{"id":549336,"date":"2026-09-30T13:55:17","date_gmt":"2026-09-30T17:55:17","guid":{"rendered":"https:\/\/alpha.pewresearch.org\/pewresearch-org\/?p=549336"},"modified":"2026-09-30T13:55:17","modified_gmt":"2026-09-30T17:55:17","slug":"can-ai-stand-in-for-human-survey-takers-not-really","status":"publish","type":"post","link":"https:\/\/alpha.pewresearch.org\/pewresearch-org\/data-labs\/2026\/09\/30\/can-ai-stand-in-for-human-survey-takers-not-really\/","title":{"rendered":"Can AI Stand In for Human Survey-Takers? Not Really"},"content":{"rendered":"\n

Synthetic surveys do a poor job of estimating the public mood and are especially error-prone for some subgroups, including Republicans and Black adults<\/h2>\n\n\n\n
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Illustration by Ibrahim Rayintakath<\/figcaption><\/figure>\n\n\n\n
About this research<\/summary>\n

This Pew Research Center report examines whether AI-generated survey data can accurately duplicate the results of high-quality public opinion polls.<\/p>\n\n\n\n

We conducted this methodological experiment to better understand an emerging method in survey research. The Center believes that speaking to the public is essential to measuring public opinion and has no current or future plans to use AI models to generate survey results. Read more about our AI policy<\/a>.<\/p>\n\n\n\n

For this study, we had an AI model take on the role of real respondents from our American Trends Panel<\/a> (ATP) and answer the same surveys administered to human panelists. These surveys included many different question formats and asked about a wide range of topics, attitudes and behaviors.<\/p>\n\n\n\n

Why did we do this?<\/h4>\n\n\n\n

The Center regularly studies and evaluates advances and trends in polling research<\/a>. As AI-based polling becomes more widely used in the industry<\/a>, we wanted to understand the data these \u201csynthetic samples\u201d produce and how it compares with high-quality surveys of real humans on topics of broad public interest. That question is the focus of this study; it does not address other uses of AI models in polling.<\/p>\n\n\n\n

How did we do this?<\/h4>\n\n\n\n

We used a \u201cdigital twins\u201d approach for this study, asking an AI model to adopt the personas of real humans who are members of the ATP. We gave the model a wide range of information about each panelist<\/a>, including their self-reported demographic information and their responses to questions on our 2025 political typology survey<\/a>. We then showed the model questions from three different ATP surveys conducted in the first half of 2026 and asked it to respond to those same questions as the assigned persona. The model was given each question on the survey in order from start to finish, with exactly the same instructions and survey programming that the real humans who took it received.<\/p>\n\n\n\n

To make a direct comparison between our AI and human polls, we only replicated surveys for the ATP panelists from each wave who had also completed the political typology survey. As a result, figures for U.S. adults in this report may differ slightly from those previously published by the Center.<\/p>\n\n\n\n

We tested multiple models, but all synthetic results unless noted otherwise come from Anthropic\u2019s Claude Opus 4.6. At the time we conducted this study, Opus 4.6 was the most recent Anthropic model available for commercial use and had the best performance of several we evaluated.<\/p>\n\n\n\n

Here are the survey questions from Wave 185<\/a>, Wave 190<\/a> and Wave 192<\/a> used for this analysis, the detailed responses<\/a> from the ATP and AI surveys, and the methodologies<\/a> for the original ATP survey waves and our AI surveys.<\/p>\n<\/details>\n\n\n\n

As artificial intelligence becomes more advanced, there is growing interest in using it to stand in for real respondents in public opinion surveys. Put simply: Instead of contacting large numbers of people and asking them what they think, pollsters can ask an AI model to predict how those people would have<\/em> answered a certain question.<\/p>\n\n\n

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AI-generated surveys consistently miss results of human polls<\/div>\n\t\t\t\t\t\t
Average absolute survey error (in percentage points) between human and synthetic respondents on 3 replicated survey waves<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\n
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* Estimates for Asian adults are representative of English speakers only.
Source: Surveys of U.S. adults and \u201cdigital twins\u201d synthetic analysis using Claude Opus 4.6 set to low reasoning with extended profile information and expert reflection. Surveys were conducted Jan. 20-26, 2026 (replicated March 9-12 and April 7-10), March 23-29, 2026 (replicated March 30-April 2), and April 20-26, 2026 (replicated April 27-May 1).
\u201cCan AI Stand In for Human Survey-Takers? Not Really\u201d<\/div>\n\t\t\t\t\t\t
PEW RESEARCH CENTER<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t
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Share this chart<\/h2>\n\t\t\t\t\t\t