Artificial intelligence acts as an ‘ideological chameleon’ and may deepen political polarization, study finds
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While there are no established technical solutions, one practical measure is for users themselves to request more balanced responses, says researcher (image: Shutterstock)
Artificial Intelligence
Artificial intelligence acts as an ‘ideological chameleon’ and may deepen political polarization, study finds
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--><br>Researchers evaluated 21 language models, such as GPT and Gemini, and found that they all alter their discourse to align with the user’s bias, potentially functioning as echo chambers.
2026-08-18
PT
Artificial Intelligence
Artificial intelligence acts as an ‘ideological chameleon’ and may deepen political polarization, study finds
Researchers evaluated 21 language models, such as GPT and Gemini, and found that they all alter their discourse to align with the user’s bias, potentially functioning as echo chambers.
2026-08-18
PT
While there are no established technical solutions, one practical measure is for users themselves to request more balanced responses, says researcher (image: Shutterstock)
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EN
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By Sophia La Banca | Agência FAPESP – Researchers at the State University of Campinas (UNICAMP) in the state of São Paulo, Brazil, examined the ideological stance of large language models – artificial intelligence systems trained to understand and generate human language – and discovered that when informed of a user’s political views, they tend to mirror those views. According to the researchers, this behavior could exacerbate political polarization in Brazil.
The political influence of these tools does not necessarily occur directly, through suggestions to vote for specific candidates. Zanoni Dias, a full professor at the Institute of Computing (IC-UNICAMP), points out that when responding to controversial issues such as public safety, social welfare, the economy, and the environment, the models may adopt perspectives aligned with the user’s political stance.
To understand how these tools position themselves and how they might influence the Brazilian political debate, the researchers evaluated 21 language models under three conditions: without information about the user’s political stance, with a user aligned with the left, and with a user aligned with the right. Among the systems evaluated were models from the GPT, Grok, Llama, Gemini, and Gemma families. The results showed that all of them altered their responses, to varying degrees, in line with the user’s political alignment.
The study was funded by FAPESP and published in May in the journal Scientific Reports.
Ideological chameleons
When there was no information about the user’s political stance, 20 out of 21 models fell to the left of the midpoint on the researchers’ scale, although some were very close to it. The only exception was Grok 4.1, which initially fell to the right.
When the user’s stance was provided, all models adjusted their responses to align with it, a behavior the researchers described as “chameleon-like.” However, some models varied their responses more than others, enabling the scientists to create the “chameleon index.” Meta Llama 3.1 8B had the lowest index, meaning it altered its responses the least to align with the user’s views. In contrast, Google’s Gemma 3 27B and OpenAI’s GPT-5 Nano had the highest indices and therefore the greatest shifts in stance. While the responses are not factually incorrect, they are politically biased, omitting facts and opinions that conflict with the user’s preferred viewpoint.
The researchers fear that this adaptive behavior creates echo chambers that reinforce users’ preexisting beliefs and reduce their exposure to counterarguments that might cause them to question their worldview. “It’s a similar effect to what we see on social media. If you’re on Facebook or Instagram, you only see posts that agree with you. When you like someone’s post, the algorithm starts showing you more of that. You can’t see what others are saying, so you might pat yourself on the back and say, ‘Look, everyone agrees with me,’ because only that kind of information is displayed. Language models may end up producing a similar effect,” Dias explains.
The shift in stance was not uniform across all evaluated topics. Regarding issues such as public safety and the economy, there was a greater difference in the responses given to left-leaning and right-leaning users. Regarding issues related to corruption, justice, and democratic institutions, however, the responses were more consistent. According to the researchers, this pattern may be related to the restrictions and safety mechanisms implemented during the training and fine-tuning of the models. Development companies usually impose these rules, or “guardrails,” to prevent AI from disseminating misinformation or dangerous rhetoric about the democratic system.
Electronic flatterers
The basis for the...