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    Making each vote count

    Graduate student Jacob Jaffe wants to improve the administration of American elections. To do that, he is posing “questions in political science that we haven’t been asking enough,” he says, “and solving them with methods we haven’t been using enough.”

    Considerable research has been devoted to understanding “who votes, and what makes people vote or not vote,” says Jaffe. He is training his attention on questions of a different nature: Does providing practical information to voters about how to cast their ballots change how they will vote? Is it possible to increase the accuracy of vote-counting, on a state-by-state and even precinct-by-precinct basis? How do voters experience polling places? These problems form the core of his dissertation.

    Taking advantage of the resources at the MIT Election Data and Science Lab, where he serves as a researcher, Jaffe conducts novel field experiments to gather highly detailed information on local, state, and federal elections, and analyzes this trove with advanced statistical techniques. Whether investigating the probability of miscounts in voting, or the possibility of changing a voter’s mode of voting, Jaffe intends to strengthen the scaffolding that supports representative government. “Elections are both theoretically and normatively important; they’re the basis of our belief in the moral rightness of the state to do the things the state does,” he says.

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    For one of his keystone projects, Jaffe seized a unique opportunity to run a big field experiment. In summer 2020, at the height of the Covid-19 pandemic, he emailed 80,000 Floridians instructions on how to vote in an upcoming primary by mail. His email contained a link enabling recipients to fill out two simple questions to receive a ballot. “I wanted to learn if this was an effective method for getting people to vote by mail, and I proved it is, statistically,” he says. “This is important to know because if elections are held in times when we might need people to vote nonlocally or vote using one method over another — if they’re displaced by a hurricane or another emergency, for instance — I learned that we can effect a new vote mode practically and quickly.”

    One of Jaffe’s insights from this experiment is that “people do read their voting-related emails, but the content of the email has to be something they can act on proximately,” he says. “A message reminding them to vote two weeks from now is not so helpful.” The lower the burden on an individual to participate in voting, whether due to proximity to a polling site or instructions on how to receive and cast a ballot, the greater the likelihood of that person engaging in the election.

    “If we want people to vote by mail, we need to reduce the informational cost so it’s easier for voters to understand how the system works,” he says.

    Another significant research thrust for Jaffe involves scrutinizing accuracy in vote counting, using instances of recounts in presidential elections. Ensuring each vote counts, he says, “is one of the most fundamental questions in democracy,” he says.

    With access to 20 elections in 2020, Jaffe is comparing original vote totals for each candidate to the recounted, correct tally, on a precinct-level basis. “Using original combinatorial techniques, I can estimate the probability of miscounting ballots,” he says. The ultimate goal is to generate a granular picture of the efficacy of election administration across the country.

    “It varies a lot by state, and most states do a good job,” he says. States that take their time in counting perform better. “There’s a phenomenon where some towns race to get results in as quickly as possible, and this affects their accuracy.”

    In spite of the bright spots, Jaffe sees chronic underfunding of American elections. “We need to give local administrators the resources, the time and money to fund employees to do their jobs,” he says. The worse the situation is, “the more likely that elections will be called wrong, with no one knowing.” Jaffe believes that his analysis can offer states useful information for improving election administration. “Determining how good a place is historically at counting ballots can help determine the likelihood of needing costly recounts in future elections,” he says.

    The ballot box and beyond

    It didn’t take Jaffe long to decide on a life dedicated to studying politics. Part of a Boston-area family who, he says, “liked discussing what was going on in the world,” he had his own subscriptions to Time magazine at age 9, and to The Economist in middle school. During high school, he volunteered for then-Massachusetts Representative Barney Frank and Senator John Kerry, working on constituent services. At Rice University, he interned all four years with political scientist Robert M. Stein, an expert on voting and elections. With Stein’s help, Jaffe landed a position the summer before his senior year with the Department of Justice (DOJ), researching voting rights cases.

    “The experience was fascinating, and the work felt super important,” says Jaffe. His portfolio involved determining whether legal challenges to particular elections met the statistical standard for racial gerrymandering. “I had to answer hard quantitative questions about the relationship between race and voting in an area, and whether minority candidates were systematically prevented from winning,” he says.

    But while Jaffe cared a lot about this work, he didn’t feel adequately challenged. “As a 21-year-old at DOJ, I learned that I could address problems in the world using statistics,” he says. “But I felt I could have a greater impact addressing tougher questions outside of voting rights.”

    Jaffe was drawn to political science at MIT, and specifically to the research of Charles Stewart III, the Kenan Sahin Distinguished Professor of Political Science, director of the MIT Election Lab, and head of Jaffe’s thesis committee. It wasn’t just the opportunity to plumb the lab’s singular repository of voting data that attracted Jaffe, but its commitment to making every vote count. For Jaffe, this was a call to arms to investigate the many, and sometimes quotidian, obstacles, between citizens and ballot boxes.

    To this end, he has been analyzing, with the help of mathematical methods from queuing theory, why some elections involve wait lines of six hours and longer at polling sites. “We know that simpler ballots mean people move don’t get stuck in these lines, where they might potentially give up before voting,” he says. “Looking at the content of ballots and the interval between voter check-in and check-out, I learned that adding races, rather than candidates, to a ballot, means that people take more time completing ballots, leading to interminable lines.”

    A key takeaway from his ensemble of studies is that “while it’s relatively rare that elections are bad, we shouldn’t think that we’re good to go,” he says. “Instead, we need to be asking under what conditions do things get bad, and how can we make them better.” More

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    Investigating at the interface of data science and computing

    A visual model of Guy Bresler’s research would probably look something like a Venn diagram. He works at the four-way intersection where theoretical computer science, statistics, probability, and information theory collide.

