Nine Potential Pitfalls when Designing Human-AI Co-Creative Systems

By Daniel Buschek, Lukas Mecke, Florian Lehmann, Hai Dang. 2021

56 citations recorded by the source corpus.

This position paper examines potential pitfalls on the way towards achieving human-AI co-creation with generative models in a way that is beneficial to the users' interests. In particular, we collected a set of nine potential pitfalls, based on the literature and our own experiences as researchers working at the intersection of HCI and AI. We illustrate each pitfall with examples and suggest ideas for addressing it. Reflecting on all pitfalls, we discuss and conclude with implications for future research directions. With this collection, we hope to contribute to a critical and constructive discussion on the roles of humans and AI in co-creative interactions, with an eye on related assumptions and potential side-effects for creative practices and beyond.

Subfields: Computer Vision and Pattern Recognition, Safety, Robustness & Interpretability, Human-Computer Interaction

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Citations in the corpus

AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts (2021, background): “Prior work has noted core challenges of the interaction, such as a lack of transparency, controllability, and user agency [5, 13, 31].”
SynthBio: A Case Study in Human-AI Collaborative Curation of Text Datasets (2021, background): “This follows a rich line of research in human-model collaborative interfaces, in which models generate draft outputs that humans edit [22, 10, 28, 13, 24, 23].”