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      Inclusive research for social change

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      Pair a decades-old program dedicated to creating research opportunities for underrepresented minorities and populations with a growing initiative committed to tackling the very issues at the heart of such disparities, and you’ll get a transformative partnership that only MIT can deliver. 

      Since 1986, the MIT Summer Research Program (MSRP) has led an institutional effort to prepare underrepresented students (minorities, women in STEM, or students with low socioeconomic status) for doctoral education by pairing them with MIT labs and research groups. For the past three years, the Initiative on Combatting Systemic Racism (ICSR), a cross-disciplinary research collaboration led by MIT’s Institute for Data, Systems, and Society (IDSS), has joined them in their mission, helping bring the issue full circle by providing MSRP students with the opportunity to use big data and computational tools to create impactful changes toward racial equity.

      “ICSR has further enabled our direct engagement with undergrads, both within and outside of MIT,” says Fotini Christia, the Ford International Professor of the Social Sciences, associate director of IDSS, and co-organizer for the initiative. “We’ve found that this line of research has attracted students interested in examining these topics with the most rigorous methods.”

      The initiative fits well under the IDSS banner, as IDSS research seeks solutions to complex societal issues through a multidisciplinary approach that includes statistics, computation, modeling, social science methodologies, human behavior, and an understanding of complex systems. With the support of faculty and researchers from all five schools and the MIT Schwarzman College of Computing, the objective of ICSR is to work on an array of different societal aspects of systemic racism through a set of verticals including policing, housing, health care, and social media.

      Where passion meets impact

      Grinnell senior Mia Hines has always dreamed of using her love for computer science to support social justice. She has experience working with unhoused people and labor unions, and advocating for Indigenous peoples’ rights. When applying to college, she focused her essay on using technology to help Syrian refugees.

      “As a Black woman, it’s very important to me that we focus on these areas, especially on how we can use technology to help marginalized communities,” Hines says. “And also, how do we stop technology or improve technology that is already hurting marginalized communities?”   

      Through MSRP, Hines was paired with research advisor Ufuoma Ovienmhada, a fourth-year doctoral student in the Department of Aeronautics and Astronautics at MIT. A member of Professor Danielle Wood’s Space Enabled research group at MIT’s Media Lab, Ovienmhada received funding from an ICSR Seed Grant and NASA’s Applied Sciences Program to support her ongoing research measuring environmental injustice and socioeconomic disparities in prison landscapes. 

      “I had been doing satellite remote sensing for environmental challenges and sustainability, starting out looking at coastal ecosystems, when I learned about an issue called ‘prison ecology,’” Ovienmhada explains. “This refers to the intersection of mass incarceration and environmental justice.”

      Ovienmhada’s research uses satellite remote sensing and environmental data to characterize exposures to different environmental hazards such as air pollution, extreme heat, and flooding. “This allows others to use these datasets for real-time advocacy, in addition to creating public awareness,” she says.

      Focused especially on extreme heat, Hines used satellite remote sensing to monitor the fluctuation of temperature to assess the risk being imposed on prisoners, including death, especially in states like Texas, where 75 percent of prisons either don’t have full air conditioning or have none at all.

      “Before this project I had done little to no work with geospatial data, and as a budding data scientist, getting to work with and understanding different types of data and resources is really helpful,” Hines says. “I was also funded and afforded the flexibility to take advantage of IDSS’s Data Science and Machine Learning online course. It was really great to be able to do that and learn even more.”

      Filling the gap

      Much like Hines, Harvey Mudd senior Megan Li was specifically interested in the IDSS-supported MSRP projects. She was drawn to the interdisciplinary approach, and she seeks in her own work to apply computational methods to societal issues and to make computer science more inclusive, considerate, and ethical. 

      Working with Aurora Zhang, a grad student in IDSS’s Social and Engineering Systems PhD program, Li used county-level data on income and housing prices to quantify and visualize how affordability based on income alone varies across the United States. She then expanded the analysis to include assets and debt to determine the most common barriers to home ownership.

      “I spent my day-to-day looking at census data and writing Python scripts that could work with it,” reports Li. “I also reached out to the Census Bureau directly to learn a little bit more about how they did their data collection, and discussed questions related to some of their previous studies and working papers that I had reviewed.” 

      Outside of actual day-to-day research, Li says she learned a lot in conversations with fellow researchers, particularly changing her “skeptical view” of whether or not mortgage lending algorithms would help or hurt home buyers in the approval process. “I think I have a little bit more faith now, which is a good thing.”

      “Harvey Mudd is undergraduate-only, and while professors do run labs here, my specific research areas are not well represented,” Li says. “This opportunity was enormous in that I got the experience I need to see if this research area is actually something that I want to do long term, and I got more mirrors into what I would be doing in grad school from talking to students and getting to know faculty.”

      Closing the loop

      While participating in MSRP offered crucial research experience to Hines, the ICSR projects enabled her to engage in topics she’s passionate about and work that could drive tangible societal change.

      “The experience felt much more concrete because we were working on these very sophisticated projects, in a supportive environment where people were very excited to work with us,” she says.

      A significant benefit for Li was the chance to steer her research in alignment with her own interests. “I was actually given the opportunity to propose my own research idea, versus supporting a graduate student’s work in progress,” she explains. 

      For Ovienmhada, the pairing of the two initiatives solidifies the efforts of MSRP and closes a crucial loop in diversity, equity, and inclusion advocacy. 

