mlsanityData to 14 Oct 2026

How Human-Centered Explainable AI Interface Are Designed and Evaluated: A Systematic Survey

arXiv 2403.14496 ↗First posted 21 Mar 2024cs.HC cs.AIcited by 5

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Abstract (arXiv)

Despite its technological breakthroughs, eXplainable Artificial Intelligence (XAI) research has limited success in producing the em effective explanations needed by users. In order to improve XAI systems' usability, practical interpretability, and efficacy for real users, the emerging area of em Explainable Interfaces (EIs) focuses on the user interface and user experience design aspects of XAI. This paper presents a systematic survey of 53 publications to identify current trends in human-XAI interaction and promising directions for EI design and development. This is among the first systematic survey of EI research.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 35 of its 108 references that are in the map. First posted on arXiv 21 Mar 2024.

Builds on

35 of 108 references.

References · 35

  1. 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,552
  2. 2017Learning Important Features Through Propagating Activation Differencescited by 1,458
  3. 2017Towards A Rigorous Science of Interpretable Machine Learningcited by 1,416
  4. 2017Explanation in Artificial Intelligence: Insights from the Social Sciencescited by 1,206
  5. 2019A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAIcited by 404
  6. 2020Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performancecited by 399
  7. 2020Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Surveycited by 254
  8. 2020Questioning the AI: Informing Design Practices for Explainable AI User Experiencescited by 253
  9. 2020A Survey of the State of Explainable AI for Natural Language Processingcited by 206
  10. 2016Layer-wise Relevance Propagation for Neural Networks with Local Renormalization Layerscited by 199
  11. 2020Proxy Tasks and Subjective Measures Can Be Misleading in Evaluating Explainable AI Systemscited by 139
  12. 2021Expanding Explainability: Towards Social Transparency in AI systemscited by 124
23 more
  1. 2021Human-Centered Explainable AI (XAI): From Algorithms to User Experiencescited by 123
  2. 2020Drug discovery with explainable artificial intelligencecited by 118
  3. 2017Explainable AI: Beware of Inmates Running the Asylum Or: How I Learnt to Stop Worrying and Love the Social and Behavioural Sciencescited by 85
  4. 2020Explainable Artificial Intelligence: a Systematic Reviewcited by 79
  5. 2018How do Humans Understand Explanations from Machine Learning Systems? An Evaluation of the Human-Interpretability of Explanationcited by 72
  6. 2021Explainable Artificial Intelligence (XAI) on TimeSeries Data: A Surveycited by 72
  7. 2021Beyond Expertise and Roles: A Framework to Characterize the Stakeholders of Interpretable Machine Learning and their Needscited by 64
  8. 2021How can I choose an explainer? An Application-grounded Evaluation of Post-hoc Explanationscited by 58
  9. 2022Investigating Explainability of Generative AI for Code through Scenario-based Designcited by 47
  10. 2020A systematic review and taxonomy of explanations in decision support and recommender systemscited by 45
  11. 2021Designing AI for Trust and Collaboration in Time-Constrained Medical Decisions: A Sociotechnical Lenscited by 35
  12. 2021Explainable AI for Robot Failures: Generating Explanations that Improve User Assistance in Fault Recoverycited by 32
  13. 2021Towards Relatable Explainable AI with the Perceptual Processcited by 27
  14. 2021Human Perceptions on Moral Responsibility of AI: A Case Study in AI-Assisted Bail Decision-Makingcited by 23
  15. 2020Leveraging Rationales to Improve Human Task Performancecited by 20
  16. 2019On the Explanation of Machine Learning Predictions in Clinical Gait Analysiscited by 15
  17. 2021Model LineUpper: Supporting Interactive Model Comparison at Multiple Levels for AutoMLcited by 12
  18. 2021From Philosophy to Interfaces: an Explanatory Method and a Tool Inspired by Achinstein's Theory of Explanationcited by 8
  19. 2021Explainable AI and Adoption of Financial Algorithmic Advisors: an Experimental Studycited by 7
  20. 2017Toward Foraging for Understanding of StarCraft Agents: An Empirical Studycited by 4
  21. 2020Mediating Community-AI Interaction through Situated Explanation: The Case of AI-Led Moderationcited by 4
  22. 2018Living Without a Mobile Phone: An Autoethnographycited by 1
  23. 2022Towards Better User Requirements: How to Involve Human Participants in XAI Researchcited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 2.

5 papers.

  1. 2025LLMs for Explainable AI: A Comprehensive Surveycited by 45
  2. 2026"It became a self-fulfilling prophecy": How Lived Experiences are Entangled with AI Predictions in Menstrual Cycle Tracking Appscited by 1
  3. 2024TSFeatLIME: An Online User Study in Enhancing Explainability in Univariate Time Series Forecastingcited by 0
  4. 2025Preliminary Quantitative Study on Explainability and Trust in AI Systemscited by 0
  5. 2026Responsible AI in Businesscited by 0