1st Workshop on
Visualization for AI Explainability
October 22, 2018 at IEEE VIS in Berlin, Germany
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) Why Momentum Really Works by Gabriel Goh
- (b) Tensorflow Playground by Smilkov, Carter, et.al.
- (c) FormaFluens Data Experiment by Strobelt, Phibbs, Martino
Important Dates
July 12, 2018: Blog/Notebooks + Position Paper Submission
August 2, 2018: Author Notification
September 3, 2018: Camera-ready Copy for Accepted Submissions
September 7, 2018: VIS Early Bird Registration Ends
October 22, 2018: Workshop in Berlin at IEEE VIS 2018
Program Overview
All times in ET (UTC -5).
| 2:20 -- 2:25 | Welcome from the Organizers |
| 2:25 -- 3:10 | Keynote: Been Kim (Google Brain) Towards Interpretability for Everyone: Testing with Concept Activation Vectors (TCAV) - The ultimate goal of interpretability is to help users gain insights into the model for more responsible use of ML. Unlike the majority of subfields in ML, interpretable ML requires studying how humans parse complex information and exploring effective ways to communicate such information. This human aspect becomes even more critical when developing interpretability methods for non-ML experts/layer users --- my core research agenda. |
| 3:10 -- 3:35 | Session I: Neural Networks and Deep Learning Visualising State Space Representations of Long Short-Term Memory Networks Emmanuel M. Smith, Jim Smith, Phil Legg and Simon Francis Visualizing neuron activations of neural networks with the grand tour Mingwei Li, Zhenge Zhao and Carlos Scheidegger |
| 3:35 -- 4:00 | Session II: Projections and Dimensionality Reduction Roads from Above Greg More, Slaven Marusic and Caihao Cui The Beginner's Guide to Dimensionality Reduction Matthew Conlen and Fred Hohman Dimension, Distances, or Neighborhoods? Projection Literacy for the Analysis of Multivariate Data Dirk Streeb, Rebecca Kehlbeck, Dominik Jäckle and Mennatallah El-Assady |
| 4:00 -- 4:20 | Coffee Break with Poster Session |
| 4:20 -- 4:45 | Session III: Data Distribution and Bias A Visual Exploration of Gaussian Processes Jochen Görtler, Rebecca Kehlbeck and Oliver Deussen Towards an Interpretable Latent Space Thilo Spinner, Jonas Körner, Jochen Görtler and Oliver Deussen Understanding Bias in Machine Learning Jindong Gu and Daniela Oelke |
| 4:45 -- 5:10 | Session IV: Machine Learning Processes and Explanation Strategies Minions, Sheep, and Fruits: Metaphorical Narratives to Explain Artificial Intelligence and Build Trust Wolfgang Jentner, Rita Sevastjanova, Florian Stoffel, Daniel Keim, Jurgen Bernard and Mennatallah El-Assady Aimacode Javascript - Minimax Michael Kawano Going beyond Visualization: Verbalization as Complementary Medium to Explain Machine Learning Models Rita Sevastjanova, Fabian Beck, Basil Ell, Cagatay Turkay, Rafael Henkin, Miriam Butt, Daniel Keim and Mennatallah El-Assady |
| 5:10 -- 5:55 | Moderated Panel Discussion |
| 5:55 -- 6:00 | Best submission ceremony and "Auf Wiedersehen" :) |
| 8:00 | VISxAI Eastcoast party |
Hall of Fame
Each year we award Best Submissions and Honorable Mentions. Congrats to our winners!
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)
Mennatallah El-Assady - University of Konstanz
Duen Horng (Polo) Chau - Georgia Tech
Adam Perer - Carnegie Mellon University
Hendrik Strobelt - IBM Research, MIT-IBM Watson AI Lab
Fernanda Viégas - Google Brain
Steering Committee
Program Committee and Reviewers
Adam Perer
Alexander Rush
Arvind Satyanarayan
Brady Redfearn
Carlos Scheidegger
Jaegul Choo
Christian Bors
Christopher Collins
David Bau
Duen Horng (Polo) Chau
Dustin Arendt
Dylan Cashman
Lana El Sanyoura
Fernanda Viégas
Fred Hohman
Hendrik Strobelt
Iris Howley
Juergen Bernard
Kanit Wongsuphasawat
Martin Wattenberg
Matthew Conlen
Mennatallah El-Assady
Minsuk Kahng
Rita Borgo
Sebastian Gehrmann
Tommy Dang
Yamini Bansal
Yang Wang