Researchers discover a shortcoming that makes LLMs less reliable
Large language models can learn to mistakenly link certain sentence patterns with specific topics — and may then repeat these patterns instead of reasoning.
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Charting the future of AI, from safer answers to faster thinking
MIT PhD students who interned with the MIT-IBM Watson AI Lab Summer Program are pushing AI tools to be more flexible, efficient, and grounded in truth.
Q&A: How MITHIC is fostering a culture of collaboration at MIT
A presidential initiative, the MIT Human Insight Collaborative is supporting new interdisciplinary initiatives and projects across the Institute.
MIT and Mass General Hospital researchers find disparities in organ acceptance
In an analysis of over 160,000 transplant candidates, researchers found that race is linked to how likely an organ offer is to be accepted on behalf of a patient.
LLMs factor in unrelated information when recommending medical treatments
Researchers find nonclinical information in patient messages — like typos, extra white space, and colorful language — reduces the accuracy of an AI model.
Envisioning a future where health care tech leaves some behind
The winning essay of the Envisioning the Future of Computing Prize puts health care disparities at the forefront.
Study shows vision-language models can’t handle queries with negation words
Words like “no” and “not” can cause this popular class of AI models to fail unexpectedly in high-stakes settings, such as medical diagnosis.
School of Engineering faculty and staff receive awards for winter 2025
Faculty members and researchers honored in recognition of their scholarship, service, and overall excellence.
MIT faculty, alumni named 2025 Sloan Research Fellows
Annual award honors early-career researchers for creativity, innovation, and research accomplishments.
Study reveals AI chatbots can detect race, but racial bias reduces response empathy
Researchers at MIT, NYU, and UCLA develop an approach to help evaluate whether large language models like GPT-4 are equitable enough to be clinically viable for mental health support.
MIT affiliates named 2024 Schmidt Sciences AI2050 Fellows
Five MIT faculty members and two additional alumni are honored with fellowships to advance research on beneficial AI.
AI in health should be regulated, but don’t forget about the algorithms, researchers say
In a recent commentary, a team from MIT, Equality AI, and Boston University highlights the gaps in regulation for AI models and non-AI algorithms in health care.
Researchers reduce bias in AI models while preserving or improving accuracy
A new technique identifies and removes the training examples that contribute most to a machine-learning model’s failures.
Improving health, one machine learning system at a time
Marzyeh Ghassemi works to ensure health-care models are trained to be robust and fair.
3 Questions: Should we label AI systems like we do prescription drugs?
Researchers argue that in health care settings, “responsible use” labels could ensure AI systems are deployed appropriately.