      “I’ve participated in a lot of different DEI-related efforts and advocacy and one thing that always comes up is the fact that it’s not just about bringing people in, it’s also about creating an environment and opportunities that align with people’s values,” Ovienmhada says. “Programs like MSRP and ICSR create opportunities for people who want to do work that’s aligned with certain values by providing the needed mentoring and financial support.” More

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      How an archeological approach can help leverage biased data in AI to improve medicine

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      The classic computer science adage “garbage in, garbage out” lacks nuance when it comes to understanding biased medical data, argue computer science and bioethics professors from MIT, Johns Hopkins University, and the Alan Turing Institute in a new opinion piece published in a recent edition of the New England Journal of Medicine (NEJM). The rising popularity of artificial intelligence has brought increased scrutiny to the matter of biased AI models resulting in algorithmic discrimination, which the White House Office of Science and Technology identified as a key issue in their recent Blueprint for an AI Bill of Rights. 

      When encountering biased data, particularly for AI models used in medical settings, the typical response is to either collect more data from underrepresented groups or generate synthetic data making up for missing parts to ensure that the model performs equally well across an array of patient populations. But the authors argue that this technical approach should be augmented with a sociotechnical perspective that takes both historical and current social factors into account. By doing so, researchers can be more effective in addressing bias in public health. 

      “The three of us had been discussing the ways in which we often treat issues with data from a machine learning perspective as irritations that need to be managed with a technical solution,” recalls co-author Marzyeh Ghassemi, an assistant professor in electrical engineering and computer science and an affiliate of the Abdul Latif Jameel Clinic for Machine Learning in Health (Jameel Clinic), the Computer Science and Artificial Intelligence Laboratory (CSAIL), and Institute of Medical Engineering and Science (IMES). “We had used analogies of data as an artifact that gives a partial view of past practices, or a cracked mirror holding up a reflection. In both cases the information is perhaps not entirely accurate or favorable: Maybe we think that we behave in certain ways as a society — but when you actually look at the data, it tells a different story. We might not like what that story is, but once you unearth an understanding of the past you can move forward and take steps to address poor practices.” 

      Data as artifact 

      In the paper, titled “Considering Biased Data as Informative Artifacts in AI-Assisted Health Care,” Ghassemi, Kadija Ferryman, and Maxine Mackintosh make the case for viewing biased clinical data as “artifacts” in the same way anthropologists or archeologists would view physical objects: pieces of civilization-revealing practices, belief systems, and cultural values — in the case of the paper, specifically those that have led to existing inequities in the health care system. 

      For example, a 2019 study showed that an algorithm widely considered to be an industry standard used health-care expenditures as an indicator of need, leading to the erroneous conclusion that sicker Black patients require the same level of care as healthier white patients. What researchers found was algorithmic discrimination failing to account for unequal access to care.  

      In this instance, rather than viewing biased datasets or lack of data as problems that only require disposal or fixing, Ghassemi and her colleagues recommend the “artifacts” approach as a way to raise awareness around social and historical elements influencing how data are collected and alternative approaches to clinical AI development. 

      “If the goal of your model is deployment in a clinical setting, you should engage a bioethicist or a clinician with appropriate training reasonably early on in problem formulation,” says Ghassemi. “As computer scientists, we often don’t have a complete picture of the different social and historical factors that have gone into creating data that we’ll be using. We need expertise in discerning when models generalized from existing data may not work well for specific subgroups.” 

      When more data can actually harm performance 

      The authors acknowledge that one of the more challenging aspects of implementing an artifact-based approach is being able to assess whether data have been racially corrected: i.e., using white, male bodies as the conventional standard that other bodies are measured against. The opinion piece cites an example from the Chronic Kidney Disease Collaboration in 2021, which developed a new equation to measure kidney function because the old equation had previously been “corrected” under the blanket assumption that Black people have higher muscle mass. Ghassemi says that researchers should be prepared to investigate race-based correction as part of the research process. 

      In another recent paper accepted to this year’s International Conference on Machine Learning co-authored by Ghassemi’s PhD student Vinith Suriyakumar and University of California at San Diego Assistant Professor Berk Ustun, the researchers found that assuming the inclusion of personalized attributes like self-reported race improve the performance of ML models can actually lead to worse risk scores, models, and metrics for minority and minoritized populations.  

      “There’s no single right solution for whether or not to include self-reported race in a clinical risk score. Self-reported race is a social construct that is both a proxy for other information, and deeply proxied itself in other medical data. The solution needs to fit the evidence,” explains Ghassemi. 

      How to move forward 

      This is not to say that biased datasets should be enshrined, or biased algorithms don’t require fixing — quality training data is still key to developing safe, high-performance clinical AI models, and the NEJM piece highlights the role of the National Institutes of Health (NIH) in driving ethical practices.  

      “Generating high-quality, ethically sourced datasets is crucial for enabling the use of next-generation AI technologies that transform how we do research,” NIH acting director Lawrence Tabak stated in a press release when the NIH announced its $130 million Bridge2AI Program last year. Ghassemi agrees, pointing out that the NIH has “prioritized data collection in ethical ways that cover information we have not previously emphasized the value of in human health — such as environmental factors and social determinants. I’m very excited about their prioritization of, and strong investments towards, achieving meaningful health outcomes.” 

