<\/p>\n
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. <\/p>\n
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\u2014but by week four, they\u2019d 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 \u201cAI dependency paradox\u201d that has been observed in other domains, including medicine. <\/p>\n
\u201cUsers get excited about these \u2018magical\u2019 LLMs but forget that they\u2019re just statistical models that predict the next \u2018token\u2019 in a sequence,\u201d 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 \u201923. <\/p>\n
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. \u201cAIs that \u2018tell\u2019 by providing direct answers are more likely to foster reliance, while those that \u2018ask\u2019 via Socratic questioning are better at engaging someone to actually learn how to discern the truth on their own,\u201d says Danry. \u201cBut it\u2019s very much a trade-off between speed and effort.\u201d <\/p>\n
#brain<\/p>\n","protected":false},"excerpt":{"rendered":"
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