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      Research, education, and connection in the face of war

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      When Russian forces invaded Ukraine in February 2022, Tetiana Herasymova had several decisions to make: What should she do, where should she live, and should she take her MITx MicroMasters capstone exams? She had registered for the Statistics and Data Science Program’s final exams just days prior to moving out of her apartment and into a bomb shelter. Although it was difficult to focus on studying and preparations with air horns sounding overhead and uncertainty lingering around her, she was determined to try. “I wouldn’t let the aggressor in the war squash my dreams,” she says.

      A love of research and the desire to improve teaching 

      An early love of solving puzzles and problems for fun piqued Herasymova’s initial interest in mathematics. When she later pursued her PhD in mathematics at Kiev National Taras Shevchenko University, Herasymova’s love of math evolved into a love of research. Throughout Herasymova’s career, she’s worked to close the gap between scientific researchers and educators. Starting as a math tutor at MBA Strategy, a company that prepares Ukrainian leaders for qualifying standardized tests for MBA programs, she was later promoted as the head of their test preparation department. Afterward, she moved on to an equivalent position at ZNOUA, a new project that prepared high school students for Ukraine’s standardized test, and she eventually became ZNOUA’s CEO.

      In 2018, she founded Prosteer, a “self-learning community” of educators who share research, pedagogy, and experience to learn from one another. “It’s really interesting to have a community of teachers from different domains,” she says, speaking of educators and researchers whose specialties range across language, mathematics, physics, music, and more.

      Implementing new pedagogical research in the classroom is often up to educators who seek out studies on an individual basis, Herasymova has found. “Lots of scientists are not practitioners,” she says, and the reverse is also true. She only became more determined to build these connections once she was promoted to head of test preparation at MBA Strategy because she wanted to share more effective pedagogy with the tutors she was mentoring.

      First, Herasymova knew she needed a way to measure the teachers’ effectiveness. She was able to determine whether students who received the company’s tutoring services improved their scores. Moreover, Ukraine keeps an open-access database of national standardized test scores, so anyone could analyze the data in hopes of improving the level of education in the country. She says, “I could do some analytics because I am a mathematician, but I knew I could do much more with this data if I knew data science and machine learning knowledge.”

      That’s why Herasymova sought out the MITx MicroMasters Program in Statistics and Data Science offered by the MIT Institute for Data, Systems, and Society (IDSS). “I wanted to learn the fundamentals so I could join the Learning Analytics domain,” she says. She was looking for a comprehensive program that covered the foundations without being overly basic. “I had some knowledge from the ground, so I could see the deepness of that course,” she says. Because of her background as an instructional designer, she thought the MicroMasters curriculum was well-constructed, calling the variety of videos, practice problems, and homework assignments that encouraged learners to approach the course material in different ways, “a perfect experience.”

      Another benefit of the MicroMasters program was its online format. “I had my usual work, so it was impossible to study in a stationary way,” she says. She found the structure to be more flexible than other programs. “It’s really great that you can construct your course schedule your own way, especially with your own adult life,” she says.

      Determination and support in the midst of war

      When the war first forced Herasymova to flee her apartment, she had already registered to take the exams for her four courses. “It was quite hard to prepare for exams when you could hear explosions outside of the bomb shelter,” she says. She and other Ukranians were invited to postpone their exams until the following session, but the next available testing period wouldn’t be held until October. “It was a hard decision, but I had to allow myself to try,” she says. “For all people in Ukraine, when you don’t know if you’re going to live or die, you try to live in the now. You have to appreciate every moment and what life brings to you. You don’t say, ‘Someday’ — you do it today or tomorrow.”

      In addition to emotional support from her boyfriend, Herasymova had a group of friends who had also enrolled in the program, and they supported each other through study sessions and an ongoing chat. Herasymova’s personal support network helped her accomplish what she set out to do with her MicroMasters program, and in turn, she was able to support her professional network. While Prosteer halted its regular work during the early stages of the war, Herasymova was determined to support the community of educators and scientists that she had built. They continued meeting weekly to exchange ideas as usual. “It’s intrinsic motivation,” she says. They managed to restore all of their activities by October.

      Despite the factors stacked against her, Herasymova’s determination paid off — she passed all of her exams in May, the final step to earning her MicroMasters certificate in statistics and data science. “I just couldn’t believe it,” she says. “It was definitely a bifurcation point. The moment when you realize that you have something to rely on, and that life is just beginning to show all its diversity despite the fact that you live in war.” With her newly minted certificate in hand, Herasymova has continued her research on the effectiveness of educational models — analyzing the data herself — with a summer research program at New York University. 

      The student becomes the master

      After moving seven times between February and October, heading west from Kyiv until most recently settling near the border of Poland, Herasymova hopes she’s moved for the last time. Ukrainian Catholic University offered her a position teaching both mathematics and programming. Before enrolling in the MicroMasters Program in Statistics and Data Science, she had some prior knowledge of programming languages and mathematical algorithms, but she didn’t know Python. She took MITx’s Introduction to Computer Science and Programming Using Python to prepare. “It gave me a huge step forward,” she says. “I learned a lot. Now, not only can I work with Python machine learning models in programming language R, I also have knowledge of the big picture of the purpose and the point to do so.”

      In addition to the skills the MicroMasters Program trained her in, she gained firsthand experience in learning new subjects and exploring topics more deeply. She will be sharing that practice with the community of students and teachers she’s built, plus, she plans on guiding them through this course during the next year. As a continuation of her own educational growth, says she’s looking forward to her next MITx course this year, Data Analysis.

      Herasymova advises that the best way to keep progressing is investing a lot of time. “Adults don’t want to hear this, but you need one or two years,” she says. “Allow yourself to be stupid. If you’re an expert in one domain and want to switch to another, or if you want to understand something new, a lot of people don’t ask questions or don’t ask for help. But from this point, if I don’t know something, I know I should ask for help because that’s the start of learning. With a fixed mindset, you won’t grow.”

