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Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI

arXiv 1910.10045 ↗First posted 22 Oct 2019cs.AI cs.LG cs.NEcited by 1,543

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Abstract (arXiv)

In the last years, Artificial Intelligence (AI) has achieved a notable momentum that may deliver the best of expectations over many application sectors across the field. For this to occur, the entire community stands in front of the barrier of explainability, an inherent problem of AI techniques brought by sub-symbolism (e.g. ensembles or Deep Neural Networks) that were not present in the last hype of AI. Paradigms underlying this problem fall within the so-called eXplainable AI (XAI) field, which is acknowledged as a crucial feature for the practical deployment of AI models. This overview examines the existing literature in the field of XAI, including a prospect toward what is yet to be reached. We summarize previous efforts to define explainability in Machine Learning, establishing a novel definition that covers prior conceptual propositions with a major focus on the audience for which explainability is sought. We then propose and discuss about a taxonomy of recent contributions related to the explainability of different Machine Learning models, including those aimed at Deep Learning methods for which a second taxonomy is built. This literature analysis serves as the background for a series of challenges faced by XAI, such as the crossroads between data fusion and explainability. Our prospects lead toward the concept of Responsible Artificial Intelligence, namely, a methodology for the large-scale implementation of AI methods in real organizations with fairness, model explainability and accountability at its core. Our ultimate goal is to provide newcomers to XAI with a reference material in order to stimulate future research advances, but also to encourage experts and professionals from other disciplines to embrace the benefits of AI in their activity sectors, without any prior bias for its lack of interpretability.

Key facts. Cited by 1,543 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Oct 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 226; 2022: 281; 2023: 303; 2024: 290; 2025: 263; 2026: 180.

1,543 papers.

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  1. 2021Towards Personalized Federated Learningcited by 462
  2. 2022From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AIcited by 299
  3. 2023Driving with LLMs: Fusing Object-Level Vector Modality for Explainable Autonomous Drivingcited by 231
  4. 2021Trustworthy AI: From Principles to Practicescited by 206
  5. 2023LanguageMPC: Large Language Models as Decision Makers for Autonomous Drivingcited by 188
  6. 2021A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challengescited by 154
  7. 2023LingoQA: Visual Question Answering for Autonomous Drivingcited by 144
  8. 2021Benchmarking and Survey of Explanation Methods for Black Box Modelscited by 136
  9. 2022Towards Faithful Model Explanation in NLP: A Surveycited by 129
  10. 2021Teach Me to Explain: A Review of Datasets for Explainable Natural Language Processingcited by 126
  11. 2021Explainable AI (XAI): A Systematic Meta-Survey of Current Challenges and Future Opportunitiescited by 125
  12. 2022OpenXAI: Towards a Transparent Evaluation of Model Explanationscited by 125
38 more
  1. 2021Expanding Explainability: Towards Social Transparency in AI systemscited by 124
  2. 2021Human-Centered Explainable AI (XAI): From Algorithms to User Experiencescited by 123
  3. 2023Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directionscited by 123
  4. 2021Acquisition of Chess Knowledge in AlphaZerocited by 120
  5. 2021What Do We Want From Explainable Artificial Intelligence (XAI)? -- A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Researchcited by 119
  6. 2021Truthful AI: Developing and governing AI that does not liecited by 109
  7. 2023Artificial Intelligence for Digital and Computational Pathologycited by 106
  8. 2021HIVE: Evaluating the Human Interpretability of Visual Explanationscited by 105
  9. 2021What I Cannot Predict, I Do Not Understand: A Human-Centered Evaluation Framework for Explainability Methodscited by 98
  10. 2023Concept-based Explainable Artificial Intelligence: A Surveycited by 97
  11. 2021Trustworthy AI: A Computational Perspectivecited by 93
  12. 2021Provably efficient machine learning for quantum many-body problemscited by 91
  13. 2021A Comprehensive Taxonomy for Explainable Artificial Intelligence: A Systematic Survey of Surveys on Methods and Conceptscited by 90
  14. 2021A Comprehensive Study on Face Recognition Biases Beyond Demographicscited by 86
  15. 2023Machine Unlearning: Solutions and Challengescited by 85
  16. 2021e-ViL: A Dataset and Benchmark for Natural Language Explanations in Vision-Language Taskscited by 84
  17. 2022Towards Human-centered Explainable AI: A Survey of User Studies for Model Explanationscited by 84
  18. 2021Transparency of Deep Neural Networks for Medical Image Analysis: A Review of Interpretability Methodscited by 83
  19. 2024Beyond Accuracy: Evaluating the Reasoning Behavior of Large Language Models -- A Surveycited by 82
  20. 2021Explanations in Autonomous Driving: A Surveycited by 80
  21. 2021Unbox the Black-box for the Medical Explainable AI via Multi-modal and Multi-centre Data Fusion: A Mini-Review, Two Showcases and Beyondcited by 75
  22. 2021Explainable Artificial Intelligence for Autonomous Driving: A Comprehensive Overview and Field Guide for Future Research Directionscited by 73
  23. 2023Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanationscited by 73
  24. 2024Detecting Multimedia Generated by Large AI Models: A Surveycited by 73
  25. 2021Explaining Time Series Predictions with Dynamic Maskscited by 72
  26. 2021Explainable Artificial Intelligence (XAI) on TimeSeries Data: A Surveycited by 71
  27. 2023Large Language Models and Knowledge Graphs: Opportunities and Challengescited by 71
  28. 2021A Survey on Interpretable Reinforcement Learningcited by 70
  29. 2023Who Should I Trust: AI or Myself? Leveraging Human and AI Correctness Likelihood to Promote Appropriate Trust in AI-Assisted Decision-Makingcited by 67
  30. 2023X-Pruner: eXplainable Pruning for Vision Transformerscited by 66
  31. 2022"Help Me Help the AI": Understanding How Explainability Can Support Human-AI Interactioncited by 63
  32. 2021NICE: An Algorithm for Nearest Instance Counterfactual Explanationscited by 62
  33. 2022Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysiscited by 62
  34. 2022Interactive Model Cards: A Human-Centered Approach to Model Documentationcited by 61
  35. 2022Concept Embedding Models: Beyond the Accuracy-Explainability Trade-Offcited by 61
  36. 2023Connecting the Dots in Trustworthy Artificial Intelligence: From AI Principles, Ethics, and Key Requirements to Responsible AI Systems and Regulationcited by 60
  37. 2021Counterfactuals and Causability in Explainable Artificial Intelligence: Theory, Algorithms, and Applicationscited by 59
  38. 2021Synthetic Benchmarks for Scientific Research in Explainable Machine Learningcited by 59

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