ExplainabilityTransparencyTrustworthinesscs.AI
Proceedings of The second international workshop on eXplainable AI for the Arts (XAIxArts)
Nick Bryan-Kinns, Corey Ford, Shuoyang Zheng, Helen Kennedy, Alan Chamberlain, Makayla Lewis, Drew Hemment, Zijin Li, Qiong Wu, Lanxi Xiao, Gus Xia, Jeba Rezwana, Michael Clemens, Gabriel Vigliensoni
June 20, 2024
Key Takeaways
I appreciate your interest, but I need to be transparent: the text you've provided is only a title and abstract for a workshop proceedings, not a full research paper with specific findings or data.
To provide accurate takeaways for a non-technical audience, I would need:
- The actual papers or research presented at the workshop
- Specific findings, case studies, or conclusions from those papers
- Data or evidence supporting key points
What I can tell you from the abstract alone:
- This workshop brought together experts in AI, design, and the arts to discuss how to make AI systems more understandable and transparent
- The focus was specifically on creative and artistic applications of AI, rather than other domains
- There's growing recognition that explainable AI (XAI) matters beyond technical fields—it's relevant to creative work too
If you have access to the full workshop papers or proceedings, I'd be happy to extract meaningful takeaways for a non-technical audience. Alternatively, if you can share specific papers or findings from the workshop, I can provide the factual, cited takeaways you're looking for.
Abstract
This second international workshop on explainable AI for the Arts (XAIxArts) brought together a community of researchers in HCI, Interaction Design, AI, explainable AI (XAI), and digital arts to explore the role of XAI for the Arts. Workshop held at the 16th ACM Conference on Creativity and Cognition (C&C 2024), Chicago, USA.