Name
Abstract | ||
|---|---|---|
As Artificial Intelligence (AI) technologies are increasingly used to make important decisions and perform autonomous tasks, providing explanations that allow users to understand the AI has become a ubiquitous concern in human-AI interaction. Recently, a number of open-source toolkits are making the growing collection of of Explainable AI (XAI) techniques accessible for researchers and practitioners to incorporate explanation features in AI systems. This course is open to anyone interested in implementing, designing and researching on the topic of XAI, aiming to provide an overview on the trends and methods of XAI and also help attendees gain hands-on experience of creating different styles of explanation with an XAI toolkit.
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Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3334480.3375044 | CHI '20: CHI Conference on Human Factors in Computing Systems
Honolulu
HI
USA
April, 2020 |
Keywords | DocType | ISBN |
AI, machine learning, explainable AI, explainability, human-AI interaction | Conference | 978-1-4503-6819-3 |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Q. Vera Liao | 1 | 173 | 25.59 |
| Moninder Singh | 2 | 381 | 105.12 |
| Yunfeng Zhang | 3 | 54 | 19.28 |
| Rachel K. E. Bellamy | 4 | 323 | 70.86 |