Jeff Huang - Reshaping the Creative Process through Peer Review, Media Formats, and Copyright
Host
Arvind Satyanarayan
CSAIL
Abstract:
Digital artists compose and share their work on online platforms that are meant to support creativity. Recently, collaboration and AI tools have been the primary new features in these platforms, enabling teams to do more, with or without artists. I propose shifting the focus back towards the individual artist by exploring three aspects of these platforms: peer review, media formats, and copyright. I share examples of platforms, Sketchy and UX Factor, incorporating peer review that provides self-assessment along with peer feedback for the artist or designer. Filtered.ink extends an existing media format to enable new capabilities for the artist without depending on the platform or proprietary format. Finally, I propose a workflow for generative AI platforms that can support artists' authorship of the finished work based on the idea-expression doctrine in copyright law.
Bio:
Jeff Huang is an Associate Professor and Associate Chair of Computer Science at Brown University. His research is in Human-Computer Interaction, focusing on building personalized systems based on behavior data. These systems enable new user-centric capabililties and are applied to attention, mobile, user experience, and health. Jeff is primarily funded by the NSF, NIH, and ARO, and he has received the NSF CAREER award, Facebook Fellowship, and ARO Young Investigator Award.
This talk will also be streamed over Zoom: https://mit.zoom.us/j/99877634306.
Digital artists compose and share their work on online platforms that are meant to support creativity. Recently, collaboration and AI tools have been the primary new features in these platforms, enabling teams to do more, with or without artists. I propose shifting the focus back towards the individual artist by exploring three aspects of these platforms: peer review, media formats, and copyright. I share examples of platforms, Sketchy and UX Factor, incorporating peer review that provides self-assessment along with peer feedback for the artist or designer. Filtered.ink extends an existing media format to enable new capabilities for the artist without depending on the platform or proprietary format. Finally, I propose a workflow for generative AI platforms that can support artists' authorship of the finished work based on the idea-expression doctrine in copyright law.
Bio:
Jeff Huang is an Associate Professor and Associate Chair of Computer Science at Brown University. His research is in Human-Computer Interaction, focusing on building personalized systems based on behavior data. These systems enable new user-centric capabililties and are applied to attention, mobile, user experience, and health. Jeff is primarily funded by the NSF, NIH, and ARO, and he has received the NSF CAREER award, Facebook Fellowship, and ARO Young Investigator Award.
This talk will also be streamed over Zoom: https://mit.zoom.us/j/99877634306.