      Elaine Nsoesie, an associate professor at the Boston University of Public Health, believes there are many potential benefits to treating biased datasets as artifacts rather than garbage, starting with the focus on context. “Biases present in a dataset collected for lung cancer patients in a hospital in Uganda might be different from a dataset collected in the U.S. for the same patient population,” she explains. “In considering local context, we can train algorithms to better serve specific populations.” Nsoesie says that understanding the historical and contemporary factors shaping a dataset can make it easier to identify discriminatory practices that might be coded in algorithms or systems in ways that are not immediately obvious. She also notes that an artifact-based approach could lead to the development of new policies and structures ensuring that the root causes of bias in a particular dataset are eliminated. 

      “People often tell me that they are very afraid of AI, especially in health. They’ll say, ‘I’m really scared of an AI misdiagnosing me,’ or ‘I’m concerned it will treat me poorly,’” Ghassemi says. “I tell them, you shouldn’t be scared of some hypothetical AI in health tomorrow, you should be scared of what health is right now. If we take a narrow technical view of the data we extract from systems, we could naively replicate poor practices. That’s not the only option — realizing there is a problem is our first step towards a larger opportunity.”  More

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      Artificial intelligence for augmentation and productivity

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      The MIT Stephen A. Schwarzman College of Computing has awarded seed grants to seven projects that are exploring how artificial intelligence and human-computer interaction can be leveraged to enhance modern work spaces to achieve better management and higher productivity.

      Funded by Andrew W. Houston ’05 and Dropbox Inc., the projects are intended to be interdisciplinary and bring together researchers from computing, social sciences, and management.

      The seed grants can enable the project teams to conduct research that leads to bigger endeavors in this rapidly evolving area, as well as build community around questions related to AI-augmented management.

      The seven selected projects and research leads include:

      “LLMex: Implementing Vannevar Bush’s Vision of the Memex Using Large Language Models,” led by Patti Maes of the Media Lab and David Karger of the Department of Electrical Engineering and Computer Science (EECS) and the Computer Science and Artificial Intelligence Laboratory (CSAIL). Inspired by Vannevar Bush’s Memex, this project proposes to design, implement, and test the concept of memory prosthetics using large language models (LLMs). The AI-based system will intelligently help an individual keep track of vast amounts of information, accelerate productivity, and reduce errors by automatically recording their work actions and meetings, supporting retrieval based on metadata and vague descriptions, and suggesting relevant, personalized information proactively based on the user’s current focus and context.

      “Using AI Agents to Simulate Social Scenarios,” led by John Horton of the MIT Sloan School of Management and Jacob Andreas of EECS and CSAIL. This project imagines the ability to easily simulate policies, organizational arrangements, and communication tools with AI agents before implementation. Tapping into the capabilities of modern LLMs to serve as a computational model of humans makes this vision of social simulation more realistic, and potentially more predictive.

      “Human Expertise in the Age of AI: Can We Have Our Cake and Eat it Too?” led by Manish Raghavan of MIT Sloan and EECS, and Devavrat Shah of EECS and the Laboratory for Information and Decision Systems. Progress in machine learning, AI, and in algorithmic decision aids has raised the prospect that algorithms may complement human decision-making in a wide variety of settings. Rather than replacing human professionals, this project sees a future where AI and algorithmic decision aids play a role that is complementary to human expertise.

      “Implementing Generative AI in U.S. Hospitals,” led by Julie Shah of the Department of Aeronautics and Astronautics and CSAIL, Retsef Levi of MIT Sloan and the Operations Research Center, Kate Kellog of MIT Sloan, and Ben Armstrong of the Industrial Performance Center. In recent years, studies have linked a rise in burnout from doctors and nurses in the United States with increased administrative burdens associated with electronic health records and other technologies. This project aims to develop a holistic framework to study how generative AI technologies can both increase productivity for organizations and improve job quality for workers in health care settings.

      “Generative AI Augmented Software Tools to Democratize Programming,” led by Harold Abelson of EECS and CSAIL, Cynthia Breazeal of the Media Lab, and Eric Klopfer of the Comparative Media Studies/Writing. Progress in generative AI over the past year is fomenting an upheaval in assumptions about future careers in software and deprecating the role of coding. This project will stimulate a similar transformation in computing education for those who have no prior technical training by creating a software tool that could eliminate much of the need for learners to deal with code when creating applications.

      “Acquiring Expertise and Societal Productivity in a World of Artificial Intelligence,” led by David Atkin and Martin Beraja of the Department of Economics, and Danielle Li of MIT Sloan. Generative AI is thought to augment the capabilities of workers performing cognitive tasks. This project seeks to better understand how the arrival of AI technologies may impact skill acquisition and productivity, and to explore complementary policy interventions that will allow society to maximize the gains from such technologies.

      “AI Augmented Onboarding and Support,” led by Tim Kraska of EECS and CSAIL, and Christoph Paus of the Department of Physics. While LLMs have made enormous leaps forward in recent years and are poised to fundamentally change the way students and professionals learn about new tools and systems, there is often a steep learning curve which people have to climb in order to make full use of the resource. To help mitigate the issue, this project proposes the development of new LLM-powered onboarding and support systems that will positively impact the way support teams operate and improve the user experience. More

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      How machine learning models can amplify inequities in medical diagnosis and treatment

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      Prior to receiving a PhD in computer science from MIT in 2017, Marzyeh Ghassemi had already begun to wonder whether the use of AI techniques might enhance the biases that already existed in health care. She was one of the early researchers to take up this issue, and she’s been exploring it ever since. In a new paper, Ghassemi, now an assistant professor in MIT’s Department of Electrical Science and Engineering (EECS), and three collaborators based at the Computer Science and Artificial Intelligence Laboratory, have probed the roots of the disparities that can arise in machine learning, often causing models that perform well overall to falter when it comes to subgroups for which relatively few data have been collected and utilized in the training process. The paper — written by two MIT PhD students, Yuzhe Yang and Haoran Zhang, EECS computer scientist Dina Katabi (the Thuan and Nicole Pham Professor), and Ghassemi — was presented last month at the 40th International Conference on Machine Learning in Honolulu, Hawaii.

