mlsanityData to 7 Oct 2026

Do Humans Trust Advice More if it Comes from AI? An Analysis of Human-AI Interactions

arXiv 2107.07015 ↗First posted 14 Jul 2021cs.AI cs.HCcited by 35

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Abstract (arXiv)

In decision support applications of AI, the AI algorithm's output is framed as a suggestion to a human user. The user may ignore this advice or take it into consideration to modify their decision. With the increasing prevalence of such human-AI interactions, it is important to understand how users react to AI advice. In this paper, we recruited over 1100 crowdworkers to characterize how humans use AI suggestions relative to equivalent suggestions from a group of peer humans across several experimental settings. We find that participants' beliefs about how human versus AI performance on a given task affects whether they heed the advice. When participants do heed the advice, they use it similarly for human and AI suggestions. Based on these results, we propose a two-stage, "activation-integration" model for human behavior and use it to characterize the factors that affect human-AI interactions.

Key facts. Cited by 35 papers in the map (data through 7 Oct 2026). Builds on 8 of its 43 references that are in the map. First posted on arXiv 14 Jul 2021.

Builds on

8 of 43 references.

References · 8

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  3. 2017Explanation in Artificial Intelligence: Insights from the Social Sciencescited by 1,201
  4. 2020A Patient-Centric Dataset of Images and Metadata for Identifying Melanomas Using Clinical Contextcited by 146
  5. 2017A Large Self-Annotated Corpus for Sarcasmcited by 63
  6. 2020CheXplain: Enabling Physicians to Explore and UnderstandData-Driven, AI-Enabled Medical Imaging Analysiscited by 40
  7. 2019Global AI Ethics: A Review of the Social Impacts and Ethical Implications of Artificial Intelligencecited by 21
  8. 2018A Statistical Approach to Adult Census Income Level Predictioncited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 3; 2023: 7; 2024: 11; 2025: 7; 2026: 6.

35 papers.

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  1. 2023CoMPosT: Characterizing and Evaluating Caricature in LLM Simulationscited by 94
  2. 2023Who Should I Trust: AI or Myself? Leveraging Human and AI Correctness Likelihood to Promote Appropriate Trust in AI-Assisted Decision-Makingcited by 67
  3. 2023"It's Weird That it Knows What I Want": Usability and Interactions with Copilot for Novice Programmerscited by 62
  4. 2022Human-Algorithm Collaboration: Achieving Complementarity and Avoiding Unfairnesscited by 45
  5. 2024Trust in AI: Progress, Challenges, and Future Directionscited by 35
  6. 2023Human-Aligned Calibration for AI-Assisted Decision Makingcited by 28
  7. 2024LLM-GLOBE: A Benchmark Evaluating the Cultural Values Embedded in LLM Outputcited by 27
  8. 2022Uncalibrated Models Can Improve Human-AI Collaborationcited by 26
  9. 2023Human-AI collaboration is not very collaborative yet: A taxonomy of interaction patterns in AI-assisted decision making from a systematic reviewcited by 18
  10. 2023"AI enhances our performance, I have no doubt this one will do the same": The Placebo effect is robust to negative descriptions of AIcited by 11
  11. 2024From Deception to Detection: The Dual Roles of Large Language Models in Fake Newscited by 11
  12. 2024Towards More Trustworthy and Interpretable LLMs for Code through Syntax-Grounded Explanationscited by 10
23 more
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  3. 2025SaudiCulture: A Benchmark for Evaluating Large Language Models Cultural Competence within Saudi Arabiacited by 8
  4. 2024Learning to Complement and to Defer to Multiple Userscited by 7
  5. 2024Understanding the Effects of Miscalibrated AI Confidence on User Trust, Reliance, and Decision Efficacycited by 6
  6. 2025Explaining and Improving Information Complementarities in Multi-Agent Decision-makingcited by 6
  7. 2021Will We Trust What We Don't Understand? Impact of Model Interpretability and Outcome Feedback on Trust in AIcited by 5
  8. 2023Learning to Complement with Multiple Humanscited by 5
  9. 2024An Empirical Exploration of Trust Dynamics in LLM Supply Chainscited by 5
  10. 2024ADESSE: Advice Explanations in Complex Repeated Decision-Making Environmentscited by 3
  11. 2025Human-Centered AI Communication in Co-Creativity: An Initial Framework and Insightscited by 3
  12. 2025The Impact and Feasibility of Self-Confidence Shaping for AI-Assisted Decision-Makingcited by 2
  13. 2025Improving User Experience with FAICO: Towards a Framework for AI Communication in Human-AI Co-Creativitycited by 2
  14. 2026Incentive-Tuning: Understanding and Designing Incentives for Empirical Human-AI Decision-Making Studiescited by 2
  15. 2024Human-AI Collaborative Multi-modal Multi-rater Learning for Endometriosis Diagnosiscited by 1
  16. 2026From Use to Oversight: How Mental Models Influence User Behavior and Output in AI Writing Assistantscited by 1
  17. 2026Learning to Decide with AI Assistance under Human-Alignmentcited by 1
  18. 2024Combating Spatial Disorientation in a Dynamic Self-Stabilization Task Using AI Assistantscited by 0
  19. 2025Guidance Source Matters: How Guidance from AI, Expert, or a Group of Analysts Impacts Visual Data Preparation and Analysiscited by 0
  20. 2025Mapping the Trust Terrain: LLMs in Software Engineering -- Insights and Perspectivescited by 0
  21. 2026Large Language Models in Peer-Run Community Behavioral Health Services: Understanding Peer Specialists and Service Users' Perspectives on Opportunities, Risks, and Mitigation Strategiescited by 0
  22. 2026Human-Centric Reflective Architecture for Human-AI Collaborative Decision-Makingcited by 0
  23. 2026Trustworthy FinAInce: Unpacking How AI-Mediated Financial Advice is Judgedcited by 0