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

arXiv: https://arxiv.org/abs/2107.07015 · HTML: https://mlsanity.com/paper/2107.07015/

## Key facts

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

## Builds on (most cited first, 8 of 8)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [A Patient-Centric Dataset of Images and Metadata for Identifying Melanomas Using Clinical Context](https://mlsanity.com/paper/2008.07360/) (2020)
- [A Large Self-Annotated Corpus for Sarcasm](https://mlsanity.com/paper/1704.05579/) (2017)
- [CheXplain: Enabling Physicians to Explore and UnderstandData-Driven, AI-Enabled Medical Imaging Analysis](https://mlsanity.com/paper/2001.05149/) (2020)
- [Global AI Ethics: A Review of the Social Impacts and Ethical Implications of Artificial Intelligence](https://mlsanity.com/paper/1907.07892/) (2019)
- [A Statistical Approach to Adult Census Income Level Prediction](https://mlsanity.com/paper/1810.10076/) (2018)

## Built on by (most cited first, 30 of 35)

- [CoMPosT: Characterizing and Evaluating Caricature in LLM Simulations](https://mlsanity.com/paper/2310.11501/) (2023)
- [Who Should I Trust: AI or Myself? Leveraging Human and AI Correctness Likelihood to Promote Appropriate Trust in AI-Assisted Decision-Making](https://mlsanity.com/paper/2301.05809/) (2023)
- ["It's Weird That it Knows What I Want": Usability and Interactions with Copilot for Novice Programmers](https://mlsanity.com/paper/2304.02491/) (2023)
- [Human-Algorithm Collaboration: Achieving Complementarity and Avoiding Unfairness](https://mlsanity.com/paper/2202.08821/) (2022)
- [Trust in AI: Progress, Challenges, and Future Directions](https://mlsanity.com/paper/2403.14680/) (2024)
- [Human-Aligned Calibration for AI-Assisted Decision Making](https://mlsanity.com/paper/2306.00074/) (2023)
- [LLM-GLOBE: A Benchmark Evaluating the Cultural Values Embedded in LLM Output](https://mlsanity.com/paper/2411.06032/) (2024)
- [Uncalibrated Models Can Improve Human-AI Collaboration](https://mlsanity.com/paper/2202.05983/) (2022)
- [Human-AI collaboration is not very collaborative yet: A taxonomy of interaction patterns in AI-assisted decision making from a systematic review](https://mlsanity.com/paper/2310.19778/) (2023)
- ["AI enhances our performance, I have no doubt this one will do the same": The Placebo effect is robust to negative descriptions of AI](https://mlsanity.com/paper/2309.16606/) (2023)
- [Towards More Trustworthy and Interpretable LLMs for Code through Syntax-Grounded Explanations](https://mlsanity.com/paper/2407.08983/) (2024)
- [From Deception to Detection: The Dual Roles of Large Language Models in Fake News](https://mlsanity.com/paper/2409.17416/) (2024)
- [Brain-inspired Artificial Intelligence: A Comprehensive Review](https://mlsanity.com/paper/2408.14811/) (2024)
- [Human-in-the-Loop Mixup](https://mlsanity.com/paper/2211.01202/) (2022)
- [SaudiCulture: A Benchmark for Evaluating Large Language Models Cultural Competence within Saudi Arabia](https://mlsanity.com/paper/2503.17485/) (2025)
- [Learning to Complement and to Defer to Multiple Users](https://mlsanity.com/paper/2407.07003/) (2024)
- [Understanding the Effects of Miscalibrated AI Confidence on User Trust, Reliance, and Decision Efficacy](https://mlsanity.com/paper/2402.07632/) (2024)
- [Explaining and Improving Information Complementarities in Multi-Agent Decision-making](https://mlsanity.com/paper/2502.06152/) (2025)
- [Will We Trust What We Don't Understand? Impact of Model Interpretability and Outcome Feedback on Trust in AI](https://mlsanity.com/paper/2111.08222/) (2021)
- [Learning to Complement with Multiple Humans](https://mlsanity.com/paper/2311.13172/) (2023)
- [An Empirical Exploration of Trust Dynamics in LLM Supply Chains](https://mlsanity.com/paper/2405.16310/) (2024)
- [ADESSE: Advice Explanations in Complex Repeated Decision-Making Environments](https://mlsanity.com/paper/2405.20705/) (2024)
- [Human-Centered AI Communication in Co-Creativity: An Initial Framework and Insights](https://mlsanity.com/paper/2505.18385/) (2025)
- [The Impact and Feasibility of Self-Confidence Shaping for AI-Assisted Decision-Making](https://mlsanity.com/paper/2502.14311/) (2025)
- [Improving User Experience with FAICO: Towards a Framework for AI Communication in Human-AI Co-Creativity](https://mlsanity.com/paper/2504.02526/) (2025)
- [Incentive-Tuning: Understanding and Designing Incentives for Empirical Human-AI Decision-Making Studies](https://mlsanity.com/paper/2601.15064/) (2026)
- [Human-AI Collaborative Multi-modal Multi-rater Learning for Endometriosis Diagnosis](https://mlsanity.com/paper/2409.02046/) (2024)
- [From Use to Oversight: How Mental Models Influence User Behavior and Output in AI Writing Assistants](https://mlsanity.com/paper/2604.05166/) (2026)
- [Learning to Decide with AI Assistance under Human-Alignment](https://mlsanity.com/paper/2605.12646/) (2026)
- [Combating Spatial Disorientation in a Dynamic Self-Stabilization Task Using AI Assistants](https://mlsanity.com/paper/2409.14565/) (2024)

## 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.