      In their analysis, the researchers focused on “subpopulation shifts” — differences in the way machine learning models perform for one subgroup as compared to another. “We want the models to be fair and work equally well for all groups, but instead we consistently observe the presence of shifts among different groups that can lead to inferior medical diagnosis and treatment,” says Yang, who along with Zhang are the two lead authors on the paper. The main point of their inquiry is to determine the kinds of subpopulation shifts that can occur and to uncover the mechanisms behind them so that, ultimately, more equitable models can be developed.

      The new paper “significantly advances our understanding” of the subpopulation shift phenomenon, claims Stanford University computer scientist Sanmi Koyejo. “This research contributes valuable insights for future advancements in machine learning models’ performance on underrepresented subgroups.”

      Camels and cattle

      The MIT group has identified four principal types of shifts — spurious correlations, attribute imbalance, class imbalance, and attribute generalization — which, according to Yang, “have never been put together into a coherent and unified framework. We’ve come up with a single equation that shows you where biases can come from.”

      Biases can, in fact, stem from what the researchers call the class, or from the attribute, or both. To pick a simple example, suppose the task assigned to the machine learning model is to sort images of objects — animals in this case — into two classes: cows and camels. Attributes are descriptors that don’t specifically relate to the class itself. It might turn out, for instance, that all the images used in the analysis show cows standing on grass and camels on sand — grass and sand serving as the attributes here. Given the data available to it, the machine could reach an erroneous conclusion — namely that cows can only be found on grass, not on sand, with the opposite being true for camels. Such a finding would be incorrect, however, giving rise to a spurious correlation, which, Yang explains, is a “special case” among subpopulation shifts — “one in which you have a bias in both the class and the attribute.”

      In a medical setting, one could rely on machine learning models to determine whether a person has pneumonia or not based on an examination of X-ray images. There would be two classes in this situation, one consisting of people who have the lung ailment, another for those who are infection-free. A relatively straightforward case would involve just two attributes: the people getting X-rayed are either female or male. If, in this particular dataset, there were 100 males diagnosed with pneumonia for every one female diagnosed with pneumonia, that could lead to an attribute imbalance, and the model would likely do a better job of correctly detecting pneumonia for a man than for a woman. Similarly, having 1,000 times more healthy (pneumonia-free) subjects than sick ones would lead to a class imbalance, with the model biased toward healthy cases. Attribute generalization is the last shift highlighted in the new study. If your sample contained 100 male patients with pneumonia and zero female subjects with the same illness, you still would like the model to be able to generalize and make predictions about female subjects even though there are no samples in the training data for females with pneumonia.

      The team then took 20 advanced algorithms, designed to carry out classification tasks, and tested them on a dozen datasets to see how they performed across different population groups. They reached some unexpected conclusions: By improving the “classifier,” which is the last layer of the neural network, they were able to reduce the occurrence of spurious correlations and class imbalance, but the other shifts were unaffected. Improvements to the “encoder,” one of the uppermost layers in the neural network, could reduce the problem of attribute imbalance. “However, no matter what we did to the encoder or classifier, we did not see any improvements in terms of attribute generalization,” Yang says, “and we don’t yet know how to address that.”

      Precisely accurate

      There is also the question of assessing how well your model actually works in terms of evenhandedness among different population groups. The metric normally used, called worst-group accuracy or WGA, is based on the assumption that if you can improve the accuracy — of, say, medical diagnosis — for the group that has the worst model performance, you would have improved the model as a whole. “The WGA is considered the gold standard in subpopulation evaluation,” the authors contend, but they made a surprising discovery: boosting worst-group accuracy results in a decrease in what they call “worst-case precision.” In medical decision-making of all sorts, one needs both accuracy — which speaks to the validity of the findings — and precision, which relates to the reliability of the methodology. “Precision and accuracy are both very important metrics in classification tasks, and that is especially true in medical diagnostics,” Yang explains. “You should never trade precision for accuracy. You always need to balance the two.”

      The MIT scientists are putting their theories into practice. In a study they’re conducting with a medical center, they’re looking at public datasets for tens of thousands of patients and hundreds of thousands of chest X-rays, trying to see whether it’s possible for machine learning models to work in an unbiased manner for all populations. That’s still far from the case, even though more awareness has been drawn to this problem, Yang says. “We are finding many disparities across different ages, gender, ethnicity, and intersectional groups.”

      He and his colleagues agree on the eventual goal, which is to achieve fairness in health care among all populations. But before we can reach that point, they maintain, we still need a better understanding of the sources of unfairness and how they permeate our current system. Reforming the system as a whole will not be easy, they acknowledge. In fact, the title of the paper they introduced at the Honolulu conference, “Change is Hard,” gives some indications as to the challenges that they and like-minded researchers face. More

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      Embracing the future we need

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      When you picture MIT doctoral students taking small PhD courses together, you probably don’t imagine them going on class field trips. But it does happen, sometimes, and one of those trips changed Andy Sun’s career.

