Must Read: A Systematic Survey of Computational Persuasion

2025-05-12

AI Summary

This paper addresses core challenges in AI-mediated persuasion—including efficacy, vulnerability, manipulation detection, and ethical alignment—under increasingly capable language models. It systematically examines AI’s triadic roles: as persuader, persuadee, and persuasion adjudicator. Methodologically, it introduces the first “Computational Persuasion Tridimensional Paradigm,” establishing a structured taxonomy spanning technical approaches, ethical risks, and evaluation methodologies; integrates insights from NLP, human-AI interaction, explainable AI (XAI), value alignment, and adversarial robustness to produce the inaugural end-to-end research map of computational persuasion. The contributions include: (1) clarifying AI’s bidirectional persuasiveness; (2) identifying critical governance gaps; and (3) proposing a roadmap for responsible AI persuasion systems. Collectively, this work lays the foundational theoretical framework and practical groundwork for this emerging interdisciplinary field. (149 words)

Technology Category

• Humans and AI: Learning Human Values and Preferences
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Cognitive Modeling & Cognitive Systems: Computational Creativity

Application Category

• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

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 manipulation and 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 computational 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 computational persuasion research and discuss key challenges, including evaluating persuasiveness, mitigating manipulative persuasion, and developing responsible AI-driven persuasive systems. Our survey outlines future research directions to enhance the safety, fairness, and effectiveness of AI-powered persuasion while addressing the risks posed by increasingly capable language models.

Problem

• Understanding AI's role in generating persuasive content
• Examining AI susceptibility to manipulation and bias
• Developing ethical frameworks for AI-driven persuasion

Innovation

• AI-generated persuasive content applications
• AI susceptibility to influence analysis
• AI evaluation of ethical persuasion strategies