mlsanityData to 7 Oct 2026

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

arXiv 2104.00358 ↗First posted 1 Apr 2021cs.HC cs.AIcited by 12

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Abstract (arXiv)

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.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 11 of its 33 references that are in the map. First posted on arXiv 1 Apr 2021.

Builds on

11 of 33 references.

References · 11

  1. 2019Analyzing and Improving the Image Quality of StyleGANcited by 3,580
  2. 2020Extracting Training Data from Large Language Modelscited by 2,144
  3. 2019Improved Precision and Recall Metric for Assessing Generative Modelscited by 925
  4. 2018The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networkscited by 847
  5. 2019Plug and Play Language Models: A Simple Approach to Controlled Text Generationcited by 793
  6. 2020GANSpace: Discovering Interpretable GAN Controlscited by 612
  7. 2020Understanding the Role of Individual Units in a Deep Neural Networkcited by 254
  8. 2019Predictive Biases in Natural Language Processing Models: A Conceptual Framework and Overviewcited by 137
  9. 2019Visual Interaction with Deep Learning Models through Collaborative Semantic Inferencecited by 24
  10. 2019An Interaction Framework for Studying Co-Creative AIcited by 19
  11. 2020Learning to See: You Are What You Seecited by 4

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Cited by year

Citing papers per year: 2021: 2; 2022: 0; 2023: 5; 2024: 4; 2025: 1.

12 papers.

  1. 2021AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Promptscited by 265
  2. 2024Homogenization Effects of Large Language Models on Human Creative Ideationcited by 127
  3. 2024LAVE: LLM-Powered Agent Assistance and Language Augmentation for Video Editingcited by 47
  4. 2023Charting the Sociotechnical Gap in Explainable AI: A Framework to Address the Gap in XAIcited by 33
  5. 2023Interactive AI Alignment: Specification, Process, and Evaluation Alignmentcited by 22
  6. 2021SynthBio: A Case Study in Human-AI Collaborative Curation of Text Datasetscited by 18
  7. 2024PersonaFlow: Designing LLM-Simulated Expert Perspectives for Enhanced Research Ideationcited by 16
  8. 2023fAIlureNotes: Supporting Designers in Understanding the Limits of AI Models for Computer Vision Taskscited by 10
  9. 2024VRCopilot: Authoring 3D Layouts with Generative AI Models in VRcited by 10
  10. 2023An Ontology of Co-Creative AI Systemscited by 7
  11. 2025Exploring the Needs of Practising Musicians in Co-Creative AI Through Co-Designcited by 3
  12. 2023CHAI-DT: A Framework for Prompting Conversational Generative AI Agents to Actively Participate in Co-Creationcited by 0