      Today, Sun is a faculty member at the MIT Sloan School of Management and a leading global expert on integrating renewable energy into the electric grid. Back in 2007, Sun was an operations research PhD candidate with a diversified academic background: He had studied electrical engineering, quantum computing, and analog computing but was still searching for a doctoral research subject involving energy. 

      One day, as part of a graduate energy class taught by visiting professor Ignacio J. Pérez Arriaga, the students visited the headquarters of ISO-New England, the organization that operates New England’s entire power grid and wholesale electricity market. Suddenly, it hit Sun. His understanding of engineering, used to design and optimize computing systems, could be applied to the grid as a whole, with all its connections, circuitry, and need for efficiency. 

      “The power grids in the U.S. continent are composed of two major interconnections, the Western Interconnection, the Eastern Interconnection, and one minor interconnection, the Texas grid,” Sun says. “Within each interconnection, the power grid is one big machine, essentially. It’s connected by tens of thousands of miles of transmission lines, thousands of generators, and consumers, and if anything is not synchronized, the system may collapse. It’s one of the most complicated engineering systems.”

      And just like that, Sun had a subject he was motivated to pursue. “That’s how I got into this field,” he says. “Taking a field trip.”Sun has barely looked back. He has published dozens of papers about optimizing the flow of intermittent renewable energy through the electricity grid, a major practical issue for grid operators, while also thinking broadly about the future form of the grid and the process of making almost all energy renewable. Sun, who in 2022 rejoined MIT as the Iberdrola-Avangrid Associate Professor in Electric Power Systems, and is also an associate professor of operations research, emphasizes the urgency of rapidly switching to renewables.

      “The decarbonization of our energy system is fundamental,” Sun says. “It will change a lot of things because it has to. We don’t have much time to get there. Two decades, three decades is the window in which we have to get a lot of things done. If you think about how much money will need to be invested, it’s not actually that much. We should embrace this future that we have to get to.”

      Successful operations

      Unexpected as it may have been, Sun’s journey toward being an electricity grid expert was informed by all the stages of his higher education. Sun grew up in China, and received his BA in electronic engineering from Tsinghua University in Beijing, in 2003. He then moved to MIT, joining the Media Lab as a graduate student. Sun intended to study quantum computing but instead began working on analog computer circuit design for Professor Neil Gershenfeld, another person whose worldview influenced Sun.  

      “He had this vision about how optimization is very important in things,” Sun says. “I had never heard of optimization before.” 

      To learn more about it, Sun started taking MIT courses in operations research. “I really enjoyed it, especially the nonlinear optimization course taught by Robert Freund in the Operations Research Center,” he recalls. 

      Sun enjoyed it so much that after a while, he joined MIT’s PhD program in operations research, thanks to the guidance of Freund. Later, he started working with MIT Sloan Professor Dimitri Bertsimas, a leading figure in the field. Still, Sun hadn’t quite nailed down what he wanted to focus on within operations research. Thinking of Sun’s engineering skills, Bertsimas suggested that Sun look for a research topic related to energy. 

      “He wasn’t an expert in energy at that time, but he knew that there are important problems there and encouraged me to go ahead and learn,” Sun says. 

      So it was that Sun found himself in ISO-New England headquarters one day in 2007, finally knowing what he wanted to study, and quickly finding opportunities to start learning from the organization’s experts on electricity markets. By 2011, Sun had finished his MIT PhD dissertation. Based in part on ISO-New England data, the thesis presented new modeling to more efficiently integrate renewable energy into the grid; built some new modeling tools grid operators could use; and developed a way to add fair short-term energy auctions to an efficient grid system.

      The core problem Sun deals with is that, unlike some other sources of electricity, renewables tend to be intermittent, generating power in an uneven pattern over time. That’s not an insurmountable problem for grid operators, but it does require some new approaches. Many of the papers Sun has written focus on precisely how to increasingly draw upon intermittent energy sources while ensuring that the grid’s current level of functionality remains intact. This is also the focus of his 2021 book, co-authored with Antonio J. Conejo, “Robust Optimiziation in Electric Energy Systems.”

      “A major theme of my research is how to achieve the integration of renewables and still operate the system reliably,” Sun says. “You have to keep the balance of supply and demand. This requires many time scales of operation from multidecade planning, to monthly or annual maintenance, to daily operations, down through second-by-second. I work on problems in all these timescales.”

      “I sit in the interface between power engineering and operations research,” Sun says. “I’m not a power engineer, but I sit in this boundary, and I keep the problems in optimization as my motivation.”

      Culture shift

      Sun’s presence on the MIT campus represents a homecoming of sorts. After receiving his doctorate from MIT, Sun spent a year as a postdoc at IBM’s Thomas J. Watson Research Center, then joined the faculty at Georgia Tech, where he remained for a decade. He returned to the Institute in January of 2022.

      “I’m just very excited about the opportunity of being back at MIT,” Sun says. “The MIT Energy Initiative is a such a vibrant place, where many people come together to work on energy. I sit in Sloan, but one very strong point of MIT is there are not many barriers, institutionally. I really look forward to working with colleagues from engineering, Sloan, everywhere, moving forward. We’re moving in the right direction, with a lot of people coming together to break the traditional academic boundaries.” 

      Still, Sun warns that some people may be underestimating the severity of the challenge ahead and the need to implement changes right now. The assets in power grids have long life time, lasting multiple decades. That means investment decisions made now could affect how much clean power is being used a generation from now. 

