MIT Laboratory for Information and Decision Systems

10/09/2026

When companies use the same hiring , is that necessarily a bad thing?

As more employers turn to AI to screen resumes and evaluate job applicants, researchers have raised concerns about what happens when many companies rely on the same algorithm.

Known as "algorithmic monoculture," this practice could potentially reinforce biases and systematically exclude certain job seekers from employment opportunities.

But new research from LIDS P*s Brian Hedden and Manish Raghavan suggests the picture is more complicated.

Their study found that using the same hiring algorithm across multiple companies isn't always harmful to job seekers. Depending on how the algorithm is used, it could be less problematic than previously thought — and may even benefit applicants in certain situations.

Learn more at MIT News: https://bit.ly/4hSEFpg

MIT EECS Department MIT Schwarzman College of Computing MIT School of Engineering MIT Sloan School of Management MIT School of Humanities, Arts, and Social Sciences

10/07/2026

Computational tools for society’s most complex challenges

For LIDS PI Cathy Wu, the path to improving transportation systems began long before she became an MIT researcher.

Growing up, she wanted to make the world a better place. She watched her dad contend with traffic on his daily commute. And she played games like “SimCity,” where she could experiment with how cities and transportation systems worked.

Today, as an associate professor in the MIT Department of Civil and Environmental Engineering (CEE) and the Institute for Data, Systems, and Society (IDSS), and a principal investigator in LIDS, Wu uses machine learning and reinforcement learning to develop safer, more efficient, and more reliable strategies for improving transportation and other complex systems.

“Societal impact is a lifelong endeavor, not something to be accomplished in a few years,” Wu says. “It will take years to really understand what’s going on and where the real problems are. In the meantime, try to be helpful. Be curious. Ask many questions.”

Read more about Wu’s journey and research in her MIT News profile: https://bit.ly/4AStaGR

📷 Jerard Welcome

MIT Schwarzman College of Computing MIT Institute for Data, Systems, and Society

09/30/2026

This game-playing AI is the new champ at Stratego

AI has conquered chess, Go, and poker. Stratego presented a different kind of challenge.

LIDS graduate student Zhiyuan Fan, PI Gabriele Farina, and collaborators from Carnegie Mellon University, New York University, and Stanford University developed an AI system that defeated top-ranked human Stratego players by a large margin — something previous AI systems had been unable to achieve.

What makes Stratego so difficult? Players have to make strategic decisions without knowing the identities of their opponent’s pieces, creating an enormous number of possible scenarios.

“With Stratego, there is an explosion of possible universes you might have to deal with,” Farina says.

The advances could ultimately have applications beyond games, helping AI tackle complex strategic decision-making problems such as military planning and business negotiations.

Learn more at MIT News: https://bit.ly/4hqqnf6
And read the paper, published today in Nature: https://www.nature.com/articles/s41586-026-11036-y

The team includes: lead author Samuel Sokota, a graduate student at Carnegie Mellon; Eugene Vinitsky, an assistant professor at NYU; Zico Kolter, a professor at Carnegie Mellon; Hengyuan Hu, a graduate student at Stanford; Zhiyuan Fan, an EECS graduate student at MIT; and Gabriele Farina, an assistant professor in the MIT Department of Electrical Engineering and Computer Science (MIT EECS), principal investigator at the MIT Laboratory for Information and Decision Systems (LIDS), and senior author of a paper on this AI system.

MIT Schwarzman College of Computing MIT School of Engineering MIT EECS Department

09/16/2026

Enabling AI for safety-critical situations

For AI operating in the physical world, getting an answer that’s “pretty close” may not be enough.

A new algorithm from LIDS graduate students Zeyang Li and Kaveh Alim and PI Navid Azizan helps pretrained generative AI models produce high-quality outputs that also obey strict constraints — without retraining the models.

Called HardFlow, the method could make generative AI more useful in areas like robotics, control of physical systems, and computer vision, where certain requirements are simply nonnegotiable.

“The promise of generative AI is its ability to explore a rich space of possibilities, but the real world places boundaries on which possibilities are acceptable,” says Azizan.

Learn more and read the paper, appearing this week in IEEE Transactions on Pattern Analysis and Machine Intelligence: https://bit.ly/4xydxBN

MIT Institute for Data, Systems, and Society MIT Mechanical Engineering MIT Schwarzman College of Computing MIT School of Engineering

09/11/2026

🗳️ Voters are turning to AI for election information. But what exactly are the chatbots telling them?

The LLM Election Observatory, a new MIT project from LIDS PI Chara Podimata and collaborators, aims to find out.

Beginning with the current U.S. midterm cycle, researchers are tracking how nearly a dozen large language models answer the same questions about prominent candidates and political issues — and how those answers change as campaigns unfold and AI models are updated.

The team is also investigating whether responses vary depending on who claims to be asking, including differences in gender, race, location, and political leaning.

The project builds on research from the 2024 presidential election that found LLMs adapted to changing campaigns and internalized biases about candidates, including perceptions of who appeared more compassionate or combative.

