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    J-PAL North America and Results for America announce 18 collaborations with state and local governments

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    J-PAL North America and Results for America have announced 18 new partnerships with state and local governments across the country through their Leveraging Evidence and Evaluation for Equitable Recovery (LEVER) programming, which launched in April of this year. 

    As state and local leaders leverage federal relief funding to invest in their communities, J-PAL North America and Results for America are providing in-depth support to agencies in using data, evaluation, and evidence to advance effective and equitable government programming for generations to come. The 18 new collaborators span the contiguous United States and represent a wide range of pressing and innovative uses of federal Covid-19 recovery funding.

    These partnerships are a key component of the LEVER program, run by J-PAL North America — a regional office of MIT’s Abdul Latif Jameel Poverty Action Lab (J-PAL) — and Results for America — a nonprofit organization that helps government agencies harness the power of evidence and data. Through 2024, LEVER will continue to provide a suite of resources, training, and evaluation design services to prepare state and local government agencies to rigorously evaluate their own programs and to harness existing evidence in developing programs and policies using federal recovery dollars.

    J-PAL North America is working with four leading government agencies following a call for proposals to the LEVER Evaluation Incubator in June. These agencies will work with J-PAL staff to design randomized evaluations to understand the causal impact of important programs that contribute to their jurisdictions’ recovery from Covid-19.

    Connecticut’s Medicaid office, operating out of the state’s Department of Social Services, is working to improve vaccine access and awareness among youth. “Connecticut Medicaid is thrilled to work with J-PAL North America. The technical expertise and training that we receive will expand our knowledge during ‘testing and learning’ interventions that improve the health of our members,” says Gui Woolston, the director of Medicaid and Division of Health Services. 

    Athens-Clarke County Unified Government is invested in evaluating programming for youth development and violence prevention implemented by the Boys and Girls Club of Athens. Their goal is “to measure and transparently communicate program impact,” explains Paige Seago, the data and outcomes coordinator for the American Rescue Plan Act. “The ability to continually iterate and tailor programs to better meet community goals is crucial to long-term success.”

    The County of San Diego’s newly formed Office of Evaluation, Performance, and Analytics is evaluating a pilot program providing rental subsidies for older adults. “Randomized evaluation can help us understand if rent subsidies will help prevent seniors from becoming homeless and will give us useful information about how to move forward,” says Chief Evaluation Officer Ricardo Basurto-Dávila. 

    In King County, Washington, the Executive Climate Office is planning to evaluate efforts to increase equitable access to household energy efficiency programs. “Because of J-PAL’s support, we have confidence that we can reduce climate impacts and extend home electrification benefits to lower-income homeowners in King County — homeowners who otherwise may not have the ability to participate in the clean energy transition,” says King County Climate Director Marissa Aho.

    Fourteen additional state and local agencies are working with Results for America as part of the LEVER Training Sprint. Together, they will develop policies that catalyze sustainable evidence building within government. 

    Jurisdictions selected for the Training Sprint represent government leaders at the city, county, and state levels — all of whom are committed to creating an evaluation framework for policy that will prioritize evidence-based decision-making across the country. Over the course of 10 weeks, with access to tools and coaching, each team will develop an internal implementation policy by embedding key evaluation and evidence practices into their jurisdiction’s decision-making processes. Participants will finish the Training Sprint with a robust decision-making framework that translates their LEVER implementation policies into actionable planning guidance. 

    Government leaders will utilize the LEVER Training Sprint to build a culture of data and evidence focused on leveraging evaluation policies to invest in delivering tangible results for their residents. About their participation in the LEVER Training Sprint, Dana Williams from Denver, Colorado says, “Impact evaluation is such an integral piece to understanding the past, present, and future. I’m excited to participate in the LEVER Training Sprint to better inform and drive evidence-based programming in Denver.”

    The Training Sprint is a part of a growing movement to ground government innovation in data and evidence. Kermina Hanna from the State of New Jersey notes, “It’s vital that we cement a data-driven commitment to equity in government operations, and I’m really excited for this opportunity to develop a national network of colleagues in government who share this passion and dedication to responsive public service.”

    Jurisdictions selected for the Training Sprint are: 

    Boston, Massachusetts;
    Carlsbad, California;
    Connecticut;
    Dallas, Texas;
    Denver City/County, Colorado;
    Fort Collins, Colorado;
    Guilford County, North Carolina;
    King County, Washington;
    Long Beach, California;
    Los Angeles, California;
    New Jersey;
    New Mexico;
    Pittsburgh, Pennsylvania; and
    Washington County, Oregon.
    Those interested in learning more can fill out the LEVER intake form. Please direct any questions about the Evaluation Incubator to Louise Geraghty and questions about the Training Sprint to Chelsea Powell. More

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      Supporting sustainability, digital health, and the future of work

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      The MIT and Accenture Convergence Initiative for Industry and Technology has selected three new research projects that will receive support from the initiative. The research projects aim to accelerate progress in meeting complex societal needs through new business convergence insights in technology and innovation.

