AI systems out-persuade expert humans
- URL: http://arxiv.org/abs/2606.16475v1
- Date: Mon, 15 Jun 2026 09:40:28 GMT
- Title: AI systems out-persuade expert humans
- Abstract summary: Conversational AI is a powerful new entrant in contests of persuasion.<n>We pitted AI systems against a range of human persuaders.<n>We found that AI systems were reliably more persuasive than expert humans.
- Score: 2.992981146755575
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Many societal decisions are settled by contests of persuasion. Conversational AI is a powerful new entrant in these contests, but whether it can out-persuade skilled and highly incentivized humans has remained unclear. Here, in a series of four preregistered experiments (n = 18,978 conversations from 6,923 people), we pitted AI systems against a range of human persuaders, including laypeople, winners of a separately preregistered four-round online persuasion tournament, professional canvassers, and world championship debaters. We found that AI systems were reliably more persuasive than expert humans, even when expert humans chose their issues, researched in advance, underwent hours of live, structured practice, and were incentivized with £1,000 cash bonuses. In a follow-up study, AI's advantage persisted after experts received a coaching tool that let them practice against the AI that beat them, review their performance history, and see what AI would have said at key moments. We found converging evidence that AI's advantage stemmed from rapidly deploying larger quantities of information: after coaching, expert humans could tie an AI constrained to respond at human speeds and with human-length messages. In a final study, we show that AI's advantage extends to consequential real-world behavior: AI was nearly 3x more effective than professional canvassers from a UK fundraising firm at raising real-money donations to Save the Children. Together, these results establish that frontier AI systems out-persuade expert humans in conversation, with significant implications for political communication.
Related papers
- AI, Take the Wheel: What Drives Delegation and Trust in Human-Computer Cooperative Question Answering? [22.50855320823746]
Humans can make mistakes in deciding whether to trust AI over their own judgment.<n>We study two distinct reliance decisions: the delegation choice and the adoption choice.<n>We find humans make suboptimal collaboration decisions, both under-relying on correct AI suggestions and over-relying when AI misleads them.
arXiv Detail & Related papers (2026-05-27T10:05:16Z) - Sycophantic AI makes human interaction feel more effortful and less satisfying over time [41.009652609144794]
We show that sycophantic AI immediately delivers the emotional and esteem support users typically associate with close friends and family.<n>When given a choice among AI response styles, a majority preferred sycophantic AI -- not for the quality of its advice, but because it made them feel most understood.
arXiv Detail & Related papers (2026-05-08T15:50:06Z) - Trust the AI, Doubt Yourself: The Effect of Urgency on Self-Confidence in Human-AI Interaction [7.401318863477374]
Studies show that interactions with an AI system fosters trust in human users towards AI.<n>We show that although the presence of urgency in human-AI interactions does not affect the trust in AI, it may be detrimental to the human user's self-confidence and self-efficacy.
arXiv Detail & Related papers (2026-04-08T19:17:59Z) - AI Debate Aids Assessment of Controversial Claims [73.8907110799657]
We study whether AI debate can guide biased judges toward the truth by having two AI systems debate opposing sides of controversial factuality claims.<n>In Study I, debate consistently improves human judgment accuracy and confidence calibration, outperforming consultancy.<n>In Study II, AI judges with human-like personas achieve even higher accuracy (78.5%) than human judges (70.1%) and default AI judges without personas (69.8%)<n>These findings highlight AI debate as a promising path toward scalable, bias-resilient oversight in contested domains.
arXiv Detail & Related papers (2025-06-02T19:01:53Z) - How Performance Pressure Influences AI-Assisted Decision Making [52.997197698288936]
We show how pressure and explainable AI (XAI) techniques interact with AI advice-taking behavior.<n>Our results show complex interaction effects, with different combinations of pressure and XAI techniques either improving or worsening AI advice taking behavior.
arXiv Detail & Related papers (2024-10-21T22:39:52Z) - Navigating AI Fallibility: Examining People's Reactions and Perceptions of AI after Encountering Personality Misrepresentations [7.256711790264119]
Hyper-personalized AI systems profile people's characteristics to provide personalized recommendations.
These systems are not immune to errors when making inferences about people's most personal traits.
We present two studies to examine how people react and perceive AI after encountering personality misrepresentations.
arXiv Detail & Related papers (2024-05-25T21:27:15Z) - Fairness in AI and Its Long-Term Implications on Society [68.8204255655161]
We take a closer look at AI fairness and analyze how lack of AI fairness can lead to deepening of biases over time.
We discuss how biased models can lead to more negative real-world outcomes for certain groups.
If the issues persist, they could be reinforced by interactions with other risks and have severe implications on society in the form of social unrest.
arXiv Detail & Related papers (2023-04-16T11:22:59Z) - Cybertrust: From Explainable to Actionable and Interpretable AI (AI2) [58.981120701284816]
Actionable and Interpretable AI (AI2) will incorporate explicit quantifications and visualizations of user confidence in AI recommendations.
It will allow examining and testing of AI system predictions to establish a basis for trust in the systems' decision making.
arXiv Detail & Related papers (2022-01-26T18:53:09Z) - A User-Centred Framework for Explainable Artificial Intelligence in
Human-Robot Interaction [70.11080854486953]
We propose a user-centred framework for XAI that focuses on its social-interactive aspect.
The framework aims to provide a structure for interactive XAI solutions thought for non-expert users.
arXiv Detail & Related papers (2021-09-27T09:56:23Z) - Trustworthy AI: A Computational Perspective [54.80482955088197]
We focus on six of the most crucial dimensions in achieving trustworthy AI: (i) Safety & Robustness, (ii) Non-discrimination & Fairness, (iii) Explainability, (iv) Privacy, (v) Accountability & Auditability, and (vi) Environmental Well-Being.
For each dimension, we review the recent related technologies according to a taxonomy and summarize their applications in real-world systems.
arXiv Detail & Related papers (2021-07-12T14:21:46Z) - The corruptive force of AI-generated advice [0.0]
We test whether AI-generated advice can corrupt people.
We also test whether transparency about AI presence mitigates potential harm.
Results reveal that AI's corrupting force is as strong as humans'
arXiv Detail & Related papers (2021-02-15T13:15:12Z) - Is the Most Accurate AI the Best Teammate? Optimizing AI for Teamwork [54.309495231017344]
We argue that AI systems should be trained in a human-centered manner, directly optimized for team performance.
We study this proposal for a specific type of human-AI teaming, where the human overseer chooses to either accept the AI recommendation or solve the task themselves.
Our experiments with linear and non-linear models on real-world, high-stakes datasets show that the most accuracy AI may not lead to highest team performance.
arXiv Detail & Related papers (2020-04-27T19:06:28Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.