Ultimately, the researchers hope the Observatory will provide scholars and policymakers with an ongoing record of how AI systems interpret, shape, and sometimes distort democratic life.

Read more in The New York Times: https://www.nytimes.com/2026/09/10/business/media/ai-chatbots-election-misinformation.html

And explore the project: https://www.llmelectionobservatory.org/

The team includes: Podimata, an Assistant Professor of Operations Research and Statistics in the Sloan School of Management, Political Science Professors Adam Berinsky and Charles Stewart , III, and students Nicolas Emmenegger, Young-Kyung Kim, and Alessandro Morosini.

MIT Schwarzman College of Computing MIT Sloan School of Management MIT School of Humanities, Arts, and Social Sciences

Artificial intelligence is quickly becoming a go-to stop for voters looking for information about political races. Researchers are studying the effects.

08/27/2026

When it comes to AI-assisted medical diagnosis, one size may not fit all.

A new study from LIDS PI Marzyeh Ghassemi and collaborators found that AI assistance generally helped both clinicians and non-experts more accurately diagnose skin diseases—but AI explanations affected the two groups differently.

Non-experts were more likely to trust AI recommendations, even when they were wrong. They became especially reliant on the AI when its recommendations included LLM-generated explanations—even when those explanations were incorrect, vague, or overly generic.

Clinicians were less likely to be misled by incorrect AI advice and actually performed best when they received only the AI model’s prediction, without an added explanation.

The research suggests that effective medical AI systems may need to account for who is using them—and provide different kinds of support based on users’ expertise.

Read more at MIT News: https://bit.ly/4gv0jR7

The research team includes: Xuhai ‘Orson’ Xu, Haoyu Hu, Haoran Zhang, Will Ke Wang, Reina Wang, Luis R. Soenksen, Omar Badri, Sheharbano Jafry, Elise Burger, Lotanna Nwandu, Apoorva Mehta, Erik P. Duhaime, Asif Qasim, Hause Lin, Janis Karleen Pereira, Jonathan Hershon, Paulius Mui, Alejandro A. Gru, Noémie Elhadad, Lena Mamykina, Matthew Groh, Philipp Tschandl, Roxana Daneshjou & Marzyeh Ghassemi

MIT EECS Department MIT Schwarzman College of Computing MIT School of Engineering

08/21/2026

Congratulations to LIDS PI Alexander “Sasha” Rakhlin, who has been named director of the MIT Statistics and Data Science Center (SDSC)!

An expert in machine learning, statistics, and computation, Rakhlin succeeds Professor Ankur Moitra and plans to deepen connections among researchers across MIT — from economics and political science to physics and engineering.

“Statistics is a shared language across MIT,” Rakhlin says. “Collaborations in areas from biology to nuclear fusion have shown how statistical thinking accelerates science itself.”

As director, he hopes to strengthen SDSC as a home for the foundations of data science and AI and a bridge to the scientific and societal challenges where those foundations can have an impact.

Read more at MIT News: https://bit.ly/4gcm08m

MIT Schwarzman College of Computing MIT Institute for Data, Systems, and Society

08/18/2026

Nature has spent millions of years developing materials that bend, twist, expand, and adapt. What if engineers could borrow those design principles to create new materials?

MIT researchers have developed a mathematical framework that breaks down natural structures — like pinecones, tree bark, and sweetgum seedpods — into building blocks that can be mixed and matched to design manufacturable, adaptive materials.

Developed by LIDS PI Gioele Zardini, graduate student Lee Marom, MIT Architecture’s Skylar Tibbits, and CEE’s Markus J. Buehler, the framework could help engineers design robotic grippers, aerospace components, and other technologies with complex behaviors inspired by nature.

Learn more at MIT News: https://bit.ly/4zr0VOK

08/05/2026

Using reason, again and again
A new MIT News profile highlights the interdisciplinary journey of LIDS PI Brian Hedden, whose work bridges philosophy, computer science, and ethics.
After returning to his alma mater in 2025, Hedden joined MIT as a shared faculty member between the MIT Schwarzman College of Computing, the department of Linguistics and Philosophy, and the MIT EECS Department. He also serves as associate dean for MIT's Social and Ethical Responsibilities of Computing (SERC) initiative.
Read more: https://bit.ly/4wadCuy

MIT School of Engineering MIT School of Humanities, Arts, and Social Sciences

07/24/2026

Remembering MIT Professor Emeritus Dimitri Bertsekas

LIDS PI Dimitri Bertsekas leaves an extraordinary legacy as a researcher, educator, and author whose work helped shape modern optimization, control theory, reinforcement learning, and artificial intelligence.

Over the course of his career, he authored more than 20 influential books and textbooks, mentored generations of students, and made lasting contributions to both academia and industry. His clear, elegant writing and passion for teaching have had a profound impact on researchers around the world.

Read a tribute to his life, work, and enduring legacy: https://bit.ly/3Rg1fip

MIT EECS Department MIT Schwarzman College of Computing MIT School of Engineering