mlsanityData to 7 Oct 2026

Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance

arXiv 2006.14779 ↗First posted 26 Jun 2020cs.AI cs.CL cs.HCcited by 396

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Cited by 31 in LLM agents and tool use, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Many researchers motivate explainable AI with studies showing that human-AI team performance on decision-making tasks improves when the AI explains its recommendations. However, prior studies observed improvements from explanations only when the AI, alone, outperformed both the human and the best team. Can explanations help lead to complementary performance, where team accuracy is higher than either the human or the AI working solo? We conduct mixed-method user studies on three datasets, where an AI with accuracy comparable to humans helps participants solve a task (explaining itself in some conditions). While we observed complementary improvements from AI augmentation, they were not increased by explanations. Rather, explanations increased the chance that humans will accept the AI's recommendation, regardless of its correctness. Our result poses new challenges for human-centered AI: Can we develop explanatory approaches that encourage appropriate trust in AI, and therefore help generate (or improve) complementary performance?

Key facts. Cited by 396 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Jun 2020.

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Citing papers per year: 2021: 23; 2022: 56; 2023: 68; 2024: 79; 2025: 68; 2026: 102.

396 papers.

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  1. 2021AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Promptscited by 265
  2. 2021Polyjuice: Generating Counterfactuals for Explaining, Evaluating, and Improving Modelscited by 219
  3. 2022Measuring Progress on Scalable Oversight for Large Language Modelscited by 212
  4. 2022The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspectivecited by 156
  5. 2022Towards Faithful Model Explanation in NLP: A Surveycited by 129
  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. 2023AI Transparency in the Age of LLMs: A Human-Centered Research Roadmapcited by 119
  9. 2021To Trust or to Think: Cognitive Forcing Functions Can Reduce Overreliance on AI in AI-assisted Decision-makingcited by 118
  10. 2024A Practical Review of Mechanistic Interpretability for Transformer-Based Language Modelscited by 113
  11. 2022Explanations Can Reduce Overreliance on AI Systems During Decision-Makingcited by 111
  12. 2023Complex Claim Verification with Evidence Retrieved in the Wildcited by 110
38 more
  1. 2024Rethinking Interpretability in the Era of Large Language Modelscited by 110
  2. 2024What Large Language Models Know and What People Think They Knowcited by 109
  3. 2024Relying on the Unreliable: The Impact of Language Models' Reluctance to Express Uncertaintycited by 106
  4. 2024When combinations of humans and AI are useful: A systematic review and meta-analysiscited by 100
  5. 2022Evaluating Human-Language Model Interactioncited by 90
  6. 2023Can Large Language Models Explain Themselves? A Study of LLM-Generated Self-Explanationscited by 90
  7. 2021The effectiveness of feature attribution methods and its correlation with automatic evaluation scorescited by 87
  8. 2021Towards a Science of Human-AI Decision Making: A Survey of Empirical Studiescited by 85
  9. 2022Towards Human-centered Explainable AI: A Survey of User Studies for Model Explanationscited by 84
  10. 2021If Only We Had Better Counterfactual Explanations: Five Key Deficits to Rectify in the Evaluation of Counterfactual XAI Techniquescited by 82
  11. 2023Appropriate Reliance on AI Advice: Conceptualization and the Effect of Explanationscited by 80
  12. 2022Interactive Concept Bottleneck Modelscited by 79
  13. 2023Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanationscited by 73
  14. 2022Human-AI Collaboration Enables More Empathic Conversations in Text-based Peer-to-Peer Mental Health Supportcited by 68
  15. 2023Who Should I Trust: AI or Myself? Leveraging Human and AI Correctness Likelihood to Promote Appropriate Trust in AI-Assisted Decision-Makingcited by 67
  16. 2024"I'm Not Sure, But...": Examining the Impact of Large Language Models' Uncertainty Expression on User Reliance and Trustcited by 67
  17. 2022Human-AI Collaboration via Conditional Delegation: A Case Study of Content Moderationcited by 66
  18. 2021Beyond Expertise and Roles: A Framework to Characterize the Stakeholders of Interpretable Machine Learning and their Needscited by 64
  19. 2022"Help Me Help the AI": Understanding How Explainability Can Support Human-AI Interactioncited by 63
  20. 2021Understanding the Effect of Out-of-distribution Examples and Interactive Explanations on Human-AI Decision Makingcited by 58
  21. 2023Bridging the Gap: A Survey on Integrating (Human) Feedback for Natural Language Generationcited by 55
  22. 2024Determinants of LLM-assisted Decision-Makingcited by 55
  23. 2022Do People Engage Cognitively with AI? Impact of AI Assistance on Incidental Learningcited by 53
  24. 2023Large Language Models Help Humans Verify Truthfulness -- Except When They Are Convincingly Wrongcited by 51
  25. 2023In Search of Verifiability: Explanations Rarely Enable Complementary Performance in AI-Advised Decision Makingcited by 50
  26. 2022Designing for Responsible Trust in AI Systems: A Communication Perspectivecited by 48
  27. 2023Steering Large Language Models for Machine Translation with Finetuning and In-Context Learningcited by 48
  28. 2022Human-Algorithm Collaboration: Achieving Complementarity and Avoiding Unfairnesscited by 45
  29. 2023RePrompt: Automatic Prompt Editing to Refine AI-Generative Art Towards Precise Expressionscited by 43
  30. 2022The Road to Explainability is Paved with Bias: Measuring the Fairness of Explanationscited by 41
  31. 2025Magentic-UI: Towards Human-in-the-loop Agentic Systemscited by 41
  32. 2022Connecting Algorithmic Research and Usage Contexts: A Perspective of Contextualized Evaluation for Explainable AIcited by 40
  33. 2023Knowing About Knowing: An Illusion of Human Competence Can Hinder Appropriate Reliance on AI Systemscited by 40
  34. 2024The Clever Hans Mirage: A Comprehensive Survey on Spurious Correlations in Machine Learningcited by 39
  35. 2025Fostering Appropriate Reliance on Large Language Models: The Role of Explanations, Sources, and Inconsistenciescited by 39
  36. 2024Human-AI Collaborative Essay Scoring: A Dual-Process Framework with LLMscited by 38
  37. 2024If in a Crowdsourced Data Annotation Pipeline, a GPT-4cited by 38
  38. 2024Towards Human-AI Deliberation: Design and Evaluation of LLM-Empowered Deliberative AI for AI-Assisted Decision-Makingcited by 38

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