      July 2022 MicroMasters Program Joint Completion Celebration. Ukrainian student Tetiana Herasymova, who completed her program amid war in her home country, speaks at 43:55. More

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      Unpacking the “black box” to build better AI models

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      When deep learning models are deployed in the real world, perhaps to detect financial fraud from credit card activity or identify cancer in medical images, they are often able to outperform humans.

      But what exactly are these deep learning models learning? Does a model trained to spot skin cancer in clinical images, for example, actually learn the colors and textures of cancerous tissue, or is it flagging some other features or patterns?

      These powerful machine-learning models are typically based on artificial neural networks that can have millions of nodes that process data to make predictions. Due to their complexity, researchers often call these models “black boxes” because even the scientists who build them don’t understand everything that is going on under the hood.

      Stefanie Jegelka isn’t satisfied with that “black box” explanation. A newly tenured associate professor in the MIT Department of Electrical Engineering and Computer Science, Jegelka is digging deep into deep learning to understand what these models can learn and how they behave, and how to build certain prior information into these models.

      “At the end of the day, what a deep-learning model will learn depends on so many factors. But building an understanding that is relevant in practice will help us design better models, and also help us understand what is going on inside them so we know when we can deploy a model and when we can’t. That is critically important,” says Jegelka, who is also a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Institute for Data, Systems, and Society (IDSS).

      Jegelka is particularly interested in optimizing machine-learning models when input data are in the form of graphs. Graph data pose specific challenges: For instance, information in the data consists of both information about individual nodes and edges, as well as the structure — what is connected to what. In addition, graphs have mathematical symmetries that need to be respected by the machine-learning model so that, for instance, the same graph always leads to the same prediction. Building such symmetries into a machine-learning model is usually not easy.

      Take molecules, for instance. Molecules can be represented as graphs, with vertices that correspond to atoms and edges that correspond to chemical bonds between them. Drug companies may want to use deep learning to rapidly predict the properties of many molecules, narrowing down the number they must physically test in the lab.

      Jegelka studies methods to build mathematical machine-learning models that can effectively take graph data as an input and output something else, in this case a prediction of a molecule’s chemical properties. This is particularly challenging since a molecule’s properties are determined not only by the atoms within it, but also by the connections between them.  

      Other examples of machine learning on graphs include traffic routing, chip design, and recommender systems.

      Designing these models is made even more difficult by the fact that data used to train them are often different from data the models see in practice. Perhaps the model was trained using small molecular graphs or traffic networks, but the graphs it sees once deployed are larger or more complex.

      In this case, what can researchers expect this model to learn, and will it still work in practice if the real-world data are different?

      “Your model is not going to be able to learn everything because of some hardness problems in computer science, but what you can learn and what you can’t learn depends on how you set the model up,” Jegelka says.

      She approaches this question by combining her passion for algorithms and discrete mathematics with her excitement for machine learning.

      From butterflies to bioinformatics

      Jegelka grew up in a small town in Germany and became interested in science when she was a high school student; a supportive teacher encouraged her to participate in an international science competition. She and her teammates from the U.S. and Singapore won an award for a website they created about butterflies, in three languages.

      “For our project, we took images of wings with a scanning electron microscope at a local university of applied sciences. I also got the opportunity to use a high-speed camera at Mercedes Benz — this camera usually filmed combustion engines — which I used to capture a slow-motion video of the movement of a butterfly’s wings. That was the first time I really got in touch with science and exploration,” she recalls.

      Intrigued by both biology and mathematics, Jegelka decided to study bioinformatics at the University of Tübingen and the University of Texas at Austin. She had a few opportunities to conduct research as an undergraduate, including an internship in computational neuroscience at Georgetown University, but wasn’t sure what career to follow.

      When she returned for her final year of college, Jegelka moved in with two roommates who were working as research assistants at the Max Planck Institute in Tübingen.

      “They were working on machine learning, and that sounded really cool to me. I had to write my bachelor’s thesis, so I asked at the institute if they had a project for me. I started working on machine learning at the Max Planck Institute and I loved it. I learned so much there, and it was a great place for research,” she says.

      She stayed on at the Max Planck Institute to complete a master’s thesis, and then embarked on a PhD in machine learning at the Max Planck Institute and the Swiss Federal Institute of Technology.

      During her PhD, she explored how concepts from discrete mathematics can help improve machine-learning techniques.

      Teaching models to learn

      The more Jegelka learned about machine learning, the more intrigued she became by the challenges of understanding how models behave, and how to steer this behavior.

      “You can do so much with machine learning, but only if you have the right model and data. It is not just a black-box thing where you throw it at the data and it works. You actually have to think about it, its properties, and what you want the model to learn and do,” she says.

      After completing a postdoc at the University of California at Berkeley, Jegelka was hooked on research and decided to pursue a career in academia. She joined the faculty at MIT in 2015 as an assistant professor.

      “What I really loved about MIT, from the very beginning, was that the people really care deeply about research and creativity. That is what I appreciate the most about MIT. The people here really value originality and depth in research,” she says.

      That focus on creativity has enabled Jegelka to explore a broad range of topics.

      In collaboration with other faculty at MIT, she studies machine-learning applications in biology, imaging, computer vision, and materials science.

      But what really drives Jegelka is probing the fundamentals of machine learning, and most recently, the issue of robustness. Often, a model performs well on training data, but its performance deteriorates when it is deployed on slightly different data. Building prior knowledge into a model can make it more reliable, but understanding what information the model needs to be successful and how to build it in is not so simple, she says.

      She is also exploring methods to improve the performance of machine-learning models for image classification.

      Image classification models are everywhere, from the facial recognition systems on mobile phones to tools that identify fake accounts on social media. These models need massive amounts of data for training, but since it is expensive for humans to hand-label millions of images, researchers often use unlabeled datasets to pretrain models instead.

      These models then reuse the representations they have learned when they are fine-tuned later for a specific task.