      “We’re talking about a short timeline, for changing something as huge as how a society fundamentally powers itself with energy,” Sun says. “A lot of that must come from the technology we have today. Renewables are becoming much better and cheaper, so their use has to go up.”

      And that means more people need to work on issues of how to deploy and integrate renewables into everyday life, in the electric grid, transportation, and more. Sun hopes people will increasingly recognize energy as a huge growth area for research and applied work. For instance, when MIT President Sally Kornbluth gave her inaugural address on May 1 this year, she emphasized tackling the climate crisis as her highest priority, something Sun noticed and applauded. 

      “I think the most important thing is the culture,” Sun says. “Bring climate up to the front, and create the platform to encourage people to come together and work on this issue.” More

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      Understanding viral justice

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      In the wake of the Covid-19 pandemic, the word “viral” has a new resonance, and it’s not necessarily positive. Ruha Benjamin, a scholar who investigates the social dimensions of science, medicine, and technology, advocates a shift in perspective. She thinks justice can also be contagious. That’s the premise of Benjamin’s award-winning book “Viral Justice: How We Grow the World We Want,” as she shared with MIT Libraries staff on a June 14 visit. 

      “If this pandemic has taught us anything, it’s that something almost undetectable can be deadly, and that we can transmit it without even knowing,” said Benjamin, professor of African American studies at Princeton University. “Doesn’t this imply that small things, seemingly minor actions, decisions, or habits, could have exponential effects in the other direction, tipping the scales towards justice?” 

      To seek a more just world, Benjamin exhorted library staff to notice the ways exclusion is built into our daily lives, showing examples of park benches with armrests at regular intervals. On the surface they appear welcoming, but they also make lying down — or sleeping — impossible. This idea is taken to the extreme with “Pay and Sit,” an art installation by Fabian Brunsing in the form of a bench that deploys sharp spikes on the seat if the user doesn’t pay a meter. It serves as a powerful metaphor for discriminatory design. 

      “Dr. Benjamin’s keynote was seriously mind-blowing,” said Cherry Ibrahim, human resources generalist in the MIT Libraries. “One part that really grabbed my attention was when she talked about benches purposely designed to prevent unhoused people from sleeping on them. There are these hidden spikes in our community that we might not even realize because they don’t directly impact us.” 

      Benjamin urged the audience to look for those “spikes,” which new technologies can make even more insidious — gender and racial bias in facial recognition, the use of racial data in software used to predict student success, algorithmic bias in health care — often in the guise of progress. She coined the term “the New Jim Code” to describe the combination of coded bias and the imagined objectivity we ascribe to technology. 

      “At the MIT Libraries, we’re deeply concerned with combating inequities through our work, whether it’s democratizing access to data or investigating ways disparate communities can participate in scholarship with minimal bias or barriers,” says Director of Libraries Chris Bourg. “It’s our mission to remove the ‘spikes’ in the systems through which we create, use, and share knowledge.”

      Calling out the harms encoded into our digital world is critical, argues Benjamin, but we must also create alternatives. This is where the collective power of individuals can be transformative. Benjamin shared examples of those who are “re-imagining the default settings of technology and society,” citing initiatives like Data for Black Lives movement and the Detroit Community Technology Project. “I’m interested in the way that everyday people are changing the digital ecosystem and demanding different kinds of rights and responsibilities and protections,” she said.

      In 2020, Benjamin founded the Ida B. Wells Just Data Lab with a goal of bringing together students, educators, activists, and artists to develop a critical and creative approach to data conception, production, and circulation. Its projects have examined different aspects of data and racial inequality: assessing the impact of Covid-19 on student learning; providing resources that confront the experience of Black mourning, grief, and mental health; or developing a playbook for Black maternal mental health. Through the lab’s student-led projects Benjamin sees the next generation re-imagining technology in ways that respond to the needs of marginalized people.

      “If inequity is woven into the very fabric of our society — we see it from policing to education to health care to work — then each twist, coil, and code is a chance for us to weave new patterns, practices, and politics,” she said. “The vastness of the problems that we’re up against will be their undoing.” More

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      in Data Management & Statistics

      Why big changes early in life can help later on

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      Imagine moving from state to state while growing up in the U.S., transferring between high schools, and eventually attending college out of state. The first two events might seem disruptive, and the third involves departing a local community. And yet, these things may be exactly what helps some people thrive later in life.

      That’s one implication of a newly published study about social networks co-authored by an MIT professor, which finds that so-called long ties — connections between people who otherwise lack any mutual contacts — are highly associated with greater economic success in life. Those long ties are fostered partly by turning points such as moving between states, and switching schools.

      The study, based on a large quantity of Facebook data, both illuminates how productive social networks are structured and identifies specific life events that significantly shape people’s networks.

      “People who have more long ties [on Facebook], and who have stronger long ties, have better economic indicators,” says Dean Eckles, an MIT professor and co-author of a new paper detailing the study’s findings.

      “Our hope is that the study provides better evidence of this really strong relationship, at the scale of the entire U.S,” Eckles says. “There hasn’t really been this sort of investigation into those types of disruptive life events.”

