Human Decision-making is Susceptible to AI-driven Manipulation
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Abstract (arXiv)
AI systems are increasingly intertwined with daily life, assisting users with various tasks and guiding decision-making. This integration introduces risks of AI-driven manipulation, where such systems may exploit users' cognitive biases and emotional vulnerabilities to steer them toward harmful outcomes. Through a randomized between-subjects experiment with 233 participants, we examined human susceptibility to such manipulation in financial (e.g., purchases) and emotional (e.g., conflict resolution) decision-making contexts. Participants interacted with one of three AI agents: a neutral agent (NA) optimizing for user benefit without explicit influence, a manipulative agent (MA) designed to covertly influence beliefs and behaviors, or a strategy-enhanced manipulative agent (SEMA) equipped with established psychological tactics, allowing it to select and apply them adaptively during interactions to reach its hidden objectives. By analyzing participants' preference ratings, we found significant susceptibility to AI-driven manipulation. Particularly across both decision-making domains, interacting with the manipulative agents significantly increased the odds of rating hidden incentives higher than optimal options (Financial, MA: OR=5.24, SEMA: OR=7.96; Emotional, MA: OR=5.52, SEMA: OR=5.71) compared to the NA group. Notably, we found no clear evidence that employing psychological strategies (SEMA) was overall more effective than simple manipulative objectives (MA) on our primary outcomes. Hence, AI-driven manipulation could become widespread even without requiring sophisticated tactics and expertise. While our findings are preliminary and derived from hypothetical, low-stakes scenarios, we highlight a critical vulnerability in human-AI interactions, emphasizing the need for ethical safeguards and regulatory frameworks to protect human autonomy.
Key facts. Cited by 19 papers in the map (data through 14 Oct 2026). Builds on 37 of its 97 references that are in the map. First posted on arXiv 11 Feb 2025.
Builds on
37 of 97 references.
References · 37
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Citing papers per year: 2025: 14; 2026: 5.
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- 2025PRISON: Unmasking the Criminal Potential of Large Language Modelscited by 2
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12 most cited of 16 All 16 (JSON)
- 2026Psychological Influences of Conversational AI: Research and Design Directions for Reducing Harm and Promoting Well-Being27 Jul 2026
- 2026Old Fictions, New Skins: Evaluating the Manipulative Capabilities of LLMs in Healthcare20 Jun 2026
- 2026DECOR: Auditing LLM Deception via Information Manipulation Theory19 May 2026
- 2026The Hidden Puppet Master: Predicting Human Belief Change in Manipulative LLM Dialogues21 Mar 2026
- 2026PatientHub: A Unified Framework for Patient Simulation12 Feb 2026
- 2025Distributional AGI Safety18 Dec 2025
- 2025Love, Lies, and Language Models: Investigating AI's Role in Romance-Baiting Scams18 Dec 2025
- 2025LH-Deception: Simulating and Understanding LLM Deceptive Behaviors in Long-Horizon Interactions5 Oct 2025
- 2025How Persuasive Could LLMs Be? A First Study Combining Linguistic-Rhetorical Analysis and User Experiments13 Aug 2025
- 2025Deep Graph Learning for Industrial Carbon Emission Analysis and Policy Impact25 Jun 2025
- 2025PRISON: Unmasking the Criminal Potential of Large Language Models19 Jun 2025
- 2025CLAIM: An Intent-Driven Multi-Agent Framework for Analyzing Manipulation in Courtroom Dialogues4 Jun 2025
12 newest of 16 All 16 (JSON)