      Ideally, researchers want the model to learn as much as it can during pretraining, so it can apply that knowledge to its downstream task. But in practice, these models often learn only a few simple correlations — like that one image has sunshine and one has shade — and use these “shortcuts” to classify images.

      “We showed that this is a problem in ‘contrastive learning,’ which is a standard technique for pre-training, both theoretically and empirically. But we also show that you can influence the kinds of information the model will learn to represent by modifying the types of data you show the model. This is one step toward understanding what models are actually going to do in practice,” she says.

      Researchers still don’t understand everything that goes on inside a deep-learning model, or details about how they can influence what a model learns and how it behaves, but Jegelka looks forward to continue exploring these topics.

      “Often in machine learning, we see something happen in practice and we try to understand it theoretically. This is a huge challenge. You want to build an understanding that matches what you see in practice, so that you can do better. We are still just at the beginning of understanding this,” she says.

      Outside the lab, Jegelka is a fan of music, art, traveling, and cycling. But these days, she enjoys spending most of her free time with her preschool-aged daughter. More

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      in Data Management & Statistics

      Simulating discrimination in virtual reality

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      Have you ever been advised to “walk a mile in someone else’s shoes?” Considering another person’s perspective can be a challenging endeavor — but recognizing our errors and biases is key to building understanding across communities. By challenging our preconceptions, we confront prejudice, such as racism and xenophobia, and potentially develop a more inclusive perspective about others.

      To assist with perspective-taking, MIT researchers have developed “On the Plane,” a virtual reality role-playing game (VR RPG) that simulates discrimination. In this case, the game portrays xenophobia directed against a Malaysian America woman, but the approach can be generalized. Situated on an airplane, players can take on the role of characters from different backgrounds, engaging in dialogue with others while making in-game choices to a series of prompts. In turn, players’ decisions control the outcome of a tense conversation between the characters about cultural differences.

      As a VR RPG, “On the Plane” encourages players to take on new roles that may be outside of their personal experiences in the first person, allowing them to confront in-group/out-group bias by incorporating new perspectives into their understanding of different cultures. Players engage with three characters: Sarah, a first-generation Muslim American of Malaysian ancestry who wears a hijab; Marianne, a white woman from the Midwest with little exposure to other cultures and customs; or a flight attendant. Sarah represents the out group, Marianne is a member of the in group, and the flight staffer is a bystander witnessing an exchange between the two passengers.“This project is part of our efforts to harness the power of virtual reality and artificial intelligence to address social ills, such as discrimination and xenophobia,” says Caglar Yildirim, an MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) research scientist who is a co-author and co-game designer on the project. “Through the exchange between the two passengers, players experience how one passenger’s xenophobia manifests itself and how it affects the other passenger. The simulation engages players in critical reflection and seeks to foster empathy for the passenger who was ‘othered’ due to her outfit being not so ‘prototypical’ of what an American should look like.”

      Yildirim worked alongside the project’s principal investigator, D. Fox Harrell, MIT professor of digital media and AI at CSAIL, the Program in Comparative Media Studies/Writing (CMS), and the Institute for Data, Systems, and Society (IDSS) and founding director of the MIT Center for Advanced Virtuality. “It is not possible for a simulation to give someone the life experiences of another person, but while you cannot ‘walk in someone else’s shoes’ in that sense, a system like this can help people recognize and understand the social patterns at work when it comes to issue like bias,” says Harrell, who is also co-author and designer on this project. “An engaging, immersive, interactive narrative can also impact people emotionally, opening the door for users’ perspectives to be transformed and broadened.” This simulation also utilizes an interactive narrative engine that creates several options for responses to in-game interactions based on a model of how people are categorized socially. The tool grants players a chance to alter their standing in the simulation through their reply choices to each prompt, affecting their affinity toward the other two characters. For example, if you play as the flight attendant, you can react to Marianne’s xenophobic expressions and attitudes toward Sarah, changing your affinities. The engine will then provide you with a different set of narrative events based on your changes in standing with others.

      To animate each avatar, “On the Plane” incorporates artificial intelligence knowledge representation techniques controlled by probabilistic finite state machines, a tool commonly used in machine learning systems for pattern recognition. With the help of these machines, characters’ body language and gestures are customizable: if you play as Marianne, the game will customize her mannerisms toward Sarah based on user inputs, impacting how comfortable she appears in front of a member of a perceived out group. Similarly, players can do the same from Sarah or the flight attendant’s point of view.In a 2018 paper based on work done in a collaboration between MIT CSAIL and the Qatar Computing Research Institute, Harrell and co-author Sercan Şengün advocated for virtual system designers to be more inclusive of Middle Eastern identities and customs. They claimed that if designers allowed users to customize virtual avatars more representative of their background, it might empower players to engage in a more supportive experience. Four years later, “On the Plane” accomplishes a similar goal, incorporating a Muslim’s perspective into an immersive environment.

      “Many virtual identity systems, such as avatars, accounts, profiles, and player characters, are not designed to serve the needs of people across diverse cultures. We have used statistical and AI methods in conjunction with qualitative approaches to learn where the gaps are,” they note. “Our project helps engender perspective transformation so that people will treat each other with respect and enhanced understanding across diverse cultural avatar representations.”

      Harrell and Yildirim’s work is part of the MIT IDSS’s Initiative on Combatting Systemic Racism (ICSR). Harrell is on the initiative’s steering committee and is the leader of the newly forming Antiracism, Games, and Immersive Media vertical, who study behavior, cognition, social phenomena, and computational systems related to race and racism in video games and immersive experiences.