      The paper, “Long ties, disruptive life events, and economic prosperity,” appears in open-access form in Proceedings of the National Academy of Sciences. The authors are Eaman Jahani PhD ’21, a postdoc and lecturer at the University of California at Berkeley, who received his doctorate from MIT’s Institute for Data, Systems, and Society, and the Statistics and Data Science Center; Samuel P. Fraiberger, a data scientist at the World Bank; Michael Bailey, an economist and research scientist manager at Meta Platforms (which operates Facebook); and Eckles, an associate professor of marketing at MIT Sloan School of Management. Jahani, who worked at Meta when the study was conducted, performed the initial research, and the aggregate data analysis protected the privacy of individuals in compliance with regulations.

      On the move

      In recent decades, scholars have often analyzed social networks while building on a 1973 study by Stanford University’s Mark Granovetter, “The Strength of Weak Ties,” one of the 10 most-cited social science papers of all time. In it, Granovetter postulated that a network’s “weak ties”— the people you know less well — are vital. Your best friends may have networks quite similar to your own, but your “weak ties” provide additional connections useful for employment, and more. Granovetter also edited this current paper for PNAS.

      To conduct the study, the scholars mapped all reciprocal interactions among U.S.-based Facebook accounts from December 2020 to June 2021, to build a data-rich picture of social networks in action. The researchers maintain a distinction between “long” and “short” ties; in this definition, long ties have no other mutual connections at all, while short ties have some.

      Ultimately the scholars found that, when assessing everyone who has lived in the same state since 2012, those who had previously moved among U.S. states had 13 percent more long ties on Facebook than those who had not. Similarly, people who had switched high schools had 10 percent more long ties than people who had not.

      Facebook does not have income data for its users, so the scholars used a series of proxy measures to evaluate financial success. People with more long ties tend to live in higher-income areas, have more internet-connected devices, use more expensive mobile phones, and make more donations to charitable causes, compared to those who do not.

      Additionally, the research evaluates whether or not moving among states, or switching schools, is itself what causes people to have more long ties. After all, it could be the case that families who move more often have qualities that lead family members to be more proactive about forging ties with people.

      To examine this, the research team analyzed a subgroup of Facebook users who had switched high schools only when their first high school closed — meaning it was not their choice to change. Those people had 6 percent more long ties than those who had attended the same high schools but not been forced to switch; given this common pool of school attendees forced into divergent circumstances, the evidence suggests that making the school change itself “shapes the proclivity to connect with different communities,” as the scholars write in the paper. 

      “It’s a plausibly random nudge,” Eckles says, “and we find the people who were exposed to these high school closures end up with more long ties. I think that is one of the compelling elements pointing toward a causal story here.”

      Three types of events, same trend

      As the scholars acknowledge in the paper, there are some limitations to the study. Because it focuses on Facebook interactions, the research does not account for offline activities that may sustain social networks. It is also likely that economic success itself shapes people’s social networks, and not just that networks help shape success. Some people may have opportunities to maintain long ties, through professional work or travel, that others do not.

      On the other hand, the study does uncover long-term social network ties that had not been evaluated before, and, as the authors write,”having three different types of events — involving different processes by which people are selected into the disruption — pointing to the same conclusions makes for a more robust and notable pattern.”

      Other scholars in the field believe the study is a notable piece of research. In a commentary on the paper also published in PNAS, Michael Macy, a sociology professor at Cornell University, writes that “the authors demonstrate the importance of contributing to cumulative knowledge by confirming hypotheses derived from foundational theory while at the same time elaborating on what was previously known by digging deeper into the underlying causal mechanisms. In short, the paper is must reading not only for area specialists but for social scientists across the disciplines.”

      For his part, Eckles emphasizes that the researchers are releasing anonymized data from the study, so that other scholars can build on it, and develop additional insights about social network structure, while complying with all privacy regulations.

      “We’ve released [that] data and made it public, and we’re really happy to be doing that,” Eckles says. “We want to make as much of this as possible open to others. That’s one of the things that I’m hoping is part of the broader impact of the paper.”

      Jahani worked as a contractor at Meta Platforms, which operates Facebook, while conducting the research. Eckles has received past funding from Meta, as well as conference sponsorship, and previously worked there, before joining MIT.   More

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      Day of AI curriculum meets the moment

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      MIT Responsible AI for Social Empowerment and Education (RAISE) recently celebrated the second annual Day of AI with two flagship local events. The Edward M. Kennedy Institute for the U.S. Senate in Boston hosted a human rights and data policy-focused event that was streamed worldwide. Dearborn STEM Academy in Roxbury, Massachusetts, hosted a student workshop in collaboration with Amazon Future Engineer. With over 8,000 registrations across all 50 U.S. states and 108 countries in 2023, participation in Day of AI has more than doubled since its inaugural year.

      Day of AI is a free curriculum of lessons and hands-on activities designed to teach kids of all ages and backgrounds the basics and responsible use of artificial intelligence, designed by researchers at MIT RAISE. This year, resources were available for educators to run at any time and in any increments they chose. The curriculum included five new modules to address timely topics like ChatGPT in School, Teachable Machines, AI and Social Media, Data Science and Me, and more. A collaboration with the International Society for Technology in Education also introduced modules for early elementary students. Educators across the world shared photos, videos, and stories of their students’ engagement, expressing excitement and even relief over the accessible lessons.

      Professor Cynthia Breazeal, director of RAISE, dean for digital learning at MIT, and head of the MIT Media Lab’s Personal Robots research group, said, “It’s been a year of extraordinary advancements in AI, and with that comes necessary conversations and concerns about who and what this technology is for. With our Day of AI events, we want to celebrate the teachers and students who are putting in the work to make sure that AI is for everyone.”

