HAI-GEN2020: IUI 2020 Workshop on Human-AI Co-Creation with Generative Models |
Website | https://hai-gen2020.github.io/ |
Submission link | https://easychair.org/conferences/?conf=haigen2020 |
Submission deadline | December 20, 2019 |
HAI-GEN 2020 - IUI 2020 Workshop on Human-AI Co-Creation with Generative Models
Cagliari, Italy, Tuesday, March 17
https://hai-gen2020.github.io/
Contact: hai-gen2020@service.microsoft.com
*** Paper & Demo Submission Deadline: December 20, 2019 ***
INTRODUCTION
Recent advances in generative modeling through deep learning approaches such as generative adversarial networks (GANs), variational autoencoders (VAEs), and sequence-to-sequence models will enable new kinds of user experiences around content creation, giving us “creative superpowers.” While the areas of computational design, generative design, and computational art have existed for some time, recent breakthroughs in deep learning have demonstrated examples of AI-generated content indistinguishable from human-generated content. These examples --- such as deep fake videos --- also highlight some of the significant societal, ethical and organizational challenges generative AI is posing including security, privacy, ownership, quality metrics and evaluation of generated content.
The goal of this half-day workshop is to bring together researchers and practitioners from both fields HCI and AI to explore the opportunities and challenges of generative modelling from an HCI perspective. We envision that the user experience of creating both physical and digital artifacts will become a partnership of humans and AI: humans will take the role of specification, goal setting, steering, high-level creativity, curation, and governance. AI will augment human abilities through inspiration, low level creativity and detail work, and the ability to test ideas at scale.
SUBMISSION GUIDELINES
We are accepting submissions in the form of short papers (4 pages), long papers (10 pages), and demos following the IUI guidelines for papers (https://iui.acm.org/2020/call_for_papers.html) and demos. (https://iui.acm.org/2020/call_for_demo_poster.html).
Encouraged topics related to generative modelling include but are not limited to:
* Novel user experiences supporting the creation of both physical and digital artifacts in an AI augmented fashion
* Business use cases of generative models
* Novel applications of generative models
* Techniques, methodologies, and algorithms that enable new user experiences and interactions with generative models and allow for directed and purposeful manipulation of the model output
* Governance, privacy, content ownership, societal impact
* Security including forensic tools and approaches for deep fake detection
* Evaluation of generative approaches and quality metrics
* Lessons learned from computational art and design, and generative design and how these impact research
All papers will be peer reviewed, single blind (i.e. author names and affiliations should be listed). If accepted, at least one of the authors must attend the workshop to present the work.
A workshop summary will be included in the ACM Digital Library for IUI 2020. While papers and demos are not part of the archival ACM IUI proceedings, we will be published them online at CEUR: http://ceur-ws.org/
Please submit your papers & demos to EasyChair: https://easychair.org/my/conference?conf=haigen2020#
IMPORTANT DATES
Paper & Demo Submission Deadline: December 20, 2019 AoE
Acceptance Notifications: January 14, 2020
Camera-Ready Submission Date: February 18, 2020
Workshop date: March 17, 2020
KEYNOTE SPEAKER
Douglas Eck, Google AI, Magenta Team
ORGANIZERS
Werner Geyer, IBM Research AI
Lydia B. Chilton, Columbia University
Ranjitha Kumar, University of Illinois at Urbana-Champaign
Adam Tauman Kalai, Microsoft Research
PROGRAM COMMITTEE
Nancy Baym, Microsoft Research
Zoya Bylinskii, Adobe Research
Carrie Cai, Google
Peter Daalsgard, Aarhus University
Sebastian Gehrmann, Harvard School of Engineering
Katy Gero, Columbia University
Per Ola Kristensson, University of Cambridge
Jacquelyn Martino, IBM Research AI
Mauro Martino, IBM Research AI
Michael Mateas, University of California, Santa Cruz
Antii Oulasvirta, Aalto University
Dafna Shahaf, Hebrew University of Jerusalem
Akash Srivastava, IBM Research AI
Hendrik Strobelt, IBM Research AI
Michael Terry, Google
Steven Wu, University of Minnesota
Haiyi Zhu, Carnegie Mellon University