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Generative AI and HCI is a Workshop as part of CHI 2022

  • Venue: Online
  • Workshop Date: 10 May 2022 (precise times TBA)
  • Submission Deadline: 9 March 2022 AoE
  • Submission Website: EasyChair
  • Submission Templates: ACM Template (4 pages max excluding references - Anonymous submissions preferred, but not required.)

Fooling Justin Bieber into picking a fight with Tom Cruise, completing Beethoven’s unfinished “SymphonyNo. 10”, and developing fake faces for stock photography are some of the results that Artificial Intelligence (AI) systems have recently brought to the world. What all of these examples have in common is that the AI does not simply categorize data and interpret text as determined by models, but instead it creates something new. This moves the purpose of AI systems from problem solving to problem finding. In this workshop we focus on various aspects of Generative AI (GenAI) and its interactions with humans, including the design of systems based on GenAI, ethical issues related to their design and use, and useful patterns for collaboration between humans and GenAI in different domains.


Session 1 10 May 2022 8:00-10:00 UTC



Angel Hsing-Chi Hwang. Why or Why Not: Barriers of Adopting Generative AI in Human-AI Co-Creativity

Corey Ford and Nick Bryan-Kinns. Speculating on Reflection and People’s Music Co-Creation with AI

Jeba Rezwana and Mary Lou Maher.  Identifying Ethical Issues in AI Partners in Human-AI Co-Creation

Imke Grabe and Jichen Zhu.  Towards Co-Creative Generative Adversarial Networks for Fashion Designers

Mary Lou Maher, Brian Magerko, Dan Venura, Douglas Fisher, Rogelio E. Cardona-Rivera, Nancy Fulda, John Gero, Minwoo Lee, David Wilson, James C. Kaufman, Maithilee Kunda, Michael Muller, Rachel K.E. Bellamy, Maya Ackerman, Evangelia Chrysikou.  A Research Plan for Integrating Generative and Cognitive AI for Human Centered, Explainable Co-Creative AI

Establishing Common Ground

Aaron Jackson, Vivian Liu and Lydia Chilton. Analyzing the Cultural Relevance of AI Generated Art

Rodolfo Ocampo, Oliver Bown and Kazjon Grace. A Framework for Dialogue-Based Human-AI Creative Collaboration

Coding and Co-Coding

Elizabeth Wilson, George Fazekas and Geraint Wiggins. Co-Creativity with a Musical AI Agent in Live Coding

Jakob Tholander and Martin Jonsson. Co-coding with AI in creative programming education


Bob Sturm. Generative AI helps one express things for which they may not have expressions (yet)

Mattia Gianotti, Andrea Rotella, Mariagiovanna Di Iorio, Fabiano Riccardi, Francesco Vona and Franca Garzotto. Emotional Synchrony and Generative Art in Interactive Multisensory Environments


Session 2 10 May 2022 16:00-18:00 UTC


Control or Not

Benedikte Wallace and Charles P Martin. Embodying the Glitch: Perspectives on Generative AI in Dance Practice

Daniel Bisig and Ephraim Wegner. Puppeteering AI - Interactive Control of an Artificial Dancer

Rupert Parry and Caroline Pegram. Perception of control in generative AI music user interfaces

Ryan Louie, Jesse Engel and Anna Huang. A Unified Evaluation of Expressive Generative Models andSteerable Interfaces for Music Creation


Frederic Gmeiner, Kenneth Holstein and Nikolas Martelaro. Team Learning as a Lens for Designing Human–AI Co-Creative Systems

Katy Gero, Vivian Liu and Lydia Chilton. Sparks: Inspiration for Science Writing using Language Models [

Small Data

Gabriel Vigliensoni, Phoenix Perry and Rebecca Fiebrink. A Small-Data Mindset for Generative AI Creative Work

Hai Dang, Lukas Mecke, Florian Lehmann, Sven Goller and Daniel Buschek. How to Prompt? Opportunities and Challenges of Zero- and Few-Shot Learning for Human-AI Interaction in Creative Applications of Generative Models


Carlos Hernandez-Olivan, Jorge Abadías Puyuelo and Jose R. Beltran. Subjective Evaluation of Deep Learning Models for Symbolic Music Composition

Katy Gero. How do we audit generative algorithms?


Andre Holzapfel, Petra Jääskeläinen and Anna-Kaisa Kaila. Environmental and Social Sustainability of Creative-Ai

Nur Yildirim. Emergent HCI Approaches to Envisioning with Generative AI Capabilities

Sarah Cooney. Generative AI as a Tool for Speculative Urban Futures


Call for Participation

Generative AI can be defined as an AI system that uses existing media to create new, plausible media. This scope is broad, and the generative potential of AI systems varies greatly. Over the last decade, we have seen a shift in methodology, moving from expert systems based on patterns and heavy human curating towards stochastic and generative models such as Generative Adversarial Networks (GANs) that use big data to produce convincingly human-like results in various domains.

