mlsanityData to 7 Oct 2026

Towards Effective Human-AI Decision-Making: The Role of Human Learning in Appropriate Reliance on AI Advice

arXiv 2310.02108 ↗First posted 3 Oct 2023cs.AI cs.HCcited by 8

Not on the map. Why?

Abstract (arXiv)

The true potential of human-AI collaboration lies in exploiting the complementary capabilities of humans and AI to achieve a joint performance superior to that of the individual AI or human, i.e., to achieve complementary team performance (CTP). To realize this complementarity potential, humans need to exercise discretion in following AI 's advice, i.e., appropriately relying on the AI's advice. While previous work has focused on building a mental model of the AI to assess AI recommendations, recent research has shown that the mental model alone cannot explain appropriate reliance. We hypothesize that, in addition to the mental model, human learning is a key mediator of appropriate reliance and, thus, CTP. In this study, we demonstrate the relationship between learning and appropriate reliance in an experiment with 100 participants. This work provides fundamental concepts for analyzing reliance and derives implications for the effective design of human-AI decision-making.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 27 of its 70 references that are in the map. First posted on arXiv 3 Oct 2023.

Builds on

27 of 70 references.

References · 27

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  3. 2014Going Deeper with Convolutionscited by 4,231
  4. 2020Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performancecited by 396
  5. 2019Counterfactual Visual Explanationscited by 312
  6. 2020Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Makingcited by 281
  7. 2018Manipulating and Measuring Model Interpretabilitycited by 256
  8. 2018On Human Predictions with Explanations and Predictions of Machine Learning Models: A Case Study on Deception Detectioncited by 159
  9. 2020Proxy Tasks and Subjective Measures Can Be Misleading in Evaluating Explainable AI Systemscited by 138
  10. 2020"Why is 'Chicago' deceptive?" Towards Building Model-Driven Tutorials for Humanscited by 86
  11. 2023Appropriate Reliance on AI Advice: Conceptualization and the Effect of Explanationscited by 80
  12. 2018An Overview of Machine Teachingcited by 63
15 more
  1. 2021Understanding the Effect of Out-of-distribution Examples and Interactive Explanations on Human-AI Decision Makingcited by 58
  2. 2021Hybrid Intelligencecited by 55
  3. 2020SCOUT: Self-aware Discriminant Counterfactual Explanationscited by 52
  4. 2020Does Explainable Artificial Intelligence Improve Human Decision-Making?cited by 49
  5. 2022Visual correspondence-based explanations improve AI robustness and human-AI team accuracycited by 37
  6. 2020The Role of Domain Expertise in User Trust and the Impact of First Impressions with Intelligent Systemscited by 34
  7. 2014Near-Optimally Teaching the Crowd to Classifycited by 32
  8. 2022Making Heads or Tails: Towards Semantically Consistent Visual Counterfactualscited by 30
  9. 2022A Meta-Analysis of the Utility of Explainable Artificial Intelligence in Human-AI Decision-Makingcited by 29
  10. 2022Should I Follow AI-based Advice? Measuring Appropriate Reliance in Human-AI Decision-Makingcited by 28
  11. 2020DEAL: Deep Evidential Active Learning for Image Classificationcited by 22
  12. 2022Investigations of Performance and Bias in Human-AI Teamwork in Hiringcited by 16
  13. 2020Do you comply with AI? -- Personalized explanations of learning algorithms and their impact on employees' compliance behaviorcited by 11
  14. 2021Improving Users' Mental Model with Attention-directed Counterfactual Editscited by 5
  15. 2022Training Novices: The Role of Human-AI Collaboration and Knowledge Transfercited by 5

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 2; 2026: 2.

8 papers.

  1. 2024Deconstructing Human-AI Collaboration: Agency, Interaction, and Adaptationcited by 8
  2. 2024Don't be Fooled: The Misinformation Effect of Explanations in Human-AI Collaborationcited by 8
  3. 2024A Survey of AI Reliancecited by 4
  4. 2024Transferring Domain Knowledge with (X)AI-Based Learning Systemscited by 3
  5. 2026Oversight Has a Capacity: Calibrating Agent Guards to a Subjective, Fatiguing Humancited by 3
  6. 2025Understanding User Preferences for Interaction Styles in Conversational Recommender Systems: The Predictive Role of System Qualities, User Experience, and Traitscited by 0
  7. 2025Development of Mental Models in Human-AI Collaboration: A Conceptual Frameworkcited by 0
  8. 2026Beyond single-channel agentic benchmarkingcited by 0