      The researchers’ latest project is part of the ICSR’s broader goal to launch and coordinate cross-disciplinary research that addresses racially discriminatory processes across American institutions. Using big data, members of the research initiative develop and employ computing tools that drive racial equity. Yildirim and Harrell accomplish this goal by depicting a frequent, problematic scenario that illustrates how bias creeps into our everyday lives.“In a post-9/11 world, Muslims often experience ethnic profiling in American airports. ‘On the Plane’ builds off of that type of in-group favoritism, a well-established finding in psychology,” says MIT Professor Fotini Christia, director of the Sociotechnical Systems Research Center (SSRC) and associate director or IDSS. “This game also takes a novel approach to analyzing hardwired bias by utilizing VR instead of field experiments to simulate prejudice. Excitingly, this research demonstrates that VR can be used as a tool to help us better measure bias, combating systemic racism and other forms of discrimination.”“On the Plane” was developed on the Unity game engine using the XR Interaction Toolkit and Harrell’s Chimeria platform for authoring interactive narratives that involve social categorization. The game will be deployed for research studies later this year on both desktop computers and the standalone, wireless Meta Quest headsets. A paper on the work was presented in December at the 2022 IEEE International Conference on Artificial Intelligence and Virtual Reality. More

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      in Data Management & Statistics

      Subtle biases in AI can influence emergency decisions

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      It’s no secret that people harbor biases — some unconscious, perhaps, and others painfully overt. The average person might suppose that computers — machines typically made of plastic, steel, glass, silicon, and various metals — are free of prejudice. While that assumption may hold for computer hardware, the same is not always true for computer software, which is programmed by fallible humans and can be fed data that is, itself, compromised in certain respects.

      Artificial intelligence (AI) systems — those based on machine learning, in particular — are seeing increased use in medicine for diagnosing specific diseases, for example, or evaluating X-rays. These systems are also being relied on to support decision-making in other areas of health care. Recent research has shown, however, that machine learning models can encode biases against minority subgroups, and the recommendations they make may consequently reflect those same biases.

      A new study by researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and the MIT Jameel Clinic, which was published last month in Communications Medicine, assesses the impact that discriminatory AI models can have, especially for systems that are intended to provide advice in urgent situations. “We found that the manner in which the advice is framed can have significant repercussions,” explains the paper’s lead author, Hammaad Adam, a PhD student at MIT’s Institute for Data Systems and Society. “Fortunately, the harm caused by biased models can be limited (though not necessarily eliminated) when the advice is presented in a different way.” The other co-authors of the paper are Aparna Balagopalan and Emily Alsentzer, both PhD students, and the professors Fotini Christia and Marzyeh Ghassemi.

      AI models used in medicine can suffer from inaccuracies and inconsistencies, in part because the data used to train the models are often not representative of real-world settings. Different kinds of X-ray machines, for instance, can record things differently and hence yield different results. Models trained predominately on white people, moreover, may not be as accurate when applied to other groups. The Communications Medicine paper is not focused on issues of that sort but instead addresses problems that stem from biases and on ways to mitigate the adverse consequences.

      A group of 954 people (438 clinicians and 516 nonexperts) took part in an experiment to see how AI biases can affect decision-making. The participants were presented with call summaries from a fictitious crisis hotline, each involving a male individual undergoing a mental health emergency. The summaries contained information as to whether the individual was Caucasian or African American and would also mention his religion if he happened to be Muslim. A typical call summary might describe a circumstance in which an African American man was found at home in a delirious state, indicating that “he has not consumed any drugs or alcohol, as he is a practicing Muslim.” Study participants were instructed to call the police if they thought the patient was likely to turn violent; otherwise, they were encouraged to seek medical help.

      The participants were randomly divided into a control or “baseline” group plus four other groups designed to test responses under slightly different conditions. “We want to understand how biased models can influence decisions, but we first need to understand how human biases can affect the decision-making process,” Adam notes. What they found in their analysis of the baseline group was rather surprising: “In the setting we considered, human participants did not exhibit any biases. That doesn’t mean that humans are not biased, but the way we conveyed information about a person’s race and religion, evidently, was not strong enough to elicit their biases.”

      The other four groups in the experiment were given advice that either came from a biased or unbiased model, and that advice was presented in either a “prescriptive” or a “descriptive” form. A biased model would be more likely to recommend police help in a situation involving an African American or Muslim person than would an unbiased model. Participants in the study, however, did not know which kind of model their advice came from, or even that models delivering the advice could be biased at all. Prescriptive advice spells out what a participant should do in unambiguous terms, telling them they should call the police in one instance or seek medical help in another. Descriptive advice is less direct: A flag is displayed to show that the AI system perceives a risk of violence associated with a particular call; no flag is shown if the threat of violence is deemed small.  

      A key takeaway of the experiment is that participants “were highly influenced by prescriptive recommendations from a biased AI system,” the authors wrote. But they also found that “using descriptive rather than prescriptive recommendations allowed participants to retain their original, unbiased decision-making.” In other words, the bias incorporated within an AI model can be diminished by appropriately framing the advice that’s rendered. Why the different outcomes, depending on how advice is posed? When someone is told to do something, like call the police, that leaves little room for doubt, Adam explains. However, when the situation is merely described — classified with or without the presence of a flag — “that leaves room for a participant’s own interpretation; it allows them to be more flexible and consider the situation for themselves.”

      Second, the researchers found that the language models that are typically used to offer advice are easy to bias. Language models represent a class of machine learning systems that are trained on text, such as the entire contents of Wikipedia and other web material. When these models are “fine-tuned” by relying on a much smaller subset of data for training purposes — just 2,000 sentences, as opposed to 8 million web pages — the resultant models can be readily biased.  

      Third, the MIT team discovered that decision-makers who are themselves unbiased can still be misled by the recommendations provided by biased models. Medical training (or the lack thereof) did not change responses in a discernible way. “Clinicians were influenced by biased models as much as non-experts were,” the authors stated.

      “These findings could be applicable to other settings,” Adam says, and are not necessarily restricted to health care situations. When it comes to deciding which people should receive a job interview, a biased model could be more likely to turn down Black applicants. The results could be different, however, if instead of explicitly (and prescriptively) telling an employer to “reject this applicant,” a descriptive flag is attached to the file to indicate the applicant’s “possible lack of experience.”