    “There are always new things to do be done at the interface. There are always opportunities for entirely new questions to ask,” says Bresler, an associate professor who recently earned tenure in MIT’s Department of Electrical Engineering and Computer Science (EECS).

    A theoretician, he aims to understand the delicate interplay between structure in data, the complexity of models, and the amount of computation needed to learn those models. Recently, his biggest focus has been trying to unveil fundamental phenomena that are broadly responsible for determining the computational complexity of statistics problems — and finding the “sweet spot” where available data and computation resources enable researchers to effectively solve a problem.

    When trying to solve a complex statistics problem, there is often a tug-of-war between data and computation. Without enough data, the computation needed to solve a statistical problem can be intractable, or at least consume a staggering amount of resources. But get just enough data and suddenly the intractable becomes solvable; the amount of computation needed to come up with a solution drops dramatically.

    The majority of modern statistical problems exhibits this sort of trade-off between computation and data, with applications ranging from drug development to weather prediction. Another well-studied and practically important example is cryo-electron microscopy, Bresler says. With this technique, researchers use an electron microscope to take images of molecules in different orientations. The central challenge is how to solve the inverse problem — determining the molecule’s structure given the noisy data. Many statistical problems can be formulated as inverse problems of this sort.

    One aim of Bresler’s work is to elucidate relationships between the wide variety of different statistics problems currently being studied. The dream is to classify statistical problems into equivalence classes, as has been done for other types of computational problems in the field of computational complexity. Showing these sorts of relationships means that, instead of trying to understand each problem in isolation, researchers can transfer their understanding from a well-studied problem to a poorly understood one, he says.

    Adopting a theoretical approach

    For Bresler, a desire to theoretically understand various basic phenomena inspired him to follow a path into academia.

    Both of his parents worked as professors and showed how fulfilling academia can be, he says. His earliest introduction to the theoretical side of engineering came from his father, who is an electrical engineer and theoretician studying signal processing. Bresler was inspired by his work from an early age. As an undergraduate at the University of Illinois at Urbana-Champaign, he bounced between physics, math, and computer science courses. But no matter the topic, he gravitated toward the theoretical viewpoint.

    In graduate school at the University of California at Berkeley, Bresler enjoyed the opportunity to work in a wide variety of topics spanning probability, theoretical computer science, and mathematics. His driving motivator was a love of learning new things.

    “Working at the interface of multiple fields with new questions, there is a feeling that one had better learn as much as possible if one is to have any chance of finding the right tools to answer those questions,” he says.

    That curiosity led him to MIT for a postdoc in the Laboratory for Information and Decision Systems (LIDS) in 2013, and then he joined the faculty two years later as an assistant professor in EECS. He was named an associate professor in 2019.

    Bresler says he was drawn to the intellectual atmosphere at MIT, as well as the supportive environment for launching bold research quests and trying to make progress in new areas of study.

    Opportunities for collaboration

    “What really struck me was how vibrant and energetic and collaborative MIT is. I have this mental list of more than 20 people here who I would love to have lunch with every single week and collaborate with on research. So just based on sheer numbers, joining MIT was a clear win,” he says.

    He’s especially enjoyed collaborating with his students, who continually teach him new things and ask deep questions that drive exciting research projects. One such student, Matthew Brennan, who was one of Bresler’s closest collaborators, tragically and unexpectedly passed away in January, 2021.

    The shock from Brennan’s death is still raw for Bresler, and it derailed his research for a time.

    “Beyond his own prodigious capabilities and creativity, he had this amazing ability to listen to an idea of mine that was almost completely wrong, extract from it a useful piece, and then pass the ball back,” he says. “We had the same vision for what we wanted to achieve in the work, and we were driven to try to tell a certain story. At the time, almost nobody was pursuing this particular line of work, and it was in a way kind of lonely. But he trusted me, and we encouraged one another to keep at it when things seemed bleak.”

    Those lessons in perseverance fuel Bresler as he and his students continue exploring questions that, by their nature, are difficult to answer.

    One area he’s worked in on-and-off for over a decade involves learning graphical models from data. Models of certain types of data, such as time-series data consisting of temperature readings, are often constructed by domain experts who have relevant knowledge and can build a reasonable model, he explains.

    But for many types of data with complex dependencies, such as social network or biological data, it is not at all clear what structure a model should take. Bresler’s work seeks to estimate a structured model from data, which could then be used for downstream applications like making recommendations or better predicting the weather.

    The basic question of identifying good models, whether algorithmically in a complex setting or analytically, by specifying a useful toy model for theoretical analysis, connects the abstract work with engineering practice, he says.

    “In general, modeling is an art. Real life is complicated and if you write down some super-complicated model that tries to capture every feature of a problem, it is doomed,” says Bresler. “You have to think about the problem and understand the practical side of things on some level to identify the correct features of the problem to be modeled, so that you can hope to actually solve it and gain insight into what one should do in practice.”

    Outside the lab, Bresler often finds himself solving very different kinds of problems. He is an avid rock climber and spends much of his free time bouldering throughout New England.

    “I really love it. It is a good excuse to get outside and get sucked into a whole different world. Even though there is problem solving involved, and there are similarities at the philosophical level, it is totally orthogonal to sitting down and doing math,” he says. More

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    Emma Gibson: Optimizing health care logistics in Africa

    Growing up in South Africa at the turn of the century, Emma Gibson saw the rise of the HIV/AIDS epidemic and its devastating impact on her home country, where many people lacked life-saving health care. At the time, Gibson was too young to understand what a sexually transmitted infection was, but she knew that HIV was infecting millions of South Africans and AIDS was taking hundreds of thousands of lives. “As a child, I was terrified by this monster that was HIV and felt so powerless to do anything about it,” she says.

