mlsanityData to 14 Oct 2026

Designerly Understanding: Information Needs for Model Transparency to Support Design Ideation for AI-Powered User Experience

arXiv 2302.10395 ↗First posted 21 Feb 2023cs.HC cs.AIcited by 18

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Abstract (arXiv)

Despite the widespread use of artificial intelligence (AI), designing user experiences (UX) for AI-powered systems remains challenging. UX designers face hurdles understanding AI technologies, such as pre-trained language models, as design materials. This limits their ability to ideate and make decisions about whether, where, and how to use AI. To address this problem, we bridge the literature on AI design and AI transparency to explore whether and how frameworks for transparent model reporting can support design ideation with pre-trained models. By interviewing 23 UX practitioners, we find that practitioners frequently work with pre-trained models, but lack support for UX-led ideation. Through a scenario-based design task, we identify common goals that designers seek model understanding for and pinpoint their model transparency information needs. Our study highlights the pivotal role that UX designers can play in Responsible AI and calls for supporting their understanding of AI limitations through model transparency and interrogation.

Key facts. Cited by 18 papers in the map (data through 14 Oct 2026). Builds on 40 of its 106 references that are in the map. First posted on arXiv 21 Feb 2023.

Builds on

40 of 106 references.

References · 40

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
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28 more
  1. 2020Questioning the AI: Informing Design Practices for Explainable AI User Experiencescited by 253
  2. 2022CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilitiescited by 249
  3. 2019Explainable Machine Learning in Deploymentcited by 211
  4. 2021Human-Centered Explainable AI (XAI): From Algorithms to User Experiencescited by 123
  5. 2020Participation is not a Design Fix for Machine Learningcited by 111
  6. 2021Robustness Gym: Unifying the NLP Evaluation Landscapecited by 108
  7. 2020Human Factors in Model Interpretability: Industry Practices, Challenges, and Needscited by 74
  8. 2022An Empirical Survey on Long Document Summarization: Datasets, Models and Metricscited by 74
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 4; 2024: 2; 2025: 6; 2026: 6.

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  1. 2023AI Transparency in the Age of LLMs: A Human-Centered Research Roadmapcited by 123
  2. 2024Red-Teaming for Generative AI: Silver Bullet or Security Theater?cited by 83
  3. 2023Rethinking Model Evaluation as Narrowing the Socio-Technical Gapcited by 56
  4. 2025Understanding the LLM-ification of CHI: Unpacking the Impact of LLMs at CHI through a Systematic Literature Reviewcited by 16
  5. 2023Investigating Practices and Opportunities for Cross-functional Collaboration around AI Fairness in Industry Practicecited by 15
  6. 2025Taxonomy of User Needs and Actionscited by 15
  7. 2024VRCopilot: Authoring 3D Layouts with Generative AI Models in VRcited by 10
  8. 2023PromptInfuser: How Tightly Coupling AI and UI Design Impacts Designers' Workflowscited by 8
  9. 2025Attention to Non-Adopterscited by 4
  10. 2025Prototyping Multimodal GenAI Real-Time Agents with Counterfactual Replays and Hybrid Wizard-of-Ozcited by 2
  11. 2026Keeping an Eye on AI: A Framework for Effective Human Oversight of AI Systemscited by 2
  12. 2026Situated, Dynamic, and Subjective: Envisioning the Design of Theory-of-Mind-Enabled Everyday AI with Industry Practitionerscited by 1
6 more
  1. 2026LitPivot: Developing Well-Situated Research Ideas Through Dynamic Contextualization and Critique within the Literature Landscapecited by 1
  2. 2025Exploring the Innovation Opportunities for Pre-trained Modelscited by 0
  3. 2025PolicyPad: Collaborative Prototyping of LLM Policiescited by 0
  4. 2026Emergent, not Immanent: A Baradian Reading of Explainable AIcited by 0
  5. 2026"So There's a Catch-22 Here": How Early Adopters Who Build Multi-Agent LLM Systems Conceptualize Transparencycited by 0
  6. 2026What We Know about Responsible AI Practices in Industry: A Half Decade of Empirical Researchcited by 0