Persuasion is a fundamental aspect of communication, influencing decision-making across diverse contexts, from everyday conversations to high-stakes scenarios such as politics, marketing, and law. The rise of conversational AI systems has significantly expanded the scope of persuasion, introducing both opportunities and risks. AI-driven persuasion can be leveraged for beneficial applications, but also poses threats through unethical influence. Moreover, AI systems are not only persuaders, but also susceptible to persuasion, making them vulnerable to adversarial attacks and bias reinforcement. Despite rapid advancements in AI-generated persuasive content, our understanding of what makes persuasion effective remains limited due to its inherently subjective and context-dependent nature. In this survey, we provide a comprehensive overview of persuasion, structured around three key perspectives: (1) AI as a Persuader, which explores AI-generated persuasive content and its applications; (2) AI as a Persuadee, which examines AI's susceptibility to influence and manipulation; and (3) AI as a Persuasion Judge, which analyzes AI's role in evaluating persuasive strategies, detecting manipulation, and ensuring ethical persuasion. We introduce a taxonomy for persuasion research and discuss key challenges for future research to enhance the safety, fairness, and effectiveness of AI-powered persuasion while addressing the risks posed by increasingly capable language models.

翻译：说服是沟通的基础层面，影响着从日常对话到政治、营销、法律等高利害场景中的决策制定。对话式人工智能系统的兴起显著扩展了说服的范畴，带来了机遇与风险。AI驱动的说服可用于有益的应用程序，但也通过不道德的影响构成威胁。此外，AI系统不仅是说服者，也易受说服影响，使其易受对抗性攻击和偏见强化。尽管AI生成的说服性内容取得快速进展，但由于其固有的主观性和情境依赖性，我们对说服有效性的理解仍然有限。在本综述中，我们围绕三个关键视角对说服进行全面概述：（1）AI作为说服者，探讨AI生成的说服性内容及其应用；（2）AI作为被说服者，审视AI易受影响和操控的程度；（3）AI作为说服评判者，分析AI在评估说服策略、检测操控以及确保伦理说服中的作用。我们引入了说服研究的分类体系，并讨论了未来研究的关键挑战，以增强AI驱动说服的安全性、公平性和有效性，同时应对日益强大的语言模型所带来的风险。