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pdf artificial intelligence can persuade humans ms20230724 max

Artificial Intelligence Can Persuade Humans on Political Issues Hui Bai, Jan Voelkel, Johannes Eichstaedt, Robb Willer This is a preprint; it has not been peer reviewed by a journal. https://doi.org/10.21203/rs.3.rs-3238396/v1 This work is licensed under a CC BY 4.0 License To whom correspondence may be addressed.

Email: kobi.hackenburg@oii.ox.ac.uk. Edited by Matthew Jackson, Stanford University, Stanford, CA; received February 13, 2024; accepted May 8, 2024 Received 2024 Feb 13; Accepted 2024 May 8; Issue date 2024 Jun 11. This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY). Advances in large language models (LLMs) have raised concerns over scalable, personalized political persuasion. Here, we integrate user data into GPT-4 prompts in real-time, facilitating the live creation of messages tailored to persuade individual users on political issues.

We then deploy this application at scale to test whether personalized, microtargeted messaging offers a persuasive advantage compared to nontargeted messaging. We find that while messages generated by GPT-4 were persuasive, in aggregate, the persuasive impact of microtargeted messages was not statistically different from that of nontargeted messages. These findings suggest—contrary to widespread speculation—that the influence of current LLMs may reside not in their ability to tailor messages to individuals but rather in the persuasiveness of their generic, nontargeted messages. Nature Communications volume 16, Article number: 6037 (2025) Cite this article The emergence of large language models (LLMs) has made it possible for generative artificial intelligence (AI) to tackle many higher-order cognitive tasks, with critical implications for industry, government, and labor markets. Here, we investigate whether existing, openly-available LLMs can be used to create messages capable of influencing humans’ political attitudes.

Across three pre-registered experiments (total N = 4829), participants who read persuasive messages generated by LLMs showed significantly more attitude change across a range of policies - including polarized policies, like an assault weapons... Overall, LLM-generated messages were similarly effective in influencing policy attitudes as messages crafted by lay humans. Participants’ reported perceptions of the authors of the persuasive messages suggest these effects occurred through somewhat distinct causal pathways. While the persuasiveness of LLM-generated messages was associated with perceptions that the author used more facts, evidence, logical reasoning, and a dispassionate voice, the persuasiveness of human-generated messages was associated with perceptions of the... These results demonstrate that recent developments in AI make it possible to create politically persuasive messages quickly, cheaply, and at massive scale. Recent developments in generative artificial intelligence (AI)—in particular, large language models (LLMs)—have led to major breakthroughs, achieving capacities thought to be impossible just a few years ago.

AI-driven applications can now be used to create visual art1, compose music2, write computer source code3, and produce text of striking complexity4. LLMs, specifically, have proven capable of debating with humans5 and outperforming humans in online strategy games involving negotiation6, suggesting that the most advanced language models can now generate complex reasoning and language expression at... With LLMs reaching these and other higher-order capacities, their potential influence on politics has emerged as a high-stakes question demanding scholarly attention. AI-generated images have already been deployed in political advertisements in the US7, indicating that LLM-generated text is likely to appear in political campaigns soon, if not already. While there are many potential applications of LLMs to political persuasion, it is unknown whether LLM-generated content can be used to shift humans’ views on political issues. Understanding the capacity of LLMs for political persuasion is crucial to inform the urgency with which lawmakers may need to develop regulations, and LLM developers may need to implement guardrails to encourage benevolent applications...

Researchers at Stanford University wanted to see if AI-generated arguments could change minds on controversial hot-button issues. It worked. Suddenly, the world is abuzz with chatter about chatbots. Artificially intelligent agents, like ChatGPT, have shown themselves to be remarkably adept at conversing in a very human-like fashion. Implications stretch from the classroom to Capitol Hill. ChatGPT, for instance, recently passed written exams at top business and law schools, among other feats both awe-inspiring and alarming.

Researchers at Stanford University’s Polarization and Social Change Lab and the Institute for Human-Centered Artificial Intelligence (HAI) wanted to probe the boundaries of AI’s political persuasiveness by testing its ability to sway real humans... “AI fared quite well. Indeed, AI-generated persuasive appeals were as effective as ones written by humans in persuading human audiences on several political issues,” said Hui “Max” Bai, a postdoctoral researcher in the Polarization and Social Change Lab... The research team, led by Robb Willer, a professor of sociology, psychology, and organizational behavior in the Stanford School of Humanities and Sciences and director of the Polarization and Social Change Lab, used GPT-3,... They asked their model to craft persuasive messages on several controversial topics.

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Artificial Intelligence Can Persuade Humans On Political Issues Hui Bai,

Artificial Intelligence Can Persuade Humans on Political Issues Hui Bai, Jan Voelkel, Johannes Eichstaedt, Robb Willer This is a preprint; it has not been peer reviewed by a journal. https://doi.org/10.21203/rs.3.rs-3238396/v1 This work is licensed under a CC BY 4.0 License To whom correspondence may be addressed.

Email: Kobi.hackenburg@oii.ox.ac.uk. Edited By Matthew Jackson, Stanford University, Stanford, CA;

Email: kobi.hackenburg@oii.ox.ac.uk. Edited by Matthew Jackson, Stanford University, Stanford, CA; received February 13, 2024; accepted May 8, 2024 Received 2024 Feb 13; Accepted 2024 May 8; Issue date 2024 Jun 11. This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY). Advances in large language models (LLMs) have raised concerns over scalable, personalized...

We Then Deploy This Application At Scale To Test Whether

We then deploy this application at scale to test whether personalized, microtargeted messaging offers a persuasive advantage compared to nontargeted messaging. We find that while messages generated by GPT-4 were persuasive, in aggregate, the persuasive impact of microtargeted messages was not statistically different from that of nontargeted messages. These findings suggest—contrary to widespread s...

Across Three Pre-registered Experiments (total N = 4829), Participants Who

Across three pre-registered experiments (total N = 4829), participants who read persuasive messages generated by LLMs showed significantly more attitude change across a range of policies - including polarized policies, like an assault weapons... Overall, LLM-generated messages were similarly effective in influencing policy attitudes as messages crafted by lay humans. Participants’ reported percept...

AI-driven Applications Can Now Be Used To Create Visual Art1,

AI-driven applications can now be used to create visual art1, compose music2, write computer source code3, and produce text of striking complexity4. LLMs, specifically, have proven capable of debating with humans5 and outperforming humans in online strategy games involving negotiation6, suggesting that the most advanced language models can now generate complex reasoning and language expression at....