and Scheidegger (VISxAI 2020); (f) The Myth of the Impartial Machine by Feng and Wu (Parametric Press); (g) FormaFluens Data Experiment by Strobelt, Phibbs, and Martino. (h) The Beginner's Guide to Dimensionality Reduction by Conlen and Hohman (VISxAI 2018).

Important Dates

July 30, 2023, anywhere: Submission Deadline
September 10, 2023: Author Notification
October 1, 2023: Camera Ready Deadline
October 18th, 2023 at 8:00am PT / 3:00pm UTC: Workshop Online
October xx, 2023: (optional) Meetup in Melbourne at VIS 2023 

Program Overview

All times in PT (UTC -8) on Wednesday, October 18, 2023.

→ VISxAI is free to attend! Join us via Zoom: VISxAI 2023 Zoom link.
Add VISxAI 2023 to your calendar!
→ If you plan to attend the in-person VISxAI meetup at IEEE VIS (Thursday, October 26 at 12:00pm), please fill out this form.

8:00 Welcome from the Organizers
8:00 -- 8:30 Session I
Conformal Prediction: A Visual Introduction -- Mihir Agarwal, Lalit Chandra Routhu, Zeel B Patel, Nipun Batra
Understanding and Comparing Multi-Modal Models -- Christina Humer, Vidya Prasad, Marc Streit, Hendrik Strobelt
Neighborhood traces: When your neighborhood changes one layer at a time -- Moritz Dück, Johannes Knittel, Hendrik Strobelt, Mennatallah El-Assady
Of Deadly Skullcaps and Amethyst Deceivers: Reflections on a Transdisciplinary Study on XAI and Trust -- Andreas Hinterreiter, Christina Humer, Benedikt Leichtmann, Martina Mara, Marc Streit
VisForPINNs: Visualization for Understanding Physics Informed Neural Networks -- Viny Saajan Victor, Manuel Ettmüller, Andre Schmeißer, Heike Leitte, Simone Gramsch
8:30 -- 8:45 Break
8:45 -- 9:15 Session II
Do Machine Learning Models Memorize or Generalize? -- Adam Pearce, Asma Ghandeharioun, Nada Hussein, Nithum Thain, Martin Wattenberg, Lucas Dixon
Diffusion Explainer: Visual Explanation for Text-to-image Stable Diffusion -- Seongmin Lee, Benjamin Hoover, Hendrik Strobelt, Zijie J. Wang, ShengYun Peng, Austin P Wright, Kevin Li, Haoyang Yang. Haekyu Park, Duen Horng Chau
Learning What's in a Name with Graphical Models -- Vu Luong, Justin S Selig
Neural Networks: A Visual Introduction -- Jared Wilber
PAC Learning Or: Why We Should (and Shouldn't) Trust Machine Learning -- Dylan Cashman
9:15 -- 9:30 Break
9:30 -- 10:30 Keynote: Matthew Conlen - @mathisonian
Beyond Notebooks: Computational Tools for Disseminating Research and Ideas
Computational notebooks and interactive essays are both powerful mediums for sharing research; however, designers often mistakenly assume that they share similar design goals and constraints. This talk provides a critical examination of the differences between these two formats, highlighting the potential for interactive essays to move beyond the linear notebook format through a survey of influential works in experimental literature, cinema, graphic design, and game design. Recent research aids authors in crafting interactive essays, leveraging a variety of computational techniques including programming language design, structured text editing, computer graphics, and AR/VR.
10:30am Closing

Call for Participation

Submission instructions

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.

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 Distill-style blog post (format), interactive Idyll markup, or a Jupyter or Observable notebook that integrates code, text, and visualization to tell the story.

Here are a few examples of visual explanations of AI methods in these types of formats:

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: orga (at) visxai.io.

Our workshop will be hybrid. We encourage and accept submissions for those who cannot travel to VIS in person.

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).

Hall of Fame

Each year we award Best Submissions and Honorable Mentions. Congrats to our winners!

VISxAI 2023
VISxAI 2022
VISxAI 2021
VISxAI 2020
VISxAI 2019
VISxAI 2018

Organizers (alphabetic)

Alex Bäuerle - Independent Researcher
Angie Boggust - Massachusetts Institute of Technology
Fred Hohman - Apple
Ian Johnson - Latent Interfaces
Zijie Jay Wang - Georgia Tech

Steering Committee

Adam Perer - Carnegie Mellon University
Hendrik Strobelt - MIT-IBM Watson AI Lab
Mennatallah El-Assady - ETH AI Center

Program Committee

Jane Adams
Marco Angelini
Donald Bertucci
Ángel Cabrera
Jaegul Choo
Brandon Duderstadt
Angus Forbes
Iris Howley
Andriy Mulyar
Rita Sevastjanova
Arjun Srinivasan
Yang Wang
James Wexler