8th Workshop on
Visualization for AI Explainability
November 2, 2025 at IEEE VIS in Vienna, Austria
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) Can Large Language Models Explain Their Internal Mechanisms? by Hussein, Ghandeharioun, Mullins, Reif, Wilson, Thain, Dixon (VISxAI 2024)
- (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
August 6, 2025, anywhere: Submission Deadline
September 8, 2025: Author Notification
October 1, 2025: Camera Ready Deadline
Program Overview
All times in CET (UTC +1).
| 9:00am | Welcome from the Organizers |
| 9:10 -- 10:30 | Session I: Lightning Talks Learning as Choosing a Loss Distribution Matthew J Holland LIME and SHAP Explained: From Computation to Interpretation Aeri Cho, Jeongmin Rhee, Seokhyeon Park, Jinwook Seo Transformer Explainer: LLM Transformer Model Visually Explained Cho, Aeree, Kim, Grace C., Karpekov, Alexander, Helbling, Alec, Wang, Zijie J., Lee, Seongmin, Hoover, Benjamin, Chau, Duen Horng The Mystery of In-Context Learning: How Transformers Learn Patterns Sundara Srivathsan, Lighittha PR, Prithivraj S, Suganya Ramamoorthy ICL‑Scope: Peering Inside In‑Context Learning with Real‑Time Interactive Visualisation Bhaskarjit Sarmah, Reetu Raj Harsh ESCAPE - Explaining Stable Diffusion via Cross Attention Maps and Prompt Editing Diego Zafferani, Giovanni De Muri, Johanna Hedlund Lindmar, Akmal Ashirmatov, Sinie van der Ben, Rita Sevastjanova, Mennatallah El-Assady Patch Explorer Imke Grabe, Jaden Fiotto-Kaufman, Rohit Gandikota, David Bau GFlowNet Playground - Theory and Examples for an Intuitive Understanding Florian Holeczek, Alexander Hillisch, Andreas Hinterreiter, Alex Hernández-García, Marc Streit, Christina Humer The Illustrated Evo2 Jared Wilber, Farhad Ramezanghorbani, Tyler Carter Shimko, John St John, David Romero |
| 10:30 -- 11:00 | Break |
| 11:00 -- 12:30 | Session II: VISxAI Unconf Come by to discuss what's hot in VIS + AI, meet new people, and build community! 1 hour: break outs. 30min: share outs. |
| 12:30 -- 2:00 | Lunch Break |
| 2:00 -- 3:30 | Session III: Fireside Chat (with original VISxAI Organizers!): Hendrik Strobelt, Adam Perer, Menna El-Assady How has the intersection of visualization and machine learning changed since the first VISxAI (2018)? |
| 3:30 -- 4:00 | Break |
| 4:00 -- 5:30 | Session IV: Closing Keynote: Martin Wattenberg - @wattenberg |
| 5:30 | Closing |
Call for Participation
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.
Hall of Fame
Each year we award Best Submissions and Honorable Mentions. Congrats to our winners!
VISxAI 2024
Can Large Language Models Explain Their Internal Mechanisms? Nada Hussein, Asma Ghandeharioun, Ryan Mullins, Emily Reif, Jimbo Wilson, Nithum Thain, Lucas Dixon
The Illustrated AlphaFold Elana P Simon, Jake Silberg
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 - Google DeepMind
Angie Boggust - Massachusetts Institute of Technology
Catherine Yeh - Harvard University
Fred Hohman - Apple
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
Lena Armstrong
Robin Chan
Jyotikrishna Dass
Zoe De Simone
Angus Forbes
Racquel Fygenson
Madeleine Grunde-McLaughlin
Alec Helbling
Aspen Hopkins
Seongmin Lee
Katelyn Morrison
Kushin Mukherjee
Nikhil Prakash
Emily Reif
Rita Sevastjanova
Venkatesh Sivaraman
James Wexler
Haoyang Yang