      The implications of this work are broader than just figuring out how to deal with individuals in the midst of mental health crises, Adam maintains.  “Our ultimate goal is to make sure that machine learning models are used in a fair, safe, and robust way.” More

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      in Data Management & Statistics

      A healthy wind

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      Nearly 10 percent of today’s electricity in the United States comes from wind power. The renewable energy source benefits climate, air quality, and public health by displacing emissions of greenhouse gases and air pollutants that would otherwise be produced by fossil-fuel-based power plants.

      A new MIT study finds that the health benefits associated with wind power could more than quadruple if operators prioritized turning down output from the most polluting fossil-fuel-based power plants when energy from wind is available.

      In the study, published today in Science Advances, researchers analyzed the hourly activity of wind turbines, as well as the reported emissions from every fossil-fuel-based power plant in the country, between the years 2011 and 2017. They traced emissions across the country and mapped the pollutants to affected demographic populations. They then calculated the regional air quality and associated health costs to each community.

      The researchers found that in 2014, wind power that was associated with state-level policies improved air quality overall, resulting in $2 billion in health benefits across the country. However, only roughly 30 percent of these health benefits reached disadvantaged communities.

      The team further found that if the electricity industry were to reduce the output of the most polluting fossil-fuel-based power plants, rather than the most cost-saving plants, in times of wind-generated power, the overall health benefits could quadruple to $8.4 billion nationwide. However, the results would have a similar demographic breakdown.

      “We found that prioritizing health is a great way to maximize benefits in a widespread way across the U.S., which is a very positive thing. But it suggests it’s not going to address disparities,” says study co-author Noelle Selin, a professor in the Institute for Data, Systems, and Society and the Department of Earth, Atmospheric and Planetary Sciences at MIT. “In order to address air pollution disparities, you can’t just focus on the electricity sector or renewables and count on the overall air pollution benefits addressing these real and persistent racial and ethnic disparities. You’ll need to look at other air pollution sources, as well as the underlying systemic factors that determine where plants are sited and where people live.”

      Selin’s co-authors are lead author and former MIT graduate student Minghao Qiu PhD ’21, now at Stanford University, and Corwin Zigler at the University of Texas at Austin.

      Turn-down service

      In their new study, the team looked for patterns between periods of wind power generation and the activity of fossil-fuel-based power plants, to see how regional electricity markets adjusted the output of power plants in response to influxes of renewable energy.

      “One of the technical challenges, and the contribution of this work, is trying to identify which are the power plants that respond to this increasing wind power,” Qiu notes.

      To do so, the researchers compared two historical datasets from the period between 2011 and 2017: an hour-by-hour record of energy output of wind turbines across the country, and a detailed record of emissions measurements from every fossil-fuel-based power plant in the U.S. The datasets covered each of seven major regional electricity markets, each market providing energy to one or multiple states.

      “California and New York are each their own market, whereas the New England market covers around seven states, and the Midwest covers more,” Qiu explains. “We also cover about 95 percent of all the wind power in the U.S.”

      In general, they observed that, in times when wind power was available, markets adjusted by essentially scaling back the power output of natural gas and sub-bituminous coal-fired power plants. They noted that the plants that were turned down were likely chosen for cost-saving reasons, as certain plants were less costly to turn down than others.

      The team then used a sophisticated atmospheric chemistry model to simulate the wind patterns and chemical transport of emissions across the country, and determined where and at what concentrations the emissions generated fine particulates and ozone — two pollutants that are known to damage air quality and human health. Finally, the researchers mapped the general demographic populations across the country, based on U.S. census data, and applied a standard epidemiological approach to calculate a population’s health cost as a result of their pollution exposure.

      This analysis revealed that, in the year 2014, a general cost-saving approach to displacing fossil-fuel-based energy in times of wind energy resulted in $2 billion in health benefits, or savings, across the country. A smaller share of these benefits went to disadvantaged populations, such as communities of color and low-income communities, though this disparity varied by state.

      “It’s a more complex story than we initially thought,” Qiu says. “Certain population groups are exposed to a higher level of air pollution, and those would be low-income people and racial minority groups. What we see is, developing wind power could reduce this gap in certain states but further increase it in other states, depending on which fossil-fuel plants are displaced.”

      Tweaking power

      The researchers then examined how the pattern of emissions and the associated health benefits would change if they prioritized turning down different fossil-fuel-based plants in times of wind-generated power. They tweaked the emissions data to reflect several alternative scenarios: one in which the most health-damaging, polluting power plants are turned down first; and two other scenarios in which plants producing the most sulfur dioxide and carbon dioxide respectively, are first to reduce their output.

      They found that while each scenario increased health benefits overall, and the first scenario in particular could quadruple health benefits, the original disparity persisted: Communities of color and low-income communities still experienced smaller health benefits than more well-off communities.

      “We got to the end of the road and said, there’s no way we can address this disparity by being smarter in deciding which plants to displace,” Selin says.

      Nevertheless, the study can help identify ways to improve the health of the general population, says Julian Marshall, a professor of environmental engineering at the University of Washington.

      “The detailed information provided by the scenarios in this paper can offer a roadmap to electricity-grid operators and to state air-quality regulators regarding which power plants are highly damaging to human health and also are likely to noticeably reduce emissions if wind-generated electricity increases,” says Marshall, who was not involved in the study.

      “One of the things that makes me optimistic about this area is, there’s a lot more attention to environmental justice and equity issues,” Selin concludes. “Our role is to figure out the strategies that are most impactful in addressing those challenges.”

      This work was supported, in part, by the U.S. Environmental Protection Agency, and by the National Institutes of Health. More

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      in Data Management & Statistics

      A breakthrough on “loss and damage,” but also disappointment, at UN climate conference

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      As the 2022 United Nations climate change conference, known as COP27, stretched into its final hours on Saturday, Nov. 19, it was uncertain what kind of agreement might emerge from two weeks of intensive international negotiations.