    Now, as an adult, her childhood fear of the HIV epidemic has evolved into a desire to fight it. Gibson seeks to improve health care for HIV and other diseases in regions with limited resources, including South Africa. She wants to help health care facilities in these areas to use their resources more effectively so that patients can more easily obtain care.

    To help reach her goal, Gibson sought mathematics and logistics training through higher education in South Africa. She first earned her bachelor’s degree in mathematical sciences at the University of the Witwatersrand, and then her master’s degree in operations research at Stellenbosch University. There, she learned to tackle complex decision-making problems using math, statistics, and computer simulations.

    During her master’s, Gibson studied the operational challenges faced in rural South African health care facilities by working with staff at Zithulele Hospital in the Eastern Cape, one of the country’s poorest provinces. Her research focused on ways to reduce hours-long wait times for patients seeking same-day care. In the end, she developed a software tool to model patient congestion throughout the day and optimize staff schedules accordingly, enabling the hospital to care for its patients more efficiently.

    After completing her master’s, Gibson wanted to further her education outside of South Africa and left to pursue a PhD in operations research at MIT. Upon arrival, she branched out in her research and worked on a project to improve breast cancer treatment in U.S. health care, a very different environment from what she was used to.

    Two years later, Gibson had the opportunity to return to researching health care in resource-limited settings and began working with Jónas Jónasson, an associate professor at the MIT Sloan School of Management, on a new project to improve diagnostic services in sub-Saharan Africa. For the past four years, she has been working diligently on this project in collaboration with researchers at the Indian School of Business and Northwestern University. “My love language is time,” she says. “If I’m investing a lot of time in something, I really value it.”

    Scheduling sample transport

    Diagnostic testing is an essential tool that allows medical professionals to identify new diagnoses in patients and monitor patients’ conditions as they undergo treatment. For example, people living with HIV require regular blood tests to ensure that their prescribed treatments are working effectively and provide an early warning of potential treatment failures.

    For Gibson’s current project, she’s trying to improve diagnostic services in Malawi, a landlocked country in southeast Africa. “We have the tools” to diagnose and treat diseases like HIV, she says. “But in resource-limited settings, we often lack the money, the staff, and the infrastructure to reach every patient that needs them.”

    When diagnostic testing is needed, clinicians collect samples from patients and send the samples to be tested at a laboratory, which then returns the results to the facility where the patient is treated. To move these items between facilities and laboratories, Malawi has developed a national sample transportation network. The transportation system plays an important role in linking remote, rural facilities to laboratory services and ensuring that patients in these areas can access diagnostic testing through community clinics. Samples collected at these clinics are first transported to nearby district hubs, and then forwarded to laboratories located in urban areas. Since most facilities do not have computers or communications infrastructure, laboratories print copies of test results and send them back to facilities through the same transportation process.

    The sample transportation cycle is onerous, but it’s a practical solution to a difficult problem. “During the Covid pandemic, we saw how hard it was to scale up diagnostic infrastructure,” Gibson says. Diagnostic services in sub-Saharan Africa face “similar challenges, but in a much poorer setting.”

    In Malawi, sample transportation is managed by a  nongovernment organization called Riders 4 Health. The organization has around 80 couriers on motorcycles who transport samples and test results between facilities. “When we started working with [Riders], the couriers operated on fixed weekly schedules, visiting each site once or twice a week,” Gibson says. But that led to “a lot of unnecessary trips and delays.”

    To make sample transportation more efficient, Gibson developed a dynamic scheduling system that adapts to the current demand for diagnostic testing. The system consists of two main parts: an information sharing platform that aggregates sample transportation data, and an algorithm that uses the data to generate optimized routes and schedules for sample transport couriers.

    In 2019, Gibson ran a four-month-long pilot test for this system in three out of the 27 districts in Malawi. During the pilot study, six couriers transported over 20,000 samples and results across 51 health care facilities, and 150 health care workers participated in data sharing.

    The pilot was a success. Gibson’s dynamic scheduling system eliminated about half the unnecessary trips and reduced transportation delays by 25 percent — a delay that used to be four days was reduced to three. Now, Riders 4 Health is developing their own version of Gibson’s system to operate nationally in Malawi. Throughout this project, “we focused on making sure this was something that could grow with the organization,” she says. “It’s gratifying to see that actually happening.”

    Leveraging patient data

    Gibson is completing her MIT degree this September but will continue working to improve health care in Africa. After graduation, she will join the technology and analytics health care practice of an established company in South Africa. Her initial focus will be on public health care institutions, including Chris Hani Baragwanath Academic Hospital in Johannesburg, the third-largest hospital in the world.

    In this role, Gibson will work to fill in gaps in African patient data for medical operational research and develop ways to use this data more effectively to improve health care in resource-limited areas. For example, better data systems can help to monitor the prevalence and impact of different diseases, guiding where health care workers and researchers put their efforts to help the most people. “You can’t make good decisions if you don’t have all the information,” Gibson says.

    To best leverage patient data for improving health care, Gibson plans to reevaluate how data systems are structured and used in the hospital. For ideas on upgrading the current system, she’ll look to existing data systems in other countries to see what works and what doesn’t, while also drawing upon her past research experience in U.S. health care. Ultimately, she’ll tailor the new hospital data system to South African needs to accurately inform future directions in health care.

    Gibson’s new job — her “dream job” — will be based in the United Kingdom, but she anticipates spending a significant amount of time in Johannesburg. “I have so many opportunities in the wider world, but the ones that appeal to me are always back in the place I came from,” she says. More

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    Mining social media data for social good

    For Erin Walk, who has loved school since she was a little girl, pursuing a graduate degree always seemed like a given. As a mechanical engineering major at Harvard University with a minor in government, she figured that going to graduate school in engineering would be the next logical step. However, during her senior year, a class on the “Technology of War” changed her trajectory, sparking her interest in technology and policy.

