mlsanityData to 7 Oct 2026

Explanation in Artificial Intelligence: Insights from the Social Sciences

arXiv 1706.07269 ↗First posted 22 Jun 2017cs.AIcited by 1,201

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Cited by 29 in LLM agents and tool use, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

There has been a recent resurgence in the area of explainable artificial intelligence as researchers and practitioners seek to make their algorithms more understandable. Much of this research is focused on explicitly explaining decisions or actions to a human observer, and it should not be controversial to say that looking at how humans explain to each other can serve as a useful starting point for explanation in artificial intelligence. However, it is fair to say that most work in explainable artificial intelligence uses only the researchers' intuition of what constitutes a `good' explanation. There exists vast and valuable bodies of research in philosophy, psychology, and cognitive science of how people define, generate, select, evaluate, and present explanations, which argues that people employ certain cognitive biases and social expectations towards the explanation process. This paper argues that the field of explainable artificial intelligence should build on this existing research, and reviews relevant papers from philosophy, cognitive psychology/science, and social psychology, which study these topics. It draws out some important findings, and discusses ways that these can be infused with work on explainable artificial intelligence.

Key facts. Cited by 1,201 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Jun 2017.

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Citing papers per year: 2021: 216; 2022: 216; 2023: 219; 2024: 195; 2025: 199; 2026: 156.

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  1. 2021Ethical and social risks of harm from Language Modelscited by 957
  2. 2022STaR: Bootstrapping Reasoning With Reasoningcited by 895
  3. 2024Mechanistic Interpretability for AI Safety -- A Reviewcited by 415
  4. 2022From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AIcited by 299
  5. 2021Understanding Dataset Difficulty with V-Usable Informationcited by 288
  6. 2023Can Large Language Models Transform Computational Social Science?cited by 245
  7. 2021Polyjuice: Generating Counterfactuals for Explaining, Evaluating, and Improving Modelscited by 219
  8. 2021Trustworthy AI: From Principles to Practicescited by 206
  9. 2021Post-hoc Interpretability for Neural NLP: A Surveycited by 155
  10. 2021Explaining Answers with Entailment Treescited by 148
  11. 2023Interactive and Explainable Region-guided Radiology Report Generationcited by 139
  12. 2021Benchmarking and Survey of Explanation Methods for Black Box Modelscited by 136
38 more
  1. 2022Towards Faithful Model Explanation in NLP: A Surveycited by 129
  2. 2021Teach Me to Explain: A Review of Datasets for Explainable Natural Language Processingcited by 126
  3. 2022Toward Transparent AI: A Survey on Interpreting the Inner Structures of Deep Neural Networkscited by 126
  4. 2021Explainable AI (XAI): A Systematic Meta-Survey of Current Challenges and Future Opportunitiescited by 125
  5. 2021Expanding Explainability: Towards Social Transparency in AI systemscited by 124
  6. 2021Human-Centered Explainable AI (XAI): From Algorithms to User Experiencescited by 123
  7. 2023Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directionscited by 123
  8. 2024Black-Box Access is Insufficient for Rigorous AI Auditscited by 123
  9. 2021What Do We Want From Explainable Artificial Intelligence (XAI)? -- A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Researchcited by 119
  10. 2023AI Transparency in the Age of LLMs: A Human-Centered Research Roadmapcited by 119
  11. 2021CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networkscited by 118
  12. 2022A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainabilitycited by 118
  13. 2021Interpretable Deep Learning: Interpretation, Interpretability, Trustworthiness, and Beyondcited by 116
  14. 2022Explanations Can Reduce Overreliance on AI Systems During Decision-Makingcited by 111
  15. 2023Faithfulness Tests for Natural Language Explanationscited by 107
  16. 2024Relying on the Unreliable: The Impact of Language Models' Reluctance to Express Uncertaintycited by 106
  17. 2023Probabilistic Concept Bottleneck Modelscited by 101
  18. 2024Are self-explanations from Large Language Models faithful?cited by 101
  19. 2022Diffusion Visual Counterfactual Explanationscited by 96
  20. 2021Trustworthy AI: A Computational Perspectivecited by 93
  21. 2021A Comprehensive Taxonomy for Explainable Artificial Intelligence: A Systematic Survey of Surveys on Methods and Conceptscited by 90
  22. 2021The Out-of-Distribution Problem in Explainability and Search Methods for Feature Importance Explanationscited by 86
  23. 2022Towards Human-centered Explainable AI: A Survey of User Studies for Model Explanationscited by 84
  24. 2021If Only We Had Better Counterfactual Explanations: Five Key Deficits to Rectify in the Evaluation of Counterfactual XAI Techniquescited by 82
  25. 2021Explanations in Autonomous Driving: A Surveycited by 80
  26. 2024Visibility into AI Agentscited by 77
  27. 2021When Can Models Learn From Explanations? A Formal Framework for Understanding the Roles of Explanation Datacited by 75
  28. 2021Contrastive Explanations for Model Interpretabilitycited by 75
  29. 2021Explainable Artificial Intelligence for Autonomous Driving: A Comprehensive Overview and Field Guide for Future Research Directionscited by 73
  30. 2022GlanceNets: Interpretabile, Leak-proof Concept-based Modelscited by 73
  31. 2023Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanationscited by 73
  32. 2024Building Machines that Learn and Think with Peoplecited by 73
  33. 2021XProtoNet: Diagnosis in Chest Radiography with Global and Local Explanationscited by 72
  34. 2021A Survey on Interpretable Reinforcement Learningcited by 70
  35. 2021Argumentative XAI: A Surveycited by 66
  36. 2023Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven decision supportcited by 65
  37. 2021Beyond Expertise and Roles: A Framework to Characterize the Stakeholders of Interpretable Machine Learning and their Needscited by 64
  38. 2021GraphSVX: Shapley Value Explanations for Graph Neural Networkscited by 63

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