      In the end, COP27 produced mixed results: on the one hand, a historic agreement for wealthy countries to compensate low-income countries for “loss and damage,” but on the other, limited progress on new plans for reducing the greenhouse gas emissions that are warming the planet.

      “We need to drastically reduce emissions now — and this is an issue this COP did not address,” said U.N. Secretary-General António Guterres in a statement at the conclusion of COP27. “A fund for loss and damage is essential — but it’s not an answer if the climate crisis washes a small island state off the map — or turns an entire African country to desert.”

      Throughout the two weeks of the conference, a delegation of MIT students, faculty, and staff was at the Sharm El-Sheikh International Convention Center to observe the negotiations, conduct and share research, participate in panel discussions, and forge new connections with researchers, policymakers, and advocates from around the world.

      Loss and damage

      A key issue coming in to COP27 (COP stands for “conference of the parties” to the U.N. Framework Convention on Climate Change, held for the 27th time) was loss and damage: a term used by the U.N. to refer to harms caused by climate change — either through acute catastrophes like extreme weather events or slower-moving impacts like sea level rise — to which communities and countries are unable to adapt. 

      Ultimately, a deal on loss and damage proved to be COP27’s most prominent accomplishment. Negotiators reached an eleventh-hour agreement to “establish new funding arrangements for assisting developing countries that are particularly vulnerable to the adverse effects of climate change.” 

      “Providing financial assistance to developing countries so they can better respond to climate-related loss and damage is not only a moral issue, but also a pragmatic one,” said Michael Mehling, deputy director of the MIT Center for Energy and Environmental Policy Research, who attended COP27 and participated in side events. “Future emissions growth will be squarely centered in the developing world, and offering support through different channels is key to building the trust needed for more robust global cooperation on mitigation.”

      Youssef Shaker, a graduate student in the MIT Technology and Policy Program and a research assistant with the MIT Energy Initiative, attended the second week of the conference, where he followed the negotiations over loss and damage closely. 

      “While the creation of a fund is certainly an achievement,” Shaker said, “significant questions remain to be answered, such as the size of the funding available as well as which countries receive access to it.” A loss-and-damage fund that is not adequately funded, Shaker noted, “would not be an impactful outcome.” 

      The agreement on loss and damage created a new committee, made up of 24 country representatives, to “operationalize” the new funding arrangements, including identifying funding sources. The committee is tasked with delivering a set of recommendations at COP28, which will take place next year in Dubai.

      Advising the U.N. on net zero

      Though the decisions reached at COP27 did not include major new commitments on reducing emissions from the combustion of fossil fuels, the transition to a clean global energy system was nevertheless a key topic of conversation throughout the conference.

      The Council of Engineers for the Energy Transition (CEET), an independent, international body of engineers and energy systems experts formed to provide advice to the U.N. on achieving net-zero emissions globally by 2050, convened for the first time at COP27. Jessika Trancik, a professor in the MIT Institute for Data, Systems, and Society and a member of CEET, spoke on a U.N.-sponsored panel on solutions for the transition to clean energy.

      Trancik noted that the energy transition will look different in different regions of the world. “As engineers, we need to understand those local contexts and design solutions around those local contexts — that’s absolutely essential to support a rapid and equitable energy transition.”

      At the same time, Trancik noted that there is now a set of “low-cost, ready-to-scale tools” available to every region — tools that resulted from a globally competitive process of innovation, stimulated by public policies in different countries, that dramatically drove down the costs of technologies like solar energy and lithium-ion batteries. The key, Trancik said, is for regional transition strategies to “tap into global processes of innovation.”

      Reinventing climate adaptation

      Elfatih Eltahir, the H. M. King Bhumibol Professor of Hydrology and Climate, traveled to COP27 to present plans for the Jameel Observatory Climate Resilience Early Warning System (CREWSnet), one of the five projects selected in April 2022 as a flagship in MIT’s Climate Grand Challenges initiative. CREWSnet focuses on climate adaptation, the term for adapting to climate impacts that are unavoidable.

      The aim of CREWSnet, Eltahir told the audience during a panel discussion, is “nothing short of reinventing the process of climate change adaptation,” so that it is proactive rather than reactive; community-led; data-driven and evidence-based; and so that it integrates different climate risks, from heat waves to sea level rise, rather than treating them individually.

      “However, it’s easy to talk about these changes,” said Eltahir. “The real challenge, which we are now just launching and engaging in, is to demonstrate that on the ground.” Eltahir said that early demonstrations will happen in a couple of key locations, including southwest Bangladesh, where multiple climate risks — rising sea levels, increasing soil salinity, and intensifying heat waves and cyclones — are combining to threaten the area’s agricultural production.

      Building on COP26

      Some members of MIT’s delegation attended COP27 to advance efforts that had been formally announced at last year’s U.N. climate conference, COP26, in Glasgow, Scotland.

      At an official U.N. side event co-organized by MIT on Nov. 11, Greg Sixt, the director of the Food and Climate Systems Transformation (FACT) Alliance led by the Abdul Latif Jameel Water and Food Systems Lab, provided an update on the alliance’s work since its launch at COP26.

      Food systems are a major source of greenhouse gas emissions — and are increasingly vulnerable to climate impacts. The FACT Alliance works to better connect researchers to farmers, food businesses, policymakers, and other food systems stakeholders to make food systems (which include food production, consumption, and waste) more sustainable and resilient. 

      Sixt told the audience that the FACT Alliance now counts over 20 research and stakeholder institutions around the world among its members, but also collaborates with other institutions in an “open network model” to advance work in key areas — such as a new research project exploring how climate scenarios could affect global food supply chains.

      Marcela Angel, research program director for the Environmental Solutions Initiative (ESI), helped convene a meeting at COP27 of the Afro-InterAmerican Forum on Climate Change, which also launched at COP26. The forum works with Afro-descendant leaders across the Americas to address significant environmental issues, including climate risks and biodiversity loss. 

