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      Emma Gibson: Optimizing health care logistics in Africa

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      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

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      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

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      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

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      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

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      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

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      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

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      in Data Management & Statistics

      Data flow’s decisive role on the global stage

      by

      In 2016, Meicen Sun came to a profound realization: “The control of digital information will lie at the heart of all the big questions and big contentions in politics.” A graduate student in her final year of study who is specializing in international security and the political economy of technology, Sun vividly recalls the emergence of the internet “as a democratizing force, an opener, an equalizer,” helping give rise to the Arab Spring. But she was also profoundly struck when nations in the Middle East and elsewhere curbed internet access to throttle citizens’ efforts to speak and mobilize freely.

      During her undergraduate and graduate studies, which came to focus on China and its expanding global role, Sun became convinced that digital constraints initially intended to prevent the free flow of ideas were also having enormous and growing economic impacts.

      “With an exceptionally high mobile internet adoption rate and the explosion of indigenous digital apps, China’s digital economy was surging, helping to drive the nation’s broader economic growth and international competitiveness,” Sun says. “Yet at the same time, the country maintained the most tightly controlled internet ecosystem in the world.”

      Sun set out to explore this apparent paradox in her dissertation. Her research to date has yielded both novel findings and troubling questions.  

      “Through its control of the internet, China has in effect provided protectionist benefits to its own data-intensive domestic sectors,” she says. “If there is a benefit to imposing internet control, given the absence of effective international regulations, does this give authoritarian states an advantage in trade and national competitiveness?” Following this thread, Sun asks, “What might this mean for the future of democracy as the world grows increasingly dependent on digital technology?”

      Protect or innovate

      Early in her graduate program, classes in capitalism and technology and public policy, says Sun, “cemented for me the idea of data as a factor of production, and the importance of cross-border information flow in making a country innovative.” This central premise serves as a springboard for Sun’s doctoral studies.

      In a series of interconnected research papers using China as her primary case, she is examining the double-edged nature of internet limits. “They accord protectionist benefits to domestic data-internet-intensive sectors, on the one hand, but on the other, act as a potential longer-term deterrent to the country’s capacity to innovate.”

      To pursue her doctoral project, advised by professor of political science Kenneth Oye, Sun is extracting data from a multitude of sources, including a website that has been routinely testing web domain accessibility from within China since 2011. This allows her to pin down when and to what degree internet control occurs. She can then compare this information to publicly available records on the expansion or contraction of data-intensive industrial sectors, enabling her to correlate internet control to a sector’s performance.

      Sun has also compiled datasets for firm-level revenue, scientific citations, and patents that permit her to measure aspects of China’s innovation culture. In analyzing her data she leverages both quantitative and qualitative methods, including one co-developed by her dissertation co-advisor, associate professor of political science In Song Kim. Her initial analysis suggests internet control prevents scholars from accessing knowledge available on foreign websites, and that if sustained, such control could take a toll on the Chinese economy over time.

      Of particular concern is the possibility that the economic success that flows from strict internet controls, as exemplified by the Chinese model, may encourage the rise of similar practices among emerging states or those in political flux.

      “The grim implication of my research is that without international regulation on information flow restrictions, democracies will be at a disadvantage against autocracies,” she says. “No matter how short-term or narrow these curbs are, they confer concrete benefits on certain economic sectors.”

      Data, politics, and economy

      Sun got a quick start as a student of China and its role in the world. She was born in Xiamen, a coastal Chinese city across from Taiwan, to academic parents who cultivated her interest in international politics. “My dad would constantly talk to me about global affairs, and he was passionate about foreign policy,” says Sun.

      Eager for education and a broader view of the world, Sun took a scholarship at 15 to attend school in Singapore. “While this experience exposed me to a variety of new ideas and social customs, I felt the itch to travel even farther away, and to meet people with different backgrounds and viewpoints from mine,” than she says.

      Sun attended Princeton University where, after two years sticking to her “comfort zone” — writing and directing plays and composing music for them — she underwent a process of intellectual transition. Political science classes opened a window onto a larger landscape to which she had long been connected: China’s behavior as a rising power and the shifting global landscape.

      She completed her undergraduate degree in politics, and followed up with a master’s degree in international relations at the University of Pennsylvania, where she focused on China-U.S. relations and China’s participation in international institutions. She was on the path to completing a PhD at Penn when, Sun says, “I became confident in my perception that digital technology, and especially information sharing, were becoming critically important factors in international politics, and I felt a strong desire to devote my graduate studies, and even my career, to studying these topics,”

      Certain that the questions she hoped to pursue could best be addressed through an interdisciplinary approach with those working on similar issues, Sun began her doctoral program anew at MIT.

      “Doer mindset”

      Sun is hopeful that her doctoral research will prove useful to governments, policymakers, and business leaders. “There are a lot of developing states actively shopping between data governance and development models for their own countries,” she says. “My findings around the pros and cons of information flow restrictions should be of interest to leaders in these places, and to trade negotiators and others dealing with the global governance of data and what a fair playing field for digital trade would be.”

      Sun has engaged directly with policy and industry experts through her fellowships with the World Economic Forum and the Pacific Forum. And she has embraced questions that touch on policy outside of her immediate research: Sun is collaborating with her dissertation co-advisor, MIT Sloan Professor Yasheng Huang, on a study of the political economy of artificial intelligence in China for the MIT Task Force on the Work of the Future.

