6th Workshop on
Visualization for AI Explainability
October 18th, 2023 at 8:00am PT / 3:00pm UTC at IEEE VIS in Online (+ 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:
- (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
- (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
Program Overview
All times in ET (UTC -5).
| 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 |
| 9:15 -- 9:30 | Break |
| 9:30 -- 10:30 | Keynote: Matthew Conlen - @mathisonian Beyond Notebooks: Computational Tools for Disseminating Research and Ideas |
| 10:30am | Closing |
Hall of Fame
Each year we award Best Submissions and Honorable Mentions. Congrats to our winners!
VISxAI 2023
Understanding and Comparing Multi-Modal Models Christina Humer, Vidya Prasad, Marc Streit, Hendrik Strobelt
Do Machine Learning Models Memorize or Generalize? Adam Pearce, Asma Ghandeharioun, Nada Hussein, Nithum Thain, Martin Wattenberg, Lucas Dixon
VISxAI 2022
K-Means Clustering: An Explorable Explainer Yi Zhe Ang
Poisoning Attacks and Subpopulation Susceptibility Evan Rose, Fnu Suya, David Evans
VISxAI 2021
What Have Language Models Learned? Adam Pearce
Feature Sonification: An investigation on the features learned for Automatic Speech Recognition Amin Ghiasi, Hamid Kazemi, W. Ronny Huang, Emily Liu, Micah Goldblum, Tom Goldstein
VISxAI 2020
Comparing DNNs with UMAP Tour Mingwei Li and Carlos Scheidegger
How Does a Computer "See" Gender? Stefan Wojcik, Emma Remy, and Chris Baronavski
VISxAI 2019
What if we Reduce the Memory of an Artificial Doom Player? Theo Jaunet, Romain Vuillemot, and Christian Wolf
Selecting the right tool for the job: a comparison of visualization algorithms Daniel Burkhardt, Scott Gigante, and Smita Krishnaswamy
VISxAI 2018
A Visual Exploration of Gaussian Processes Jochen Görtler, Rebecca Kehlbeck and Oliver Deussen
The Beginner's Guide to Dimensionality Reduction Matthew Conlen and Fred Hohman
Roads from Above Greg More, Slaven Marusic and Caihao Cui
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 and Reviewers
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