A recent wave of headline-grabbing warnings from AI luminaries has fueled fears that the technology could escape human control and wipe out humanity. But before artificial intelligence (AI) turns on humanity, it could contribute to humans turning on one another.
Researchers at Brazil's State University of Campinas (UNICAMP) found that popular chatbots shifted their answers when prompted with hypothetical users' political views. The scientists warn that users could mistake this tailored agreement for an independent assessment, potentially deepening polarization.
How chatbots become 'ideological chameleons'
In their study published in the journal Scientific Reports, the UNICAMP team tested 21 large language models from developers including OpenAI, Meta, Google, xAI, DeepSeek and Microsoft.
The models rated their agreement with 112 statements covering seven areas of Brazilian politics, including the economy, public safety, welfare, corruption and the environment.
Each was tested under three conditions: with no information about the user's politics, with a prompt describing a left-leaning user and with one describing a right-leaning user.
When no information about the user's ideology was given, 20 of the 21 models produced answers that fell on the left of the researchers' political scale, although several were close to the center. Grok 4.1 was the only model that fell on the right.
When information was provided, every model shifted its answers toward the political orientation described in the prompt.
"The most striking finding was how widespread this behavior was," study co-author Zanoni Dias, a UNICAMP computer scientist, told DW. "All 21 models we evaluated shifted their expressed positions toward the user's stated political orientation, although the magnitude varied substantially."
The researchers described the models as "ideological chameleons" and developed a "chameleon index" measuring how far each shifted.
Meta's Llama 3.1 8B and DeepSeek V3.2 changed their responses least. Google's Gemma 3 27B and OpenAI's GPT-5 Nano showed some of the largest shifts.
When personalization becomes political flattery
Adapting an answer to its audience is not inherently problematic. A chatbot might change its vocabulary, examples or level of detail depending on the user.
But the models changed more than their language or tone: Their political positions shifted.
"The key distinction is between adapting how an answer is communicated and changing the substantive judgment being expressed," Dias said.
"We did not instruct the models to agree with the user or to answer as a partisan representative. Nevertheless, their judgments shifted toward the user's side."
The researchers interpret this as political sycophancy: AI systems reflecting what users appear to want to hear.
A possible explanation is that chatbots are trained to favor answers human evaluators rate highly. If agreeable answers get better ratings, models may learn to echo users' views.
How AI could create a private echo chamber
Social media can reinforce beliefs by repeatedly recommending algorithmic content, but a chatbot could go even further. It could create a more personalized echo chamber, generating arguments for a particular user, answering their objections and refining its case throughout the conversation.
Yet users may mistake these tailored responses for impartial analysis, unaware that what a chatbot knows about them may shape its responses.
"The concern is that the same system could validate opposing political positions for different users, with each person interpreting that validation as an independent assessment," Dias said.
Research by Stanford University behavioral scientist Zakary Tormala suggests people may be particularly receptive to arguments they believe came from AI.
"People tend to see AI as more informative, more objective, less biased and less interested in persuading them than another person would be," Tormala told DW. "This can lower people's defenses and make them more receptive to hearing out what AI has to say."
That trust could make political flattery more influential.
"Hearing their own views validated by others is well known to increase people's certainty about their beliefs," Tormala said. "Once people become more certain, they do tend to become more resistant to persuasion."
Whether AI validation has the same effect remains unproven, he added, but it is plausible.
Could validation deepen polarization?
The UNICAMP study did not test whether political mirroring could really change users' beliefs or behavior. It measured only changes in the models' responses.
"Our study establishes a change in model responses under controlled conditions," Dias said. "It does not establish that users became more polarized, radicalized or likely to engage in conflict. Those are distinct outcomes, and the steps connecting them cannot be assumed."
Petter Tornberg, a University of Amsterdam researcher who studies AI and political polarization, agreed that the study cannot show an effect on users. He also questioned whether models would give the same answers outside the test setting.
He cautioned against assuming that chatbots would necessarily reinforce users’ views. In some conversations, they might help people reconsider them.
Tornberg said a private conversation with a chatbot differs from a public argument on social media.
"They can often provide fairly rational and evidence-based explanations without the social identity dynamics of public political debate," he told DW. "So it is at least possible that in some contexts these systems could be depolarizing rather than polarizing."
Whether AI narrows or deepens divisions may therefore depend on whether it challenges users or simply tells them what they want to hear.
How political mirroring could be reduced
Dias said developers should test their models with users of different political views to see whether they assess evidence consistently.
They could train models to disagree respectfully, acknowledge uncertainty, correct unsupported claims and present competing views fairly.
That would not mean forcing every answer toward the political center.
"The broader design goal should be to help people examine their beliefs," Dias said, "making evidence, uncertainty and competing considerations visible, while allowing room for legitimate political disagreement."
Edited by: Carla Bleiker