      This year, as she writes her dissertation papers, Sun will be based at Georgetown University, where she has a Mortara Center Global Political Economy Project Predoctoral Fellowship. In Washington, she will continue her journey to becoming a “policy-minded scholar, a thinker with a doer mindset, whose findings have bearing on things that happen in the world.” More

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      in Data Management & Statistics

      “To make even the smallest contribution to improving my country would be my dream”

      by

      Thailand has become an economic leader in Southeast Asia in recent decades, but while the country has rapidly industrialized, many Thai citizens have been left behind. As a child growing up in Bangkok, Pavarin Bhandtivej would watch the news and wonder why families in the nearby countryside had next to nothing. He aspired to become a policy researcher and create beneficial change.

      But Bhandtivej knew his goal wouldn’t be easy. He was born with a visual impairment, making it challenging for him to see, read, and navigate. This meant he had to work twice as hard in school to succeed. It took achieving the highest grades for Bhandtivej to break through stigmas and have his talents recognized. Still, he persevered, with a determination to uplift others. “I would return to that initial motivation I had as a kid. For me, to make even the smallest contribution to improving my country would be my dream,” he says.

      “When I would face these obstacles, I would tell myself that struggling people are waiting for someone to design policies for them to have better lives. And that person could be me. I cannot fall here in front of these obstacles. I must stay motivated and move on.”

      Bhandtivej completed his undergraduate degree in economics at Thailand’s top college, Chulalongkorn University. His classes introduced him to many debates about development policy, such as universal basic income. During one debate, after both sides made compelling arguments about how to alleviate poverty, Bhandtivej realized there was no clear winner. “A question came to my mind: Who’s right?” he says. “In terms of theory, both sides were correct. But how could we know what approach would work in the real world?”

      A new approach to higher education

      The search for those answers would lead Bhandtivej to become interested in data analysis. He began investigating online courses, eventually finding the MIT MicroMasters Program in Data, Economics, and Development Policy (DEDP), which was created by MIT’s Department of Economics and the Abdul Latif Jameel Poverty Action Lab (J-PAL). The program requires learners to complete five online courses that teach quantitative methods for evaluating social programs, leading to a MicroMasters credential. Students that pass the courses’ proctored exams are then also eligible to apply for a full-time, accelerated, on-campus master’s program at MIT, led by professors Esther Duflo, Abhijit Banerjee, and Benjamin Olken.

      The program’s mission to make higher education more accessible worked well for Bhandtivej. He studied tirelessly, listening and relistening to online lectures and pausing to scrutinize equations. By the end, his efforts paid off — Bhandtivej was the MicroMasters program’s top scorer. He was soon admitted into the second cohort of the highly selective DEDP master’s program.

      “You can imagine how time-consuming it was to use text-to-speech to get through a 30-page reading with numerous equations, tables, and graphs,” he explains. “Luckily, Disability and Access Services provided accommodations to timed exams and I was able to push through.”   

      In the gap year before the master’s program began, Bhandtivej returned to Chulalongkorn University as a research assistant with Professor Thanyaporn Chankrajang. He began applying his newfound quantitative skills to study the impacts of climate change in Thailand. His contributions helped uncover how rising temperatures and irregular rainfall are leading to reduced rice crop yields. “Thailand is the world’s second largest exporter of rice, and the vast majority of Thais rely heavily on rice for its nutritional and commercial value. We need more data to encourage leaders to act now,” says Bhandtivej. “As a Buddhist, it was meaningful to be part of generating this evidence, as I am always concerned about my impact on other humans and sentient beings.”

      Staying true to his mission

      Now pursuing his master’s on campus, Bhandtivej is taking courses like 14.320 (Econometric Data Science) and studying how to design, conduct, and analyze empirical studies. “The professors I’ve had have opened a whole new world for me,” says Bhandtivej. “They’ve inspired me to see how we can take rigorous scientific practices and apply them to make informed policy decisions. We can do more than rely on theories.”

      The final portion of the program requires a summer capstone experience, which Bhandtivej is using to work at Innovations for Poverty Action. He has recently begun to analyze how remote learning interventions in Bangladesh have performed since Covid-19. Many teachers are concerned, since disruptions in childhood education can lead to intergenerational poverty. “We have tried interventions that connect students with teachers, provide discounted data packages, and send information on where to access adaptive learning technologies and other remote learning resources,” he says. “It will be interesting to see the results. This is a truly urgent topic, as I don’t believe Covid-19 will be the last pandemic of our lifetime.”

      Enhancing education has always been one of Bhandtivej’s priority interests. He sees education as the gateway that brings a person’s innate talent to light. “There is a misconception in many developing countries that disabled people cannot learn, which is untrue,” says Bhandtivej. “Education provides a critical signal to future employers and overall society that we can work and perform just as well, as long as we have appropriate accommodations.”

      In the future, Bhandtivej plans on returning to Thailand to continue his journey as a policy researcher. While he has many issues he would like to tackle, his true purpose still lies in doing work that makes a positive impact on people’s lives. “My hope is that my story encourages people to think of not only what they are capable of achieving themselves, but also what they can do for others.”

      “You may think you are just a small creature on a large planet. That you have just a tiny role to play. But I think — even if we are just a small part — whatever we can do to make life better for our communities, for our country, for our planet … it’s worth it.” More