mlsanityData to 14 Oct 2026

Artificial Influence: An Analysis Of AI-Driven Persuasion

arXiv 2303.08721 ↗First posted 15 Mar 2023cs.CY cs.AI cs.CLcited by 33

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Abstract (arXiv)

Persuasion is a key aspect of what it means to be human, and is central to business, politics, and other endeavors. Advancements in artificial intelligence (AI) have produced AI systems that are capable of persuading humans to buy products, watch videos, click on search results, and more. Even systems that are not explicitly designed to persuade may do so in practice. In the future, increasingly anthropomorphic AI systems may form ongoing relationships with users, increasing their persuasive power. This paper investigates the uncertain future of persuasive AI systems. We examine ways that AI could qualitatively alter our relationship to and views regarding persuasion by shifting the balance of persuasive power, allowing personalized persuasion to be deployed at scale, powering misinformation campaigns, and changing the way humans can shape their own discourse. We consider ways AI-driven persuasion could differ from human-driven persuasion. We warn that ubiquitous highlypersuasive AI systems could alter our information environment so significantly so as to contribute to a loss of human control of our own future. In response, we examine several potential responses to AI-driven persuasion: prohibition, identification of AI agents, truthful AI, and legal remedies. We conclude that none of these solutions will be airtight, and that individuals and governments will need to take active steps to guard against the most pernicious effects of persuasive AI.

Key facts. Cited by 33 papers in the map (data through 14 Oct 2026). Builds on 10 of its 53 references that are in the map. First posted on arXiv 15 Mar 2023.

Builds on

10 of 53 references.

References · 10

  1. 2017Deep reinforcement learning from human preferencescited by 3,912
  2. 2022Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedbackcited by 3,720
  3. 2021TruthfulQA: Measuring How Models Mimic Human Falsehoodscited by 2,975
  4. 2017Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithmcited by 734
  5. 2021Unsolved Problems in ML Safetycited by 292
  6. 2018The Design and Implementation of XiaoIce, an Empathetic Social Chatbotcited by 172
  7. 2021Truthful AI: Developing and governing AI that does not liecited by 113
  8. 2022Is Power-Seeking AI an Existential Risk?cited by 105
  9. 2022X-Risk Analysis for AI Researchcited by 60
  10. 2021No-Press Diplomacy from Scratchcited by 31

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 7; 2024: 9; 2025: 8; 2026: 9.

33 papers.

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  1. 2023An Overview of Catastrophic AI Riskscited by 222
  2. 2023AI Deception: A Survey of Examples, Risks, and Potential Solutionscited by 188
  3. 2023Sociotechnical Safety Evaluation of Generative AI Systemscited by 157
  4. 2023How to Catch an AI Liar: Lie Detection in Black-Box LLMs by Asking Unrelated Questionscited by 83
  5. 2024People cannot distinguish GPT-4 from a human in a Turing testcited by 48
  6. 2025Large Language Models Pass the Turing Testcited by 48
  7. 2023Evaluating Superhuman Models with Consistency Checkscited by 47
  8. 2023The Persuasive Power of Large Language Modelscited by 47
  9. 2024A Safe Harbor for AI Evaluation and Red Teamingcited by 47
  10. 2024Best-of-Venom: Attacking RLHF by Injecting Poisoned Preference Datacited by 35
  11. 2024AI Alignment with Changing and Influenceable Reward Functionscited by 33
  12. 2024Measuring and Benchmarking Large Language Models' Capabilities to Generate Persuasive Languagecited by 31
21 more
  1. 2023Bias and Fairness in Chatbots: An Overviewcited by 20
  2. 2025The Levers of Political Persuasion with Conversational AIcited by 20
  3. 2025Human Decision-making is Susceptible to AI-driven Manipulationcited by 19
  4. 2025Steering the CensorShip: Uncovering Representation Vectors for LLM "Thought" Controlcited by 14
  5. 2025Inherent and emergent liability issues in LLM-based agentic systems: a principal-agent perspectivecited by 13
  6. 2025Must Read: A Comprehensive Survey of Computational Persuasioncited by 13
  7. 2025Mitigating Manipulation and Enhancing Persuasion: A Reflective Multi-Agent Approach for Legal Argument Generationcited by 8
  8. 2024Large Language Models as Misleading Assistants in Conversationcited by 7
  9. 2024Sociotechnical Implications of Generative Artificial Intelligence for Information Accesscited by 6
  10. 2024Persuasion Games using Large Language Modelscited by 4
  11. 2026Value Leakage: An LLM's Answers Are Silently Shaped by Its Own Valuescited by 3
  12. 2026The Bots of Persuasion: Examining How Conversational Agents' Linguistic Expressions of Personality Affect User Perceptions and Decisionscited by 2
  13. 2024Politicians vs ChatGPT. A study of presuppositions in French and Italian political communicationcited by 1
  14. 2026Can AI-Generated Persuasion Be Detected? Persuaficial Benchmark and AI vs. Human Linguistic Differencescited by 1
  15. 2026Artificial intelligence can persuade people to take political actionscited by 1
  16. 2025ChatGPT for President! Presupposed content in politicians versus GPT-generated textscited by 0
  17. 2026Information Access of the Oppressed: Freirean Design for Emancipatory Information Accesscited by 0
  18. 2026Long-term Measurements: Towards a Longitudinal Understanding of Human-AI Interactionscited by 0
  19. 2026Position: The Alignment Community is Unintentionally Building a Censor's Toolkitcited by 0
  20. 2026Belief Cascades Drive Persuasion in LLM Agent Networkscited by 0
  21. 2026The AI's Poker Face: Contrasting Persuasion and Sycophancy in Voice-Based Multi-Turn Conversationscited by 0