How to Support Users in Understanding Intelligent Systems? Structuring the Discussion
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Abstract (arXiv)
The opaque nature of many intelligent systems violates established usability principles and thus presents a challenge for human-computer interaction. Research in the field therefore highlights the need for transparency, scrutability, intelligibility, interpretability and explainability, among others. While all of these terms carry a vision of supporting users in understanding intelligent systems, the underlying notions and assumptions about users and their interaction with the system often remain unclear. We review the literature in HCI through the lens of implied user questions to synthesise a conceptual framework integrating user mindsets, user involvement, and knowledge outcomes to reveal, differentiate and classify current notions in prior work. This framework aims to resolve conceptual ambiguity in the field and enables researchers to clarify their assumptions and become aware of those made in prior work. We thus hope to advance and structure the dialogue in the HCI research community on supporting users in understanding intelligent systems.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Jan 2020.
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Citing papers per year: 2021: 2; 2022: 5; 2023: 1; 2024: 1.
9 papers.
- 2022Designing Creative AI Partners with COFI: A Framework for Modeling Interaction in Human-AI Co-Creative Systemscited by 59
- 2021The Who in XAI: How AI Background Shapes Perceptions of AI Explanationscited by 48
- 2021Explainability Pitfalls: Beyond Dark Patterns in Explainable AIcited by 36
- 2023Charting the Sociotechnical Gap in Explainable AI: A Framework to Address the Gap in XAIcited by 33
- 2024Position: Towards Bidirectional Human-AI Alignmentcited by 15
- 2022GANSlider: How Users Control Generative Models for Images using Multiple Sliders with and without Feedforward Informationcited by 13
- 2022A Cognitive Framework for Delegation Between Error-Prone AI and Human Agentscited by 5
- 2022Modeling Human Behavior Part II -- Cognitive approaches and Uncertaintycited by 1
- 2022Modeling Human Behavior Part I -- Learning and Belief Approachescited by 1
- 2024Position: Towards Bidirectional Human-AI Alignment13 Jun 2024
- 2023Charting the Sociotechnical Gap in Explainable AI: A Framework to Address the Gap in XAI1 Feb 2023
- 2022Modeling Human Behavior Part I -- Learning and Belief Approaches13 May 2022
- 2022Modeling Human Behavior Part II -- Cognitive approaches and Uncertainty13 May 2022
- 2022Designing Creative AI Partners with COFI: A Framework for Modeling Interaction in Human-AI Co-Creative Systems15 Apr 2022
- 2022A Cognitive Framework for Delegation Between Error-Prone AI and Human Agents6 Apr 2022
- 2022GANSlider: How Users Control Generative Models for Images using Multiple Sliders with and without Feedforward Information2 Feb 2022
- 2021Explainability Pitfalls: Beyond Dark Patterns in Explainable AI26 Sep 2021
- 2021The Who in XAI: How AI Background Shapes Perceptions of AI Explanations28 Jul 2021