mlsanityData to 7 Oct 2026

Towards A Process Model for Co-Creating AI Experiences

arXiv 2104.07595 ↗First posted 15 Apr 2021cs.HC cs.AIcited by 18

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Abstract (arXiv)

Thinking of technology as a design material is appealing. It encourages designers to explore the material's properties to understand its capabilities and limitations, a prerequisite to generative design thinking. However, as a material, AI resists this approach because its properties emerge as part of the design process itself. Therefore, designers and AI engineers must collaborate in new ways to create both the material and its application experience. We investigate the co-creation process through a design study with 10 pairs of designers and engineers. We find that design 'probes' with user data are a useful tool in defining AI materials. Through data probes, designers construct designerly representations of the envisioned AI experience (AIX) to identify desirable AI characteristics. Data probes facilitate divergent thinking, material testing, and design validation. Based on our findings, we propose a process model for co-creating AIX and offer design considerations for incorporating data probes in design tools.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 5 of its 96 references that are in the map. First posted on arXiv 15 Apr 2021.

Builds on

5 of 96 references.

References · 5

  1. 2018Model Cards for Model Reportingcited by 1,068
  2. 2017NIMA: Neural Image Assessmentcited by 387
  3. 2018Improving fairness in machine learning systems: What do industry practitioners need?cited by 262
  4. 2019The Ethics of AI Ethics -- An Evaluation of Guidelinescited by 129
  5. 2019Data Changes Everything: Challenges and Opportunities in Data Visualization Design Handoffcited by 6

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Cited by year

Citing papers per year: 2022: 2; 2023: 10; 2024: 4; 2025: 2.

18 papers.

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  1. 2023The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practicecited by 86
  2. 2022Investigating Explainability of Generative AI for Code through Scenario-based Designcited by 47
  3. 2024AI-Augmented Brainwriting: Investigating the use of LLMs in group ideationcited by 43
  4. 2022Improving Human-AI Partnerships in Child Welfare: Understanding Worker Practices, Challenges, and Desires for Algorithmic Decision Supportcited by 37
  5. 2023Designerly Understanding: Information Needs for Model Transparency to Support Design Ideation for AI-Powered User Experiencecited by 18
  6. 2023Investigating Practices and Opportunities for Cross-functional Collaboration around AI Fairness in Industry Practicecited by 15
  7. 2023Jigsaw: Supporting Designers to Prototype Multimodal Applications by Chaining AI Foundation Modelscited by 11
  8. 2023fAIlureNotes: Supporting Designers in Understanding the Limits of AI Models for Computer Vision Taskscited by 10
  9. 2025AI Mismatches: Identifying Potential Algorithmic Harms Before AI Developmentcited by 10
  10. 2023Human-centered NLP Fact-checking: Co-Designing with Fact-checkers using Matchmaking for AIcited by 9
  11. 2024Unpacking Human-AI interactions: From interaction primitives to a design spacecited by 7
  12. 2023Is a Seat at the Table Enough? Engaging Teachers and Students in Dataset Specification for ML in Educationcited by 5
6 more
  1. 2023Grasping AI: experiential exercises for designerscited by 3
  2. 2024Developing and Evaluating a Design Method for Positive Artificial Intelligencecited by 2
  3. 2024The AI-DEC: A Card-based Design Method for User-centered AI Explanationscited by 2
  4. 2025Data-Prompt Co-Evolution: Growing Test Sets to Refine LLM Behaviorcited by 1
  5. 2023AwesomeMeta+: A Mixed-Prototyping Meta-Learning System Supporting AI Application Design Anywherecited by 0
  6. 2023Eliciting Model Steering Interactions from Users via Data and Visual Design Probescited by 0