mlsanityData to 14 Oct 2026

Explainable Artificial Intelligence (XAI) from a user perspective- A synthesis of prior literature and problematizing avenues for future research

arXiv 2211.15343 ↗First posted 24 Nov 2022cs.AI cs.LGcited by 20

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Abstract (arXiv)

The final search query for the Systematic Literature Review (SLR) was conducted on 15th July 2022. Initially, we extracted 1707 journal and conference articles from the Scopus and Web of Science databases. Inclusion and exclusion criteria were then applied, and 58 articles were selected for the SLR. The findings show four dimensions that shape the AI explanation, which are format (explanation representation format), completeness (explanation should contain all required information, including the supplementary information), accuracy (information regarding the accuracy of the explanation), and currency (explanation should contain recent information). Moreover, along with the automatic representation of the explanation, the users can request additional information if needed. We have also found five dimensions of XAI effects: trust, transparency, understandability, usability, and fairness. In addition, we investigated current knowledge from selected articles to problematize future research agendas as research questions along with possible research paths. Consequently, a comprehensive framework of XAI and its possible effects on user behavior has been developed.

Key facts. Cited by 20 papers in the map (data through 14 Oct 2026). Builds on 17 of its 140 references that are in the map. First posted on arXiv 24 Nov 2022.

Builds on

17 of 140 references.

References · 17

  1. 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,552
  2. 2017Towards A Rigorous Science of Interpretable Machine Learningcited by 1,416
  3. 2016European Union regulations on algorithmic decision-making and a "right to explanation"cited by 344
  4. 2020Questioning the AI: Informing Design Practices for Explainable AI User Experiencescited by 253
  5. 2021Expanding Explainability: Towards Social Transparency in AI systemscited by 124
  6. 2018'It's Reducing a Human Being to a Percentage'; Perceptions of Justice in Algorithmic Decisionscited by 91
  7. 2019Automated Rationale Generation: A Technique for Explainable AI and its Effects on Human Perceptionscited by 81
  8. 2020Human Factors in Model Interpretability: Industry Practices, Challenges, and Needscited by 74
  9. 2019Towards Explainable Deep Neural Networks (xDNN)cited by 61
  10. 2021Understanding the Effect of Out-of-distribution Examples and Interactive Explanations on Human-AI Decision Makingcited by 58
  11. 2019Explainable artificial intelligence model to predict acute critical illness from electronic health recordscited by 42
  12. 2020Reviewing the Need for Explainable Artificial Intelligence (xAI)cited by 29
5 more
  1. 2019Experiences with Improving the Transparency of AI Models and Servicescited by 14
  2. 2019Outlining the Design Space of Explainable Intelligent Systems for Medical Diagnosiscited by 8
  3. 2021Explainable AI, but explainable to whom?cited by 7
  4. 2020Forecasting AI Progress: A Research Agendacited by 3
  5. 2021Insight about Detection, Prediction and Weather Impact of Coronavirus (Covid-19) using Neural Networkcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 7; 2024: 4; 2025: 6; 2026: 3.

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  1. 2023FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcarecited by 60
  2. 2024AI-Augmented Brainwriting: Investigating the use of LLMs in group ideationcited by 44
  3. 2023The future of human-centric eXplainable Artificial Intelligence (XAI) is not post-hoc explanationscited by 12
  4. 2023Why is plausibility surprisingly problematic as an XAI criterion?cited by 9
  5. 2024A Review of Multimodal Explainable Artificial Intelligence: Past, Present and Futurecited by 9
  6. 2024How explainable AI affects human performance: A systematic review of the behavioural consequences of saliency mapscited by 6
  7. 2023Human-Centered Evaluation of XAI Methodscited by 5
  8. 2023Explainable Artificial Intelligence for Drug Discovery and Development -- A Comprehensive Surveycited by 4
  9. 2025Context-Aware Visualization for Explainable AI Recommendations in Social Media: A Vision for User-Aligned Explanationscited by 1
  10. 2026"It became a self-fulfilling prophecy": How Lived Experiences are Entangled with AI Predictions in Menstrual Cycle Tracking Appscited by 1
  11. 2023Comparative Analysis of Contextual Relation Extraction based on Deep Learning Modelscited by 0
  12. 2023Notion of Explainable Artificial Intelligence -- An Empirical Investigation from A Users Perspectivecited by 0
8 more
  1. 2024Explainable AI: Definition and attributes of a good explanation for health AIcited by 0
  2. 2025Human Guided Learning of Transparent Regression Modelscited by 0
  3. 2025Direct Preference Optimization for Adaptive Concept-based Explanationscited by 0
  4. 2025Explanation User Interfaces: A Systematic Literature Reviewcited by 0
  5. 2025To Explain Or Not To Explain: An Empirical Investigation Of AI-Based Recommendations On Social Media Platformscited by 0
  6. 2025Towards a Transparent and Interpretable AI Model for Medical Image Classificationscited by 0
  7. 2026Privacy and Safety Experiences and Concerns of U.S. Women Using Generative AI for Seeking Sexual and Reproductive Health Informationcited by 0
  8. 2026Shaping Scientific Explanations to Expert Perspectives with Persona-Conditioned Reinforcement Learningcited by 0