      At the event — convened with the Colombian government and the nonprofit Conservation International — ESI brought together leaders from six countries in the Americas and presented recent work that estimates that there are over 178 million individuals who identify as Afro-descendant living in the Americas, in lands of global environmental importance. 

      “There is a significant overlap between biodiversity hot spots, protected areas, and areas of high Afro-descendant presence,” said Angel. “But the role and climate contributions of these communities is understudied, and often made invisible.”    

      Limiting methane emissions

      Methane is a short-lived but potent greenhouse gas: When released into the atmosphere, it immediately traps about 120 times more heat than carbon dioxide does. More than 150 countries have now signed the Global Methane Pledge, launched at COP26, which aims to reduce methane emissions by at least 30 percent by 2030 compared to 2020 levels.

      Sergey Paltsev, the deputy director of the Joint Program on the Science and Policy of Global Change and a senior research scientist at the MIT Energy Initiative, gave the keynote address at a Nov. 17 event on methane, where he noted the importance of methane reductions from the oil and gas sector to meeting the 2030 goal.

      “The oil and gas sector is where methane emissions reductions could be achieved the fastest,” said Paltsev. “We also need to employ an integrated approach to address methane emissions in all sectors and all regions of the world because methane emissions reductions provide a near-term pathway to avoiding dangerous tipping points in the global climate system.”

      “Keep fighting relentlessly”

      Arina Khotimsky, a senior majoring in materials science and engineering and a co-president of the MIT Energy and Climate Club, attended the first week of COP27. She reflected on the experience in a social media post after returning home. 

      “COP will always have its haters. Is there greenwashing? Of course! Is everyone who should have a say in this process in the room? Not even close,” wrote Khotimsky. “So what does it take for COP to matter? It takes everyone who attended to not only put ‘climate’ on front-page news for two weeks, but to return home and keep fighting relentlessly against climate change. I know that I will.” More

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      in Data Management & Statistics

      MIT Policy Hackathon produces new solutions for technology policy challenges

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      Almost three years ago, the Covid-19 pandemic changed the world. Many are still looking to uncover a “new normal.”

      “Instead of going back to normal, [there’s a new generation that] wants to build back something different, something better,” says Jorge Sandoval, a second-year graduate student in MIT’s Technology and Policy Program (TPP) at the Institute for Data, Systems and Society (IDSS). “How do we communicate this mindset to others, that the world cannot be the same as before?”

      This was the inspiration behind “A New (Re)generation,” this year’s theme for the IDSS-student-run MIT Policy Hackathon, which Sandoval helped to organize as the event chair. The Policy Hackathon is a weekend-long, interdisciplinary competition that brings together participants from around the globe to explore potential solutions to some of society’s greatest challenges. 

      Unlike other competitions of its kind, Sandoval says MIT’s event emphasizes a humanistic approach. “The idea of our hackathon is to promote applications of technology that are humanistic or human-centered,” he says. “We take the opportunity to examine aspects of technology in the spaces where they tend to interact with society and people, an opportunity most technical competitions don’t offer because their primary focus is on the technology.”

      The competition started with 50 teams spread across four challenge categories. This year’s categories included Internet and Cybersecurity, Environmental Justice, Logistics, and Housing and City Planning. While some people come into the challenge with friends, Sandoval said most teams form organically during an online networking meeting hosted by MIT.

      “We encourage people to pair up with others outside of their country and to form teams of different diverse backgrounds and ages,” Sandoval says. “We try to give people who are often not invited to the decision-making table the opportunity to be a policymaker, bringing in those with backgrounds in not only law, policy, or politics, but also medicine, and people who have careers in engineering or experience working in nonprofits.”

      Once an in-person event, the Policy Hackathon has gone through its own regeneration process these past three years, according to Sandoval. After going entirely online during the pandemic’s height, last year they successfully hosted the first hybrid version of the event, which served as their model again this year.

      “The hybrid version of the event gives us the opportunity to allow people to connect in a way that is lost if it is only online, while also keeping the wide range of accessibility, allowing people to join from anywhere in the world, regardless of nationality or income, to provide their input,” Sandoval says.

      For Swetha Tadisina, an undergraduate computer science major at Lafayette College and participant in the internet and cybersecurity category, the hackathon was a unique opportunity to meet and work with people much more advanced in their careers. “I was surprised how such a diverse team that had never met before was able to work so efficiently and creatively,” Tadisina says.

      Erika Spangler, a public high school teacher from Massachusetts and member of the environmental justice category’s winning team, says that while each member of “Team Slime Mold” came to the table with a different set of skills, they managed to be in sync from the start — even working across the nine-and-a-half-hour time difference the four-person team faced when working with policy advocate Shruti Nandy from Calcutta, India.

      “We divided the project into data, policy, and research and trusted each other’s expertise,” Spangler says, “Despite having separate areas of focus, we made sure to have regular check-ins to problem-solve and cross-pollinate ideas.”

      During the 48-hour period, her team proposed the creation of an algorithm to identify high-quality brownfields that could be cleaned up and used as sites for building renewable energy. Their corresponding policy sought to mandate additional requirements for renewable energy businesses seeking tax credits from the Inflation Reduction Act.

      “Their policy memo had the most in-depth technical assessment, including deep dives in a few key cities to show the impact of their proposed approach for site selection at a very granular level,” says Amanda Levin, director of policy analysis for the Natural Resources Defense Council (NRDC). Levin acted as both a judge and challenge provider for the environmental justice category.

      “They also presented their policy recommendations in the memo in a well-thought-out way, clearly noting the relevant actor,” she adds. This clarity around what can be done, and who would be responsible for those actions, is highly valuable for those in policy.”

      Levin says the NRDC, one of the largest environmental nonprofits in the United States, provided five “challenge questions,” making it clear that teams did not need to address all of them. She notes that this gave teams significant leeway, bringing a wide variety of recommendations to the table. 