    “[Warfare] seems like a very dark reason for this interest to blossom … but I was so interested in how these technological developments including cyberwar had such a large impact on the entire course of world history,” Walk says. The class took a starkly different perspective from her engineering classes, which often focused on how a revolutionary technology was built. Instead, Walk was challenged to think about “the implications of what this [technology] could do.” 

    Now, Walk is studying the intersection between data science, policy, and technology as a graduate student in the Social and Engineering Systems program (SES), part of the Institute for Data, Systems, and Society (IDSS). Her research has demonstrated the value and bias inherent in social media data, with a focus on how to mine social media data to better understand the conflict in Syria. 

    Using data for social good

    With a newfound interest in policy developing just as college was drawing to a close, Walk says, “I realized I did not know what I wanted to do research on for five whole years, and the idea of getting a PhD started to feel very daunting.” Instead, she decided to work for a web security company in Washington, as a member of the policy team. “Being in school can be this fast process where you feel like you are being pushed through a tube and all of a sudden you come out the other end. Work gave me a lot more mental time to think about what I enjoyed and what was important to me,” she says.

    Walk served as a liaison between thinktanks and nonprofits in Washington that worked to provide services and encourage policies that enable equitable technology distribution. The role helped her identify what held her interest: corporate social responsibility projects that addressed access to technology, in this case, by donating free web security services to nonprofit organizations and to election websites. She became curious about how access to data and to the Internet can be beneficial for education, and how such access can be leveraged to establish connections to populations that are otherwise hard-to-reach, such as refugees, marginalized groups, or activist communities that rely on anonymity for safety.

    Walk knew she wanted to pursue this kind of tech activism work, but she also recognized that staying with a company driven by profits would not be the best avenue to fulfill her personal career aspirations. Graduate school seemed like the best option to both learn the data science skills she needed, and pursue full-time research focusing on technology and policy.

    Finding new ways to tap social media data

    With these goals in mind, Walk joined the SES graduate program in IDSS. “This program for me had the most balance,” she says. “I have a lot of leeway to explore whatever kind of research I want, provided it has an impact component and a data component.”

    During her first year, she intended to explore a variety of research advisors to find the right fit. Instead, during her first few months on MIT’s campus, she sat down for an introductory meeting with her now-research advisor, Fotini Christia, the Ford International Professor in the Social Sciences, and walked out with a project. Her new task: analyzing “how different social media sources are used differently by groups within the conflict, and how those different narratives present themselves online. So much social science research tends to use just Twitter, or just Facebook, to draw conclusions. It is important to understand how your data set might be skewed,” she says.

    Walk’s current research focuses on another novel way to tap social media. Scholars traditionally use geographic data to understand population movements, but her research has demonstrated that social media can also be a ripe data source. She is analyzing how social media discussions differ in places with and without refugees, with a particular focus on places where refugees have returned to their homelands, including Syria.

    “Now that the [Syrian] civil war has been going on for so long, there is a lot of discussion on how to bring refugees back in [to their homelands],” Walk says. Her research adds to this discussion by using social media sources to understand and predict the factors that encourage refugees to return, such as economic opportunities and decreases in local violence. Her goal is to harness some of the social media data to provide policymakers and nonprofits with information on how to address repatriation and related issues.

    Walk attributes much of her growth as a graduate student to the influence of collaborators, especially Professor Kiran Garimella at Rutgers’ Department of Library and Information Science. “So much of being a graduate student is feeling like you have a stupid question and figuring out who you can be vulnerable with in asking that stupid question,” she says. “I am very lucky to have a lot of those people in my life.”

    Encouraging the next generation

    Now, as a third-year student, Walk is the one whom others go to with their “stupid questions.” This desire to mentor and share her knowledge extends beyond the laboratory. “Something I discovered is that I really like talking to and advising people who are in a similar position to where I was. It is fulfilling to work with smart people close to my age who are just trying to figure out the answers to these meaty life issues that I have also struggled with,” she says.

    This realization led Walk to a position as a resident advisor at Harvard University’s Mather House, an undergraduate dormitory and community center. Walk became a faculty dean aide during her first year at MIT, and since then has served as a full-time Mather House resident tutor. “Every year I advise a new class of students, and I just become invested in their process. I get to talk to people about their lives, about their classes, about what is making them excited and about what is making them sad,” she says.

    After she graduates, Walk plans to explore issues that have a positive, tangible impact on policy outcomes and people, perhaps in an academic lab or in a nonprofit organization. Two such issues that particularly intrigue her are internet access and privacy for underserved populations. Regardless of the issues, she will continue to draw from both political science and data science. “One of my favorite things about being a part of interdisciplinary research is that [experts in] political science and computer science approach these issues so differently, and it is very grounding to have both of those perspectives. Political science thinks so carefully about measurement, population selection, and research design … [while] computer science has so many interesting methods that should be used in other disciplines,” she says.

    No matter what the future holds, Walk already has a sense of contentment. She admits that “my path was much less linear than I expected. I don’t think I even realized that a field like this existed.” Nevertheless, she says with a laugh, “I think that little-girl me would be very proud of present-day me.” More

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    Living better with algorithms

    Laboratory for Information and Decision Systems (LIDS) student Sarah Cen remembers the lecture that sent her down the track to an upstream question.

    At a talk on ethical artificial intelligence, the speaker brought up a variation on the famous trolley problem, which outlines a philosophical choice between two undesirable outcomes.

    The speaker’s scenario: Say a self-driving car is traveling down a narrow alley with an elderly woman walking on one side and a small child on the other, and no way to thread between both without a fatality. Who should the car hit?

    Then the speaker said: Let’s take a step back. Is this the question we should even be asking?

