Explained: How to tell if artificial intelligence is working the way we want it to
“Interpretability methods” seek to shed light on how machine-learning models make predictions, but researchers say to proceed with caution.
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Exploring emerging topics in artificial intelligence policy
The second AI Policy Forum Symposium convened global stakeholders across sectors to discuss critical policy questions in artificial intelligence.
In bias we trust?
Explanation methods that help users determine whether to trust machine-learning model predictions can be less accurate for disadvantaged subgroups, a new study finds.
Artificial intelligence predicts patients’ race from their medical images
Study shows AI can identify self-reported race from medical images that contain no indications of race detectable by human experts.
Can artificial intelligence overcome the challenges of the health care system?
MIT and Mass General Brigham researchers and physicians connect in person to bring AI into mainstream health care.
Injecting fairness into machine-learning models
A new technique boosts models’ ability to reduce bias, even if the dataset used to train the model is unbalanced.
The downside of machine learning in health care
Assistant Professor Marzyeh Ghassemi explores how hidden biases in medical data could compromise artificial intelligence approaches.
The promise and pitfalls of artificial intelligence explored at TEDxMIT event
MIT scientists discuss the future of AI with applications across many sectors, as a tool that can be both beneficial and harmful.
Machine-learning system flags remedies that might do more harm than good
The system could help physicians select the least risky treatments in urgent situations, such as treating sepsis.
The potential of artificial intelligence to bring equity in health care
Nearly 1,400 joined the AI for Health Care Equity Conference that explored new AI technologies as a platform for change.
Automating artificial intelligence for medical decision-making
Model replaces the laborious process of annotating massive patient datasets by hand.
Using machine learning to improve patient care
New CSAIL research employs many types of medical data, including electronic health records, to predict outcomes in hospitals.
How machine learning can help with voice disorders
By detecting signs of vocal misuse, system from CSAIL and Mass General could eventually be used to help diagnose voice disorders.