This workshop applies human centered themes to a new and powerful technology, generative artificial intelligence (AI). Unlike AI systems that produce decisions or descriptions, generative AI systems produce new instances of types of data that can include images, texts, music, design, and motion. The results are often similar to results produced by humans.

However, it is not yet clear how humans can make sense of generative algorithms and outcomes. We have yet to understand what user interface technologies will enable humans to control, and more generally to interact with these powerful capabilities. These human-like capabilities put into question our current paradigms for mixed initiative user interfaces. Further, the unpredictability of “creative” algorithms raises new questions about how, when, and how much control humans may wish to share with these algorithms. Finally, it is not clear what kinds of collaboration patterns will emerge when creative humans and creative technologies work together.

In this one-day workshop, we will convene the interdisciplinary research domain of generative AI and HCI. Participation in this invitational workshop is open to seasoned scholars and early career researchers from diverse disciplines. We solicit descriptions of completed projects, works-in-progress, and provocations. Together we will develop theories and practices in this intriguing new domain.


Because of the far-reaching implications of Generative AI, we propose the following list of non-exhaustive, thematic questions to guide our discussions at the workshop:

  • What is generative AI and how can we leverage diverse definitions of it? Does generative AI go beyond intelligent interaction? What distinguishes generative AI?
  • How do you design in this space? What design patterns do we need to think about? How does uncertainty play into this and how to we help people set expectations to designing with AI?
  • Do generative AI algorithms contribute needed serendipity to the design process—or simply randomness—or worse, chaos?
  • Is presenting AI as a desirable and “objective” method appropriate for generative AI?

We encourage people to write and answer their own questions as well. We hope that the workshop leads to new ways-of-thinking. These themes can be addressed within the following topics:

  • The emerging capabilities of generative AI.
  • Generative AI existence in different domains including (but not limited to) images, music, text, design, and motion.
  • The role of generative AI in assisting, replacing, and regimenting human work.
  • Human-AI collaboration and co-creative systems.
  • Ethical issues including misuses and abuses, provenance, copyright, bias, and diversity.
  • The uncanny valley in Human-AI interactions.

As above, we encourage people to add new topics and domains.

Submissions may be up to four pages long (references may appear on additional pages), following the CHI 2022 instructions at https://chi2022.acm.org/for-authors/presenting/papers/chi-publication-formats/ .

The deadline for submissions is 9 March 2022 AoE (i.e., 23:59pm in the latest timezone on the planet).

Please make your submission via EasyChair at https://easychair.org/my/conference?conf=genaichi2022.

Please send any comments or questions to Michael Muller, michael_muller@us.ibm.com.


Lydia B. Chilton is an Assistant Professor in the Computer Science Department at Columbia University. She is an early pioneer in decomposing complex tasks so that crowds and computers can solve them together. Her current research is in computational design - how computation and AI can help people with design, innovation and creative problem solving. Applications include: conveying a message within an image for journalism and advertising, developing technology for public libraries, improving risk communication during hurricanes, and helping scientists explain their work on Twitter.

Anna Kantosalo is a Postdoctoral Researcher at the University of Helsinki. The focus of her research is Human–Computer Co-Creativity and she is defining models and methods for building and describing systems in which humans and autonomous creative agents can work together. She has chaired the Future of Co-Creative Systems workshop adjoined with the International Conference on Computational Creativity twice.

Mary Lou Maher is a Professor in the Software and Information Systems Department at the University of North Carolina at Charlotte. Her early research in AI-based generative design has lead to a human centered approach to computational creativity and co-creative systems. She has Chaired the Creativity and Cognition Conference (2019) and the International Conference on Computational Creativity (2019) as well as organized several workshops on AI-based design and creativity.

Charles Martin is a Lecturer in Computer Science at the Australian National University. Charles works at the intersection of music, AI/ML and HCI. He studies how humans can interact creatively with intelligent computing systems and how such systems might fit in the real world. Charles has organised multiple generative-AI-focused workshops at the New Interfaces for Musical Expression conference.

Michael Muller is a Research Scientist at IBM Research in Cambridge MA USA. With colleagues, he has analyzed how domain experts make use of generative AI outcomes, and how humans intervene between “the data” and “the model” as aspects of responsible and accountable data science work. He has co-organized workshops on human centered data science at CHI, CSCW, and GROUP conferences, and a workshop on human centered AI at a NeurIPS conference.

Greg Walsh is an associate professor at the University of Baltimore where he teaches courses in Design. He is an interaction design researcher who focuses on user-centered, inclusive design for children and adults. His work seeks to include more voices in the design process and has been a recipient of a prestigious Google Faculty Research Award. His work has included participatory design sessions in Baltimore City libraries and is now exploring the use of generative AI as a co-design partner.