    That’s when things clicked for Cen. Instead of considering the point of impact, a self-driving car could have avoided choosing between two bad outcomes by making a decision earlier on — the speaker pointed out that, when entering the alley, the car could have determined that the space was narrow and slowed to a speed that would keep everyone safe.

    Recognizing that today’s AI safety approaches often resemble the trolley problem, focusing on downstream regulation such as liability after someone is left with no good choices, Cen wondered: What if we could design better upstream and downstream safeguards to such problems? This question has informed much of Cen’s work.

    “Engineering systems are not divorced from the social systems on which they intervene,” Cen says. Ignoring this fact risks creating tools that fail to be useful when deployed or, more worryingly, that are harmful.

    Cen arrived at LIDS in 2018 via a slightly roundabout route. She first got a taste for research during her undergraduate degree at Princeton University, where she majored in mechanical engineering. For her master’s degree, she changed course, working on radar solutions in mobile robotics (primarily for self-driving cars) at Oxford University. There, she developed an interest in AI algorithms, curious about when and why they misbehave. So, she came to MIT and LIDS for her doctoral research, working with Professor Devavrat Shah in the Department of Electrical Engineering and Computer Science, for a stronger theoretical grounding in information systems.

    Auditing social media algorithms

    Together with Shah and other collaborators, Cen has worked on a wide range of projects during her time at LIDS, many of which tie directly to her interest in the interactions between humans and computational systems. In one such project, Cen studies options for regulating social media. Her recent work provides a method for translating human-readable regulations into implementable audits.

    To get a sense of what this means, suppose that regulators require that any public health content — for example, on vaccines — not be vastly different for politically left- and right-leaning users. How should auditors check that a social media platform complies with this regulation? Can a platform be made to comply with the regulation without damaging its bottom line? And how does compliance affect the actual content that users do see?

    Designing an auditing procedure is difficult in large part because there are so many stakeholders when it comes to social media. Auditors have to inspect the algorithm without accessing sensitive user data. They also have to work around tricky trade secrets, which can prevent them from getting a close look at the very algorithm that they are auditing because these algorithms are legally protected. Other considerations come into play as well, such as balancing the removal of misinformation with the protection of free speech.

    To meet these challenges, Cen and Shah developed an auditing procedure that does not need more than black-box access to the social media algorithm (which respects trade secrets), does not remove content (which avoids issues of censorship), and does not require access to users (which preserves users’ privacy).

    In their design process, the team also analyzed the properties of their auditing procedure, finding that it ensures a desirable property they call decision robustness. As good news for the platform, they show that a platform can pass the audit without sacrificing profits. Interestingly, they also found the audit naturally incentivizes the platform to show users diverse content, which is known to help reduce the spread of misinformation, counteract echo chambers, and more.

    Who gets good outcomes and who gets bad ones?

    In another line of research, Cen looks at whether people can receive good long-term outcomes when they not only compete for resources, but also don’t know upfront what resources are best for them.

    Some platforms, such as job-search platforms or ride-sharing apps, are part of what is called a matching market, which uses an algorithm to match one set of individuals (such as workers or riders) with another (such as employers or drivers). In many cases, individuals have matching preferences that they learn through trial and error. In labor markets, for example, workers learn their preferences about what kinds of jobs they want, and employers learn their preferences about the qualifications they seek from workers.

    But learning can be disrupted by competition. If workers with a particular background are repeatedly denied jobs in tech because of high competition for tech jobs, for instance, they may never get the knowledge they need to make an informed decision about whether they want to work in tech. Similarly, tech employers may never see and learn what these workers could do if they were hired.

    Cen’s work examines this interaction between learning and competition, studying whether it is possible for individuals on both sides of the matching market to walk away happy.

    Modeling such matching markets, Cen and Shah found that it is indeed possible to get to a stable outcome (workers aren’t incentivized to leave the matching market), with low regret (workers are happy with their long-term outcomes), fairness (happiness is evenly distributed), and high social welfare.

    Interestingly, it’s not obvious that it’s possible to get stability, low regret, fairness, and high social welfare simultaneously.  So another important aspect of the research was uncovering when it is possible to achieve all four criteria at once and exploring the implications of those conditions.

    What is the effect of X on Y?

    For the next few years, though, Cen plans to work on a new project, studying how to quantify the effect of an action X on an outcome Y when it’s expensive — or impossible — to measure this effect, focusing in particular on systems that have complex social behaviors.

    For instance, when Covid-19 cases surged in the pandemic, many cities had to decide what restrictions to adopt, such as mask mandates, business closures, or stay-home orders. They had to act fast and balance public health with community and business needs, public spending, and a host of other considerations.

    Typically, in order to estimate the effect of restrictions on the rate of infection, one might compare the rates of infection in areas that underwent different interventions. If one county has a mask mandate while its neighboring county does not, one might think comparing the counties’ infection rates would reveal the effectiveness of mask mandates. 

    But of course, no county exists in a vacuum. If, for instance, people from both counties gather to watch a football game in the maskless county every week, people from both counties mix. These complex interactions matter, and Sarah plans to study questions of cause and effect in such settings.

    “We’re interested in how decisions or interventions affect an outcome of interest, such as how criminal justice reform affects incarceration rates or how an ad campaign might change the public’s behaviors,” Cen says.

    Cen has also applied the principles of promoting inclusivity to her work in the MIT community.

    As one of three co-presidents of the Graduate Women in MIT EECS student group, she helped organize the inaugural GW6 research summit featuring the research of women graduate students — not only to showcase positive role models to students, but also to highlight the many successful graduate women at MIT who are not to be underestimated.

