Institute for Data Systems and Society (IDSS)

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Dealing with the limitations of our noisy world

March 1, 2024

Tamara Broderick uses statistical approaches to understand and quantify the uncertainty that can affect study results.

Automated system teaches users when to collaborate with an AI assistant

January 3, 2024

MIT researchers develop a customized onboarding process that helps a human learn when a model’s advice is trustworthy.

AI accelerates problem-solving in complex scenarios

December 5, 2023

A new, data-driven approach could lead to better solutions for tricky optimization problems like global package routing or power grid operation.

Rewarding excellence in open data

November 22, 2023

MIT researchers who share their data recognized at second annual awards celebration.

Celebrating the impact of IDSS

August 10, 2023

Taking the place of IDSS’s annual statistics and data science conference SDSCon, the celebration also doubled as a way to recognize Dahleh for his work creating and executing the vision of IDSS as he prepares to step down from his director position this summer.

Researchers create a tool for accurately simulating complex systems

May 10, 2023

The system they developed eliminates a source of bias in simulations, leading to improved algorithms that can boost the performance of applications.

Caroline Uhler named SIAM Fellow for 2023

April 3, 2023

In the award announcement, SIAM noted that Uhler is being honored for her “fundamental contributions at the interface of statistics, machine learning, and biology”.

A method for designing neural networks optimally suited for certain tasks

March 31, 2023

With the right building blocks, machine-learning models can more accurately perform tasks like fraud detection or spam filtering.

Subtle biases in AI can influence emergency decisions

January 11, 2023

But the harm from a discriminatory AI system can be minimized if the advice it delivers is properly framed, an MIT team has shown.

Unpacking the “black box” to build better AI models

January 11, 2023

Stefanie Jegelka seeks to understand how machine-learning models behave, to help researchers build more robust models for applications in biology, computer vision, optimization, and more.