Fighting discrimination in mortgage lending
A new technique for removing bias in datasets can enable machine-learning models to make loan approval predictions that are both fair and accurate.
A new technique for removing bias in datasets can enable machine-learning models to make loan approval predictions that are both fair and accurate.
“Privid” could help officials gather secure public health data or enable transportation departments to monitor the density and flow of pedestrians, without learning personal information about people.
Researchers design a user-friendly interface that helps nonexperts make forecasts using data collected over time.
The findings may inform decisions on holding large outdoor gatherings amid future public health crises.
Former associate director succeeds founding director Lydia Snover; MIT 2022 Quality of Life Survey launches.
MLK Visiting Professor S. Craig Watkins looks beyond algorithm bias to an AI future where models more effectively deal with systemic inequality.
A machine-learning model for image classification that’s trained using synthetic data can rival one trained on the real thing, a study shows.
Students propose solutions to re-imagine the customer experience for Hong Kong’s airport city development.
Researchers create a mathematical framework to examine the genome and detect signatures of natural selection, deciphering the evolutionary past and future of non-coding DNA.
Chemical engineers use neural networks to discover the properties of metal-organic frameworks, for catalysis and other applications.
Empowering a global community of learners in displacement.
New effort empowers MIT researchers to shape real estate’s future and build responsibly and sustainably.
MEng graduate students engage with IBM to develop their research skills and solutions to real-world problems.
A new MIT-wide effort launched by the Institute for Data, Systems, and Society uses social science and computation to address systemic racism.
A new technique boosts models’ ability to reduce bias, even if the dataset used to train the model is unbalanced.