    Whether in computing or in the community, a system taking steps to address bias is one that enjoys legitimacy and trust, Cen says. “Accountability, legitimacy, trust — these principles play crucial roles in society and, ultimately, will determine which systems endure with time.”  More

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    Frequent encounters build familiarity

    Do better spatial networks make for better neighbors? There is evidence that they do, according to Paige Bollen, a sixth-year political science graduate student at MIT. The networks Bollen works with are not virtual but physical, part of the built environment in which we are all embedded. Her research on urban spaces suggests that the routes bringing people together or keeping them apart factor significantly in whether individuals see each other as friend or foe.

    “We all live in networks of streets, and come across different types of people,” says Bollen. “Just passing by others provides information that informs our political and social views of the world.” In her doctoral research, Bollen is revealing how physical context matters in determining whether such ordinary encounters engender suspicion or even hostility, while others can lead to cooperation and tolerance.

    Through her in-depth studies mapping the movement of people in urban communities in Ghana and South Africa, Bollen is demonstrating that even in diverse communities, “when people repeatedly come into contact, even if that contact is casual, they can build understanding that can lead to cooperation and positive outcomes,” she says. “My argument is that frequent, casual contact, facilitated by street networks, can make people feel more comfortable with those unlike themselves,” she says.

    Mapping urban networks

    Bollen’s case for the benefits of casual contact emerged from her pursuit of several related questions: Why do people in urban areas who regard other ethnic groups with prejudice and economic envy nevertheless manage to collaborate for a collective good? How do you reduce fears that arise from differences? How do the configuration of space and the built environment influence contact patterns among people?

    While other social science research suggests that there are weak ties in ethnically mixed urban communities, with casual contact exacerbating hostility, Bollen noted that there were plenty of examples of “cooperation across ethnic divisions in ethnically mixed communities.” She absorbed the work of psychologist Stanley Milgram, whose 1972 research showed that strangers seen frequently in certain places become familiar — less anonymous or threatening. So she set out to understand precisely how “the built environment of a neighborhood interacts with its demography to create distinct patterns of contact between social groups.”

    With the support of MIT Global Diversity Lab and MIT GOV/LAB, Bollen set out to develop measures of intergroup contact in cities in Ghana and South Africa. She uses street network data to predict contact patterns based on features of the built environment and then combines these measures with mobility data on peoples’ actual movement.

    “I created a huge dataset for every intersection in these cities, to determine the central nodes where many people are passing through,” she says. She combined these datasets with census data to determine which social groups were most likely to use specific intersections based on their position in a particular street network. She mapped these measures of casual contact to outcomes, such as inter-ethnic cooperation in Ghana and voting behavior in South Africa.

    “My analysis [in Ghana] showed that in areas that are more ethnically heterogeneous and where there are more people passing through intersections, we find more interconnections among people and more cooperation within communities in community development efforts,” she says.

    In a related survey experiment conducted on Facebook with 1,200 subjects, Bollen asked Accra residents if they would help an unknown non-co-ethnic in need with a financial gift. She found that the likelihood of offering such help was strongly linked to the frequency of interactions. “Helping behavior occurred when the subjects believed they would see this person again, even when they did not know the person in need well,” says Bollen. “They figured if they helped, they could count on this person’s reciprocity in the future.”

    For Bollen, this was “a powerful gut check” for her hypothesis that “frequency builds familiarity, because frequency provides information and drives expectations, which means it can reduce uncertainty and fear of the other.”

    In research underway in South Africa, a nation increasingly dealing with anti-immigrant violence, Bollen is investigating whether frequency of contact reduces prejudice against foreigners. Using her detailed street maps, 1.1 billion unique geolocated cellphone pings, and election data, she finds that frequent contact opportunities with immigrants are associated with lower support for anti-immigrant party voting.    Passion for places and spaces

    Bollen never anticipated becoming a political scientist. The daughter of two academics, she was “bent on becoming a data scientist.” But she was also “always interested in why people behave in certain ways and how this influences macro trends.”

    As an undergraduate at Tufts University, she became interested in international affairs. But it was her 2013 fieldwork studying women-only carriages in Delhi, India’s metro system, that proved formative. “I interviewed women for a month, talking to them about how these cars enabled them to participate in public life,” she recalls. Another project involving informal transportation routes in Cape Town, South Africa, immersed her more deeply in the questions of people’s experience of public space. “I left college thinking about mobility and public space, and I discovered how much I love geographic information systems,” she says.

    A gig with the Commonwealth of Massachusetts to improve the 911 emergency service — updating and cleaning geolocations of addresses using Google Street View — further piqued her interest. “The job was tedious, but I realized you can really understand a place, and how people move around, from these images.” Bollen began thinking about a career in urban planning.

    Then a two-year stint as a researcher at MIT GOV/LAB brought Bollen firmly into the political science fold. Working with Lily Tsai, the Ford Professor of Political Science, on civil society partnerships in the developing world, Bollen realized that “political science wasn’t what I thought it was,” she says. “You could bring psychology, economics, and sociology into thinking about politics.” Her decision to join the doctoral program was simple: “I knew and loved the people I was with at MIT.”

    Bollen has not regretted that decision. “All the things I’ve been interested in are finally coming together in my dissertation,” she says. Due to the pandemic, questions involving space, mobility, and contact became sharper to her. “I shifted my research emphasis from asking people about inter-ethnic differences and inequality through surveys, to using contact and context information to measure these variables.”

    She sees a number of applications for her work, including working with civil society organizations in communities touched by ethnic or other frictions “to rethink what we know about contact, challenging some of the classic things we think we know.”

    As she moves into the final phases of her dissertation, which she hopes to publish as a book, Bollen also relishes teaching comparative politics to undergraduates. “There’s something so fun engaging with them, and making their arguments stronger,” she says. With the long process of earning a PhD, this helps her “enjoy what she is doing every single day.” More

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    How artificial intelligence can help combat systemic racism

    In 2020, Detroit police arrested a Black man for shoplifting almost $4,000 worth of watches from an upscale boutique. He was handcuffed in front of his family and spent a night in lockup. After some questioning, however, it became clear that they had the wrong man. So why did they arrest him in the first place?

