Many people find AI-based chatbots helpful in keeping up with news, but a study by Pattie Maes and her colleagues at the MIT Media Lab points to a big problem with this strategy.
Participants who evaluated paired news headlines and images over the course of four weeks were initially 21% percent more accurate at telling fake news from real when aided by a chatbot—but by week four, they’d become 15% worse at identifying fake news without AI than they were before (though roughly a quarter of them reported feeling better at it). The result reflects an “AI dependency paradox” that has been observed in other domains, including medicine.
“Users get excited about these ‘magical’ LLMs but forget that they’re just statistical models that predict the next ‘token’ in a sequence,” says Anku Rani, a PhD student in media arts and sciences and one of the lead authors of the study, along with fellow MAS PhD student Valdemar Danry, SM ’23.
The researchers also found, however, that certain AI styles were associated with stronger independent performance later on, even if they slowed people down at first. “AIs that ‘tell’ by providing direct answers are more likely to foster reliance, while those that ‘ask’ via Socratic questioning are better at engaging someone to actually learn how to discern the truth on their own,” says Danry. “But it’s very much a trade-off between speed and effort.”
