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AI chatbots may increase accurate political knowledge

As voters seeking information about political issues increasingly turn to LLMs to do research, will AI lead them astray with hallucinations and bias, distorting the democratic process? Maybe not, according to a study of AI use in the 2024 U.K. election, published in PNAS Nexus.

AI chatbots may increase accurate political knowledge

A recent study on the use of AI chatbots during the 2024 U.K. election, published in PNAS Nexus, challenges concerns about AI potentially distorting voters' political knowledge. Researchers from Lennart Luettgau, Hannah Kirk, Kobi Hackenburg and colleagues asked UK residents to research true or false claims on climate change, immigration, criminal justice and COVID-19 policy.

The findings indicate that using AI chatbots, such as GPT-4o, Claude-3.5, Mistral or Google search, actually increased belief in true information and decreased belief in misinformation, regardless of whether the participants used the chatbots or traditional search engines. Notably, this research, whether conducted through chatbots or Google search, did not alter participants' trust in institutions, experts, media or technology.

However, both methods did shift participants' views towards progressive positions while decreasing agreement with conservative views, regardless of their initial political leanings. Additionally, chatbot-assisted research was found to be 6% to 10% faster than traditional search methods. The study authors caution that their findings may not fully apply to all AI models, and further research is needed to determine if this extends to more open-ended conversations or personalized search engines.

This research suggests that AI chatbots may effectively increase political knowledge without compromising factual accuracy, a finding that could have implications for ongoing discussions on AI governance and democratic integrity.

Written by urgent.news from Phys.org's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.

Read the original at phys.org →

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