Can Artificial Intelligence Improve Election Polling? Experts Weigh the Possibilities

Artificial intelligence is beginning to change the way researchers think about public opinion and election polling. While traditional surveys depend on asking real voters about their intentions, AI could introduce new tools for analyzing voter behavior, identifying population groups and improving the way polling data is interpreted.

The debate has become more relevant after recent elections produced results that were not fully anticipated by some opinion polls. According to neuroscientist and AI specialist Álvaro Machado, artificial intelligence could contribute to election research in several ways, although it should not be viewed as a replacement for conventional polling.

One potential application is the use of AI to divide populations into highly detailed groups. Voters increasingly have different political preferences, motivations and social characteristics, making broad categories less useful in some situations.

AI systems can process large quantities of information and identify patterns between different groups. This could help researchers build more detailed models of the electorate and understand how different segments might respond to political issues.

Another possibility is using artificial intelligence to analyze the reliability of survey responses and calculate probabilities. However, this approach remains controversial because human political preferences can change quickly.

A voter may express support for one candidate during a survey and later change their decision. Factors such as political events, campaign developments and tactical voting can influence the final choice.

This creates an important distinction between what people say they intend to do and what they ultimately do at the ballot box.

AI-Generated Voters

One of the most controversial developments is the creation of synthetic respondents.

Instead of interviewing thousands of real people, researchers can create artificial profiles based on demographic and behavioral information and then ask an AI model to respond as if it were those individuals.

The concept is sometimes described using terms such as “synthetic respondents” or “digital twins.”

However, recent research suggests that this approach cannot yet reliably replace interviews with real people.

A September 2026 study by the Pew Research Center tested AI-generated responses against answers from real participants. The researchers found significant differences between the synthetic responses and the views expressed by actual respondents. Different AI models also produced systematically different results even when they were given essentially the same information.

This means that simply asking an AI model what voters think can produce misleading conclusions.

Pew Research has therefore emphasized that speaking directly with real people remains essential for measuring public opinion.

AI Interviewers Could Become More Common

Another possible use of AI is in the interviewing process itself.

Instead of replacing voters with artificial respondents, researchers could use virtual interviewers to communicate with real people. An AI-generated interviewer could conduct conversations at a much larger scale while maintaining a consistent questionnaire.

This could also make some respondents more comfortable when discussing sensitive political preferences.

The idea is that people who might hesitate to reveal their opinions to a human interviewer could potentially respond differently to an automated system.

However, researchers would still need to deal with problems such as sampling, privacy, data protection and the possibility of respondents deliberately providing inaccurate information.

The Problem of Representativeness

AI does not eliminate one of the oldest challenges in polling: obtaining a sample that accurately represents the population.

Online surveys, for example, can suffer from selection bias because the people who choose to participate may differ from the population as a whole. Statistical techniques such as weighting and modeling can help reduce these problems, but they cannot automatically make poor-quality data reliable.

This is why AI should probably be viewed as an additional research tool rather than a substitute for established polling methods.

AI Can Also Help Beyond Polling

The technology has applications across the wider election process.

Election organizations are already exploring AI for tasks such as drafting communications, translating information and checking election materials. These applications are generally considered less risky because their results can be reviewed by humans.

At the same time, AI