Study: When allocating scarce resources with AI, randomization can improve fairness
Introducing structured randomization into decisions based on machine-learning model predictions can address inherent uncertainties while maintaining efficiency.
Introducing structured randomization into decisions based on machine-learning model predictions can address inherent uncertainties while maintaining efficiency.
A new study shows someone’s beliefs about an LLM play a significant role in the model’s performance and are important for how it is deployed.
The model could help clinicians assess breast cancer stage and ultimately help in reducing overtreatment.
A new technique enables users to compare several large models and choose the one that works best for their task.
More accurate uncertainty estimates could help users decide about how and when to use machine-learning models in the real world.
Fifteen new faculty members join six of the school’s academic departments.
The MIT Schwarzman College of Computing building will form a new cluster of connectivity across a spectrum of disciplines in computing and artificial intelligence.
MIT spinout DataCebo helps companies bolster their datasets by creating synthetic data that mimic the real thing.
Tamara Broderick uses statistical approaches to understand and quantify the uncertainty that can affect study results.
By breaking an intractable problem into smaller chunks, a deep-learning technique identifies the optimal areas for thinning out traffic in a warehouse.