      “As a challenge partner, the work put together by all the teams is already being used to help inform discussions about the implementation of the Inflation Reduction Act,” Levin says. “Being able to tap into the collective intelligence of the hackathon helped uncover new perspectives and policy solutions that can help make an impact in addressing the important policy challenges we face today.”

      While having partners with experience in data science and policy definitely helped, fellow Team Slime Mold member Sara Sheffels, a PhD candidate in MIT’s biomaterials program, says she was surprised how much her experiences outside of science and policy were relevant to the challenge: “My experience organizing MIT’s Graduate Student Union shaped my ideas about more meaningful community involvement in renewables projects on brownfields. It is not meaningful to merely educate people about the importance of renewables or ask them to sign off on a pre-planned project without addressing their other needs.”

      “I wanted to test my limits, gain exposure, and expand my world,” Tadisina adds. “The exposure, friendships, and experiences you gain in such a short period of time are incredible.”

      For Willy R. Vasquez, an electrical and computer engineering PhD student at the University of Texas, the hackathon is not to be missed. “If you’re interested in the intersection of tech, society, and policy, then this is a must-do experience.” More

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      in Data Management & Statistics

      A far-sighted approach to machine learning

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      Picture two teams squaring off on a football field. The players can cooperate to achieve an objective, and compete against other players with conflicting interests. That’s how the game works.

      Creating artificial intelligence agents that can learn to compete and cooperate as effectively as humans remains a thorny problem. A key challenge is enabling AI agents to anticipate future behaviors of other agents when they are all learning simultaneously.

      Because of the complexity of this problem, current approaches tend to be myopic; the agents can only guess the next few moves of their teammates or competitors, which leads to poor performance in the long run. 

      Researchers from MIT, the MIT-IBM Watson AI Lab, and elsewhere have developed a new approach that gives AI agents a farsighted perspective. Their machine-learning framework enables cooperative or competitive AI agents to consider what other agents will do as time approaches infinity, not just over a few next steps. The agents then adapt their behaviors accordingly to influence other agents’ future behaviors and arrive at an optimal, long-term solution.

      This framework could be used by a group of autonomous drones working together to find a lost hiker in a thick forest, or by self-driving cars that strive to keep passengers safe by anticipating future moves of other vehicles driving on a busy highway.

      “When AI agents are cooperating or competing, what matters most is when their behaviors converge at some point in the future. There are a lot of transient behaviors along the way that don’t matter very much in the long run. Reaching this converged behavior is what we really care about, and we now have a mathematical way to enable that,” says Dong-Ki Kim, a graduate student in the MIT Laboratory for Information and Decision Systems (LIDS) and lead author of a paper describing this framework.

      The senior author is Jonathan P. How, the Richard C. Maclaurin Professor of Aeronautics and Astronautics and a member of the MIT-IBM Watson AI Lab. Co-authors include others at the MIT-IBM Watson AI Lab, IBM Research, Mila-Quebec Artificial Intelligence Institute, and Oxford University. The research will be presented at the Conference on Neural Information Processing Systems.

      Play video

      In this demo video, the red robot, which has been trained using the researchers’ machine-learning system, is able to defeat the green robot by learning more effective behaviors that take advantage of the constantly changing strategy of its opponent.

      More agents, more problems

      The researchers focused on a problem known as multiagent reinforcement learning. Reinforcement learning is a form of machine learning in which an AI agent learns by trial and error. Researchers give the agent a reward for “good” behaviors that help it achieve a goal. The agent adapts its behavior to maximize that reward until it eventually becomes an expert at a task.

      But when many cooperative or competing agents are simultaneously learning, things become increasingly complex. As agents consider more future steps of their fellow agents, and how their own behavior influences others, the problem soon requires far too much computational power to solve efficiently. This is why other approaches only focus on the short term.

      “The AIs really want to think about the end of the game, but they don’t know when the game will end. They need to think about how to keep adapting their behavior into infinity so they can win at some far time in the future. Our paper essentially proposes a new objective that enables an AI to think about infinity,” says Kim.

      But since it is impossible to plug infinity into an algorithm, the researchers designed their system so agents focus on a future point where their behavior will converge with that of other agents, known as equilibrium. An equilibrium point determines the long-term performance of agents, and multiple equilibria can exist in a multiagent scenario. Therefore, an effective agent actively influences the future behaviors of other agents in such a way that they reach a desirable equilibrium from the agent’s perspective. If all agents influence each other, they converge to a general concept that the researchers call an “active equilibrium.”

      The machine-learning framework they developed, known as FURTHER (which stands for FUlly Reinforcing acTive influence witH averagE Reward), enables agents to learn how to adapt their behaviors as they interact with other agents to achieve this active equilibrium.

      FURTHER does this using two machine-learning modules. The first, an inference module, enables an agent to guess the future behaviors of other agents and the learning algorithms they use, based solely on their prior actions.

      This information is fed into the reinforcement learning module, which the agent uses to adapt its behavior and influence other agents in a way that maximizes its reward.

      “The challenge was thinking about infinity. We had to use a lot of different mathematical tools to enable that, and make some assumptions to get it to work in practice,” Kim says.

      Winning in the long run

      They tested their approach against other multiagent reinforcement learning frameworks in several different scenarios, including a pair of robots fighting sumo-style and a battle pitting two 25-agent teams against one another. In both instances, the AI agents using FURTHER won the games more often.

      Since their approach is decentralized, which means the agents learn to win the games independently, it is also more scalable than other methods that require a central computer to control the agents, Kim explains.

      The researchers used games to test their approach, but FURTHER could be used to tackle any kind of multiagent problem. For instance, it could be applied by economists seeking to develop sound policy in situations where many interacting entitles have behaviors and interests that change over time.

      Economics is one application Kim is particularly excited about studying. He also wants to dig deeper into the concept of an active equilibrium and continue enhancing the FURTHER framework.

      This research is funded, in part, by the MIT-IBM Watson AI Lab. More