    The reason: a facial recognition algorithm had matched the photo on his driver’s license to grainy security camera footage.

    Facial recognition algorithms — which have repeatedly been demonstrated to be less accurate for people with darker skin — are just one example of how racial bias gets replicated within and perpetuated by emerging technologies.

    “There’s an urgency as AI is used to make really high-stakes decisions,” says MLK Visiting Professor S. Craig Watkins, whose academic home for his time at MIT is the Institute for Data, Systems, and Society (IDSS). “The stakes are higher because new systems can replicate historical biases at scale.”

    Watkins, a professor at the University of Texas at Austin and the founding director of the Institute for Media Innovation​, researches the impacts of media and data-based systems on human behavior, with a specific concentration on issues related to systemic racism. “One of the fundamental questions of the work is: how do we build AI models that deal with systemic inequality more effectively?”

    Play video

    Artificial Intelligence and the Future of Racial Justice | S. Craig Watkins | TEDxMIT

    Ethical AI

    Inequality is perpetuated by technology in many ways across many sectors. One broad domain is health care, where Watkins says inequity shows up in both quality of and access to care. The demand for mental health care, for example, far outstrips the capacity for services in the United States. That demand has been exacerbated by the pandemic, and access to care is harder for communities of color.

    For Watkins, taking the bias out of the algorithm is just one component of building more ethical AI. He works also to develop tools and platforms that can address inequality outside of tech head-on. In the case of mental health access, this entails developing a tool to help mental health providers deliver care more efficiently.

    “We are building a real-time data collection platform that looks at activities and behaviors and tries to identify patterns and contexts in which certain mental states emerge,” says Watkins. “The goal is to provide data-informed insights to care providers in order to deliver higher-impact services.”

    Watkins is no stranger to the privacy concerns such an app would raise. He takes a user-centered approach to the development that is grounded in data ethics. “Data rights are a significant component,” he argues. “You have to give the user complete control over how their data is shared and used and what data a care provider sees. No one else has access.”

    Combating systemic racism

    Here at MIT, Watkins has joined the newly launched Initiative on Combatting Systemic Racism (ICSR), an IDSS research collaboration that brings together faculty and researchers from the MIT Stephen A. Schwarzman College of Computing and beyond. The aim of the ICSR is to develop and harness computational tools that can help effect structural and normative change toward racial equity.

    The ICSR collaboration has separate project teams researching systemic racism in different sectors of society, including health care. Each of these “verticals” addresses different but interconnected issues, from sustainability to employment to gaming. Watkins is a part of two ICSR groups, policing and housing, that aim to better understand the processes that lead to discriminatory practices in both sectors. “Discrimination in housing contributes significantly to the racial wealth gap in the U.S.,” says Watkins.

    The policing team examines patterns in how different populations get policed. “There is obviously a significant and charged history to policing and race in America,” says Watkins. “This is an attempt to understand, to identify patterns, and note regional differences.”

    Watkins and the policing team are building models using data that details police interventions, responses, and race, among other variables. The ICSR is a good fit for this kind of research, says Watkins, who notes the interdisciplinary focus of both IDSS and the SCC. 

    “Systemic change requires a collaborative model and different expertise,” says Watkins. “We are trying to maximize influence and potential on the computational side, but we won’t get there with computation alone.”

    Opportunities for change

    Models can also predict outcomes, but Watkins is careful to point out that no algorithm alone will solve racial challenges.

    “Models in my view can inform policy and strategy that we as humans have to create. Computational models can inform and generate knowledge, but that doesn’t equate with change.” It takes additional work — and additional expertise in policy and advocacy — to use knowledge and insights to strive toward progress.

    One important lever of change, he argues, will be building a more AI-literate society through access to information and opportunities to understand AI and its impact in a more dynamic way. He hopes to see greater data rights and greater understanding of how societal systems impact our lives.

    “I was inspired by the response of younger people to the murders of George Floyd and Breonna Taylor,” he says. “Their tragic deaths shine a bright light on the real-world implications of structural racism and has forced the broader society to pay more attention to this issue, which creates more opportunities for change.” More

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    Understanding air pollution from space

    Climate change and air pollution are interlocking crises that threaten human health. Reducing emissions of some air pollutants can help achieve climate goals, and some climate mitigation efforts can in turn improve air quality.

    One part of MIT Professor Arlene Fiore’s research program is to investigate the fundamental science in understanding air pollutants — how long they persist and move through our environment to affect air quality.

    “We need to understand the conditions under which pollutants, such as ozone, form. How much ozone is formed locally and how much is transported long distances?” says Fiore, who notes that Asian air pollution can be transported across the Pacific Ocean to North America. “We need to think about processes spanning local to global dimensions.”

    Fiore, the Peter H. Stone and Paola Malanotte Stone Professor in Earth, Atmospheric and Planetary Sciences, analyzes data from on-the-ground readings and from satellites, along with models, to better understand the chemistry and behavior of air pollutants — which ultimately can inform mitigation strategies and policy setting.

    A global concern

    At the United Nations’ most recent climate change conference, COP26, air quality management was a topic discussed over two days of presentations.

    “Breathing is vital. It’s life. But for the vast majority of people on this planet right now, the air that they breathe is not giving life, but cutting it short,” said Sarah Vogel, senior vice president for health at the Environmental Defense Fund, at the COP26 session.

    “We need to confront this twin challenge now through both a climate and clean air lens, of targeting those pollutants that warm both the air and harm our health.”

