The benefits of medical AI assistance vary based on user expertise
Study finds non-experts deferred to LLM-based diagnostic assistance, even when it was wrong, while clinicians caught AI errors.
Study finds non-experts deferred to LLM-based diagnostic assistance, even when it was wrong, while clinicians caught AI errors.
MIT students and postdocs discussed science funding and research with policymakers in Washington during the MIT Science Policy Initiative’s annual Congressional Visit Days.
Read MoreAssistant Professor Bailey Flanigan has arrived at complex computational methods for helping democracy thrive.
Read MoreAssistant Professor Pat Pataranutaporn describes a new interface that lets everyday users glimpse inside an AI’s neural network before their chatbot ever says a word.
Read MoreStudents from the MIT Cybersecurity Clinic help local governments and other vulnerable organizations defend against digital threats.
Read MoreDuring the AI and Society Forum, leading MIT researchers examined critical questions about AI’s influence on employment and democracy.
Read MoreMIT economists found US companies tend to target employees earning a “wage premium,” which increases inequality but not necessarily productivity.
Read MoreA new debiasing technique called WRING avoids creating or amplifying biases that can occur with existing debiasing approaches.
Read MoreThe “EnergAIzer” method generates reliable results in seconds, enabling data center operators to efficiently allocate resources and reduce wasted energy.
Read MoreAs the School of Humanities, Arts, and Social Sciences marks 75 years, Dean Agustín Rayo reflects on how AI is reshaping higher education and why SHASS disciplines continue to be central to MIT’s mission.
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