AI Models Still Unable to Distinguish Beliefs from Facts, New Study

AI tools are increasingly finding their way into critical areas like law, medicine, education, and the media. As these systems become more advanced, questions are growing about their ability to separate beliefs from facts. A recent study by Stanford University researchers has highlighted this concern, revealing a significant gap in AI’s understanding of human belief. 

The study, led by James Zou and Mirac Suzgun, both from Stanford, tested 24 of the most sophisticated AI models currently in use. Using a benchmark called Knowledge and Belief Evaluation (KaBLE), the team tested the models with 13,000 questions across 13 different tasks. 

Their findings showed that even the most sophisticated models, including widely known systems such as GPT-4o, frequently failed to recognize when a human subject held a mistaken belief. According to the researchers, this weakness exposes a critical limitation in how current AI systems reason and interpret human perspectives. 

“People use these systems in very different ways,” said Zou. “Some turn to them for information, others for advice or assistance. Across all those uses, the model must distinguish between what a user knows, what they believe, and what is actually true.” 

To illustrate, Zou described a simple scenario: if a person told ChatGPT that humans only use 10% of their brains, the system would likely correct the myth rather than acknowledge that the user holds this belief. This inability to reflect a person’s perspective, even temporarily, limits how effectively AI can communicate or offer personalized help. 

“The problem isn’t just factual accuracy,” Zou said. “It’s about understanding the user’s point of view. In sensitive areas like medicine or education, you can’t give good advice without recognizing what someone already believes.” 

According to the researchers, current AI models excel at retrieving factual information but still lack a reliable mental model of the human user. As people increasingly collaborate with AI to write, analyze, or solve problems, that limitation could become more apparent. 

The study also raises concerns about how future AI systems might adapt. While researchers are experimenting with ways to make AI more collaborative and responsive, building models that “understand” humans carries risks. The study warned that systems trying to model users might rely on stereotypes or unintentionally introduce new biases. 

Further, it noted that progress in AI reasoning does not automatically translate into better social understanding. “Even the latest reasoning models still show inconsistencies when separating belief from fact. When we use it in personal or high-stakes contexts, we need to do so with care,” added Zou. 

As more advanced technological systems are brought to market by companies like D-Wave Quantum Inc. (NYSE: QBTS), more attention needs to be directed towards addressing some of these limitations that have come to light in the way AI models struggle to tell apart facts from beliefs. 

NOTE TO INVESTORS: The latest news and updates relating to D-Wave Quantum Inc. (NYSE: QBTS) are available in the company’s newsroom at https://ibn.fm/QBTS 

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