    Earlier this year, the World Health Organization (WHO) updated its global air quality guidelines it had issued 15 years earlier for six key pollutants including ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO). The new guidelines are more stringent based on what the WHO stated is the “quality and quantity of evidence” of how these pollutants affect human health. WHO estimates that roughly 7 million premature deaths are attributable to the joint effects of air pollution.

    “We’ve had all these health-motivated reductions of aerosol and ozone precursor emissions. What are the implications for the climate system, both locally but also around the globe? How does air quality respond to climate change? We study these two-way interactions between air pollution and the climate system,” says Fiore.

    But fundamental science is still required to understand how gases, such as ozone and nitrogen dioxide, linger and move throughout the troposphere — the lowermost layer of our atmosphere, containing the air we breathe.

    “We care about ozone in the air we’re breathing where we live at the Earth’s surface,” says Fiore. “Ozone reacts with biological tissue, and can be damaging to plants and human lungs. Even if you’re a healthy adult, if you’re out running hard during an ozone smog event, you might feel an extra weight on your lungs.”

    Telltale signs from space

    Ozone is not emitted directly, but instead forms through chemical reactions catalyzed by radiation from the sun interacting with nitrogen oxides — pollutants released in large part from burning fossil fuels—and volatile organic compounds. However, current satellite instruments cannot sense ground-level ozone.

    “We can’t retrieve surface- or even near-surface ozone from space,” says Fiore of the satellite data, “although the anticipated launch of a new instrument looks promising for new advances in retrieving lower-tropospheric ozone”. Instead, scientists can look at signatures from other gas emissions to get a sense of ozone formation. “Nitrogen dioxide and formaldehyde are a heavy focus of our research because they serve as proxies for two of the key ingredients that go on to form ozone in the atmosphere.”

    To understand ozone formation via these precursor pollutants, scientists have gathered data for more than two decades using spectrometer instruments aboard satellites that measure sunlight in ultraviolet and visible wavelengths that interact with these pollutants in the Earth’s atmosphere — known as solar backscatter radiation.

    Satellites, such as NASA’s Aura, carry instruments like the Ozone Monitoring Instrument (OMI). OMI, along with European-launched satellites such as the Global Ozone Monitoring Experiment (GOME) and the Scanning Imaging Absorption spectroMeter for Atmospheric CartograpHY (SCIAMACHY), and the newest generation TROPOspheric Monitoring instrument (TROPOMI), all orbit the Earth, collecting data during daylight hours when sunlight is interacting with the atmosphere over a particular location.

    In a recent paper from Fiore’s group, former graduate student Xiaomeng Jin (now a postdoc at the University of California at Berkeley), demonstrated that she could bring together and “beat down the noise in the data,” as Fiore says, to identify trends in ozone formation chemistry over several U.S. metropolitan areas that “are consistent with our on-the-ground understanding from in situ ozone measurements.”

    “This finding implies that we can use these records to learn about changes in surface ozone chemistry in places where we lack on-the-ground monitoring,” says Fiore. Extracting these signals by stringing together satellite data — OMI, GOME, and SCIAMACHY — to produce a two-decade record required reconciling the instruments’ differing orbit days, times, and fields of view on the ground, or spatial resolutions. 

    Currently, spectrometer instruments aboard satellites are retrieving data once per day. However, newer instruments, such as the Geostationary Environment Monitoring Spectrometer launched in February 2020 by the National Institute of Environmental Research in the Ministry of Environment of South Korea, will monitor a particular region continuously, providing much more data in real time.

    Over North America, the Tropospheric Emissions: Monitoring of Pollution Search (TEMPO) collaboration between NASA and the Smithsonian Astrophysical Observatory, led by Kelly Chance of Harvard University, will provide not only a stationary view of the atmospheric chemistry over the continent, but also a finer-resolution view — with the instrument recording pollution data from only a few square miles per pixel (with an anticipated launch in 2022).

    “What we’re very excited about is the opportunity to have continuous coverage where we get hourly measurements that allow us to follow pollution from morning rush hour through the course of the day and see how plumes of pollution are evolving in real time,” says Fiore.

    Data for the people

    Providing Earth-observing data to people in addition to scientists — namely environmental managers, city planners, and other government officials — is the goal for the NASA Health and Air Quality Applied Sciences Team (HAQAST).

    Since 2016, Fiore has been part of HAQAST, including collaborative “tiger teams” — projects that bring together scientists, nongovernment entities, and government officials — to bring data to bear on real issues.

    For example, in 2017, Fiore led a tiger team that provided guidance to state air management agencies on how satellite data can be incorporated into state implementation plans (SIPs). “Submission of a SIP is required for any state with a region in non-attainment of U.S. National Ambient Air Quality Standards to demonstrate their approach to achieving compliance with the standard,” says Fiore. “What we found is that small tweaks in, for example, the metrics we use to convey the science findings, can go a long way to making the science more usable, especially when there are detailed policy frameworks in place that must be followed.”

    Now, in 2021, Fiore is part of two tiger teams announced by HAQAST in late September. One team is looking at data to address environmental justice issues, by providing data to assess communities disproportionately affected by environmental health risks. Such information can be used to estimate the benefits of governmental investments in environmental improvements for disproportionately burdened communities. The other team is looking at urban emissions of nitrogen oxides to try to better quantify and communicate uncertainties in the estimates of anthropogenic sources of pollution.

    “For our HAQAST work, we’re looking at not just the estimate of the exposure to air pollutants, or in other words their concentrations,” says Fiore, “but how confident are we in our exposure estimates, which in turn affect our understanding of the public health burden due to exposure. We have stakeholder partners at the New York Department of Health who will pair exposure datasets with health data to help prioritize decisions around public health.

    “I enjoy working with stakeholders who have questions that require science to answer and can make a difference in their decisions.” Fiore says. More