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Empowering systemic racism research at MIT and beyond
At the turn of the 20th century, W.E.B. Du Bois wrote about the conditions and culture of Black people in Philadelphia, documenting also the racist attitudes and beliefs that pervaded the white society around them. He described how unequal outcomes in domains like health could be attributed not only to racist ideas, but to racism embedded in American institutions.Almost 125 years later, the concept of “systemic racism” is central to the study of race. Centuries of data collection and analysis, like the work of Du Bois, document the mechanisms of racial inequity in law and institutions, and attempt to measure their impact.
“There’s extensive research showing racial discrimination and systemic inequity in essentially all sectors of American society,” explains Fotini Christia, the Ford International Professor of Social Sciences in the Department of Political Science, who directs the MIT Institute for Data, Systems, and Society (IDSS), where she also co-leads the Initiative on Combatting Systemic Racism (ICSR). “Newer research demonstrates how computational technologies, typically trained or reliant on historical data, can further entrench racial bias. But these same tools can also help to identify racially inequitable outcomes, to understand their causes and impacts, and even contribute to proposing solutions.” Learn more
Making it easier to verify an AI model’s responses
Despite their impressive capabilities, large language models are far from perfect. These artificial intelligence models sometimes “hallucinate” by generating incorrect or unsupported information in response to a query.Due to this hallucination problem, an LLM’s responses are often verified by human fact-checkers, especially if a model is deployed in a high-stakes setting like health care or finance. However, validation processes typically require people to read through long documents cited by the model, a task so onerous and error-prone it may prevent some users from deploying generative AI models in the first place.
To help human validators, MIT researchers created a user-friendly system that enables people to verify an LLM’s responses much more quickly. With this tool, called SymGen, an LLM generates responses with citations that point directly to the place in a source document, such as a given cell in a database. Learn more
MIT professor, cancer survivor, awarded for AI mammogram analysis program that better detects risks
BOSTON (WHDH) - Cancer survivor Dr. Regina Barzilay is a cancer survivor who is using her experience to help others.The professor at the Massachusetts Institute of Technology helped develop a way for artificial intelligence to analyze mammograms, with an algorithm that can detect small changes that might otherwise go unnoticed, which means possibly being able to predict developing cancers up to five years earlier.
“Once we finished developing the system we looked back at my own mammograms and it’s quite clear that my cancer was seen by machines at least two years before my diagnosis,” Barzilay said.
Now, 10 years cancer-free, Barzilay said her cancer battle led her to study ways to use AI predictive technology. Learn more
ARPA-H project to accelerate the discovery and development of new antibiotics using generative AI
Today, the U.S. Department of Health and Human Services (HHS) through the Advanced Research Projects Agency for Health (ARPA-H) announced funding for the Transforming Antibiotic R&D with Generative AI to stop Emerging Threats (TARGET) project, which will use AI to speed the discovery and development of new classes of antibiotics. This program is another action to support the United States’ longstanding commitment to combating antimicrobial resistance (AMR), from groundbreaking innovation to international collaboration. The U.S. is a global leader in the fight against AMR and has a demonstrated track record of progress in protecting people, animals, and the environment from the threat of AMR domestically and globally.“Antibiotic resistance is a real and urgent threat affecting millions of people. We need to prevent infections and conserve the antibiotics we have. We also urgently need new drugs to treat these increasingly resistant infections. This project will use AI to speed this needed innovation and help ensure we have the medicines we need to keep people alive,” said Secretary Xavier Becerra. Learn more
