6th Workshop on
Visualization for AI Explainability

October 21st, 2023 at IEEE VIS 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 21, 2023, anywhere: Submission Deadline
September 1, 2023: Author Notification
September 22, 2023: Camera Ready Deadline
October 21st, 2023: Workshop in Oklahoma City at IEEE VIS 2022

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.

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).

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 https://distill.pub/2019/visual-exploration-gaussian-processes/.

Hall of Fame

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

VISxAI 2022
VISxAI 2021
VISxAI 2020
VISxAI 2019
VISxAI 2018

Organizers (alphabetic)

Alex Bäuerle - Sigma Computing
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