      Reflecting community values and protecting digital citizens

      Play video

      On May 18, 2023, MIT RAISE hosted a global Day of AI celebration featuring a flagship local event focused on human rights and data policy at the Edward M. Kennedy Institute for the U.S. Senate. Students from the Warren Prescott Middle School and New Mission High School heard from speakers the City of Boston, Liberty Mutual, and MIT to discuss the many benefits and challenges of artificial intelligence education. Video: MIT Open Learning

      MIT President Sally Kornbluth welcomed students from Warren Prescott Middle School and New Mission High School to the Day of AI program at the Edward M. Kennedy Institute. Kornbluth reflected on the exciting potential of AI, along with the ethical considerations society needs to be responsible for.

      “AI has the potential to do all kinds of fantastic things, including driving a car, helping us with the climate crisis, improving health care, and designing apps that we can’t even imagine yet. But what we have to make sure it doesn’t do is cause harm to individuals, to communities, to us — society as a whole,” she said.

      This theme resonated with each of the event speakers, whose jobs spanned the sectors of education, government, and business. Yo Deshpande, technologist for the public realm, and Michael Lawrence Evans, program director of new urban mechanics from the Boston Mayor’s Office, shared how Boston thinks about using AI to improve city life in ways that are “equitable, accessible, and delightful.” Deshpande said, “We have the opportunity to explore not only how AI works, but how using AI can line up with our values, the way we want to be in the world, and the way we want to be in our community.”

      Adam L’Italien, chief innovation officer at Liberty Mutual Insurance (one of Day of AI’s founding sponsors), compared our present moment with AI technologies to the early days of personal computers and internet connection. “Exposure to emerging technologies can accelerate progress in the world and in your own lives,” L’Italien said, while recognizing that the AI development process needs to be inclusive and mitigate biases.

      Human policies for artificial intelligence

      So how does society address these human rights concerns about AI? Marc Aidinoff ’21, former White House Office of Science and Technology Policy chief of staff, led a discussion on how government policy can influence the parameters of how technology is developed and used, like the Blueprint for an AI Bill of Rights. Aidinoff said, “The work of building the world you want to see is far harder than building the technical AI system … How do you work with other people and create a collective vision for what we want to do?” Warren Prescott Middle School students described how AI could be used to solve problems that humans couldn’t. But they also shared their concerns that AI could affect data privacy, learning deficits, social media addiction, job displacement, and propaganda.

      In a mock U.S. Senate trial activity designed by Daniella DiPaola, PhD student at the MIT Media Lab, the middle schoolers investigated what rights might be undermined by AI in schools, hospitals, law enforcement, and corporations. Meanwhile, New Mission High School students workshopped the ideas behind bill S.2314, the Social Media Addiction Reduction Technology (SMART) Act, in an activity designed by Raechel Walker, graduate research assistant in the Personal Robots Group, and Matt Taylor, research assistant at the Media Lab. They discussed what level of control could or should be introduced at the parental, educational, and governmental levels to reduce the risks of internet addiction.

      “Alexa, how do I program AI?”

      Play video

      The 2023 Day of AI celebration featured a flagship local event at the Dearborn STEM Academy in Roxbury in collaboration with Amazon Future Engineer. Students participated in a hands-on activity using MIT App Inventor as part of Day of AI’s Alexa lesson. Video: MIT Open Learning

      At Dearborn STEM Academy, Amazon Future Engineer helped students work through the Intro to Voice AI curriculum module in real-time. Students used MIT App Inventor to code basic commands for Alexa. In an interview with WCVB, Principal Darlene Marcano said, “It’s important that we expose our students to as many different experiences as possible. The students that are participating are on track to be future computer scientists and engineers.”

      Breazeal told Dearborn students, “We want you to have an informed voice about how you want AI to be used in society. We want you to feel empowered that you can shape the world. You can make things with AI to help make a better world and a better community.”

      Rohit Prasad ’08, senior vice president and head scientist for Alexa at Amazon, and Victor Reinoso ’97, global director of philanthropic education initiatives at Amazon, also joined the event. “Amazon and MIT share a commitment to helping students discover a world of possibilities through STEM and AI education,” said Reinoso. “There’s a lot of current excitement around the technological revolution with generative AI and large language models, so we’re excited to help students explore careers of the future and navigate the pathways available to them.” To highlight their continued investment in the local community and the school program, Amazon donated a $25,000 Innovation and Early College Pathways Program Grant to the Boston Public School system.

      Day of AI down under

      Not only was the Day of AI program widely adopted across the globe, Australian educators were inspired to adapt their own regionally specific curriculum. An estimated 161,000 AI professionals will be needed in Australia by 2030, according to the National Artificial Intelligence Center in the Commonwealth Scientific and Industrial Research Organization (CSIRO), an Australian government agency and Day of AI Australia project partner. CSIRO worked with the University of New South Wales to develop supplementary educational resources on AI ethics and machine learning. Day of AI Australia reached 85,000 students at 400-plus secondary schools this year, sparking curiosity in the next generation of AI experts.

      The interest in AI is accelerating as fast as the technology is being developed. Day of AI offers a unique opportunity for K-12 students to shape our world’s digital future and their own.

      “I hope that some of you will decide to be part of this bigger effort to help us figure out the best possible answers to questions that are raised by AI,” Kornbluth told students at the Edward M. Kennedy Institute. “We’re counting on you, the next generation, to learn how AI works and help make sure it’s for everyone.” More