7th Workshop on
Visualization for AI Explainability

October 13, 2024 at IEEE VIS in St. Pete Beach, Florida

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) PAC Learning Or: Why We Should (and Shouldn't) Trust Machine Learning by Cashman (VISxAI 2023); (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, 2024, anywhere: Submission Deadline
September 10, 2024: Author Notification
October 1, 2024: Camera Ready Deadline
October 13, 2024 - Morning Session (ET)
    

Program Overview

Coming soon!


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).

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

Steering Committee

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

Program Committee

Coming soon!