5th Workshop on
Visualization for AI Explainability

October 17th, 2022 at IEEE VIS in Oklahoma City, Oklahoma

VISxAI Logo

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.

Examples 2022
Example interactive visualization articles:

Important Dates

July 29, 2022, anywhere: Explainables Submission
August 29, 2022: Author Notification
October 17th, 2022: Workshop in Oklahoma City at IEEE VIS 2022

Program Overview

All times in ET (UTC -5).

2:00 -- 2:05
Welcome from the Organizers
2:05 -- 2:55
Keynote: Ian Johnson (Observable) - @enjalot
Towards a Pattern Language for Visualizing AI
2:55 -- 3:15
Session I
Action as Information
Paschalis Bizopoulos
3:15 -- 3:45
Break
3:45 -- 4:15
Session II
How is Real-World Gender Bias Reflected in Language Models?
Javier Rando, Alexander Theus, Rita Sevastjanova, Mennatallah El-Assady
What should we watch tonight?
Ibrahim Al-Hazwani, Gabriela Morgenshtern, Yves Rutishauser, Mennatallah El-Assady, Jürgen Bernard
4:15 -- 4:45
Session III
An Interactive Introduction to Causal Inference
Lucius E.J. Bynum, Falaah Arif Khan, Oleksandra Konopatska, Joshua Loftus, Julia Stoyanovich
4:45 -- 5:00
Closing Session

Hall of Fame

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

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
Statistical Distances and Their Implications to GAN Training Max Daniels
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)

Adam Perer - Carnegie Mellon University
Angie Boggust - Massachusetts Institute of Technology
Fred Hohman - Apple
Ian Johnson - Latent Interfaces
Hendrik Strobelt - MIT-IBM Watson AI Lab
Mennatallah El-Assady - ETH AI Center
Zijie Jay Wang - Georgia Tech
Steering Committee
Duen Horng (Polo) Chau - Georgia Tech
Fernanda Viégas - Google Brain

Program Committee and Reviewers

Soumya Dutta
Zhixuan Zhou
Marco Angelini
Jürgen Bernard
Alex Bäuerle
Jaegul Choo
Angus Forbes
Iris Howley
Romain Vuillemot
James Wexler
Donald Bertucci
Ángel Cabrera
Victor Dibia
Denis Parra
Rita Sevastjanova
Yang Wang