October 21st, 2023 at IEEE VIS in Melbourne, Australia (+ online)
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.
July 21, 2023, anywhere: Submission Deadline September 1, 2023: Author Notification September 22, 2023: Camera Ready Deadline October 21st, 2023: Workshop in Melbourne at IEEE VIS 2023
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.
Our workshop will be hybrid. We encourage and accept submissions for those who cannot travel to VIS in person.
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).
Alex Bäuerle - Sigma Computing
Angie Boggust - Massachusetts Institute of Technology
Fred Hohman - Apple
Ian Johnson - Latent Interfaces
Zijie Jay Wang - Georgia Tech
Adam Perer - Carnegie Mellon University
Hendrik Strobelt - MIT-IBM Watson AI Lab
Mennatallah El-Assady - ETH AI Center