      Established in MIT’s School of Engineering and now in its third year, the MIT and Accenture Convergence Initiative is furthering its mission to bring together technological experts from across business and academia to share insights and learn from one another. Recently, Thomas W. Malone, the Patrick J. McGovern (1959) Professor of Management, joined the initiative as its first-ever faculty lead. The research projects relate to three of the initiative’s key focus areas: sustainability, digital health, and the future of work.

      “The solutions these research teams are developing have the potential to have tremendous impact,” says Anantha Chandrakasan, dean of the School of Engineering and the Vannevar Bush Professor of Electrical Engineering and Computer Science. “They embody the initiative’s focus on advancing data-driven research that addresses technology and industry convergence.”

      “The convergence of science and technology driven by advancements in generative AI, digital twins, quantum computing, and other technologies makes this an especially exciting time for Accenture and MIT to be undertaking this joint research,” says Kenneth Munie, senior managing director at Accenture Strategy, Life Sciences. “Our three new research projects focusing on sustainability, digital health, and the future of work have the potential to help guide and shape future innovations that will benefit the way we work and live.”

      The MIT and Accenture Convergence Initiative charter project researchers are described below.

      Accelerating the journey to net zero with industrial clusters

      Jessika Trancik is a professor at the Institute for Data, Systems, and Society (IDSS). Trancik’s research examines the dynamic costs, performance, and environmental impacts of energy systems to inform climate policy and accelerate beneficial and equitable technology innovation. Trancik’s project aims to identify how industrial clusters can enable companies to derive greater value from decarbonization, potentially making companies more willing to invest in the clean energy transition.

      To meet the ambitious climate goals that have been set by countries around the world, rising greenhouse gas emissions trends must be rapidly reversed. Industrial clusters — geographically co-located or otherwise-aligned groups of companies representing one or more industries — account for a significant portion of greenhouse gas emissions globally. With major energy consumers “clustered” in proximity, industrial clusters provide a potential platform to scale low-carbon solutions by enabling the aggregation of demand and the coordinated investment in physical energy supply infrastructure.

      In addition to Trancik, the research team working on this project will include Aliza Khurram, a postdoc in IDSS; Micah Ziegler, an IDSS research scientist; Melissa Stark, global energy transition services lead at Accenture; Laura Sanderfer, strategy consulting manager at Accenture; and Maria De Miguel, strategy senior analyst at Accenture.

      Eliminating childhood obesity

      Anette “Peko” Hosoi is the Neil and Jane Pappalardo Professor of Mechanical Engineering. A common theme in her work is the fundamental study of shape, kinematic, and rheological optimization of biological systems with applications to the emergent field of soft robotics. Her project will use both data from existing studies and synthetic data to create a return-on-investment (ROI) calculator for childhood obesity interventions so that companies can identify earlier returns on their investment beyond reduced health-care costs.

      Childhood obesity is too prevalent to be solved by a single company, industry, drug, application, or program. In addition to the physical and emotional impact on children, society bears a cost through excess health care spending, lost workforce productivity, poor school performance, and increased family trauma. Meaningful solutions require multiple organizations, representing different parts of society, working together with a common understanding of the problem, the economic benefits, and the return on investment. ROI is particularly difficult to defend for any single organization because investment and return can be separated by many years and involve asymmetric investments, returns, and allocation of risk. Hosoi’s project will consider the incentives for a particular entity to invest in programs in order to reduce childhood obesity.

      Hosoi will be joined by graduate students Pragya Neupane and Rachael Kha, both of IDSS, as well a team from Accenture that includes Kenneth Munie, senior managing director at Accenture Strategy, Life Sciences; Kaveh Safavi, senior managing director in Accenture Health Industry; and Elizabeth Naik, global health and public service research lead.

      Generating innovative organizational configurations and algorithms for dealing with the problem of post-pandemic employment

      Thomas Malone is the Patrick J. McGovern (1959) Professor of Management at the MIT Sloan School of Management and the founding director of the MIT Center for Collective Intelligence. His research focuses on how new organizations can be designed to take advantage of the possibilities provided by information technology. Malone will be joined in this project by John Horton, the Richard S. Leghorn (1939) Career Development Professor at the MIT Sloan School of Management, whose research focuses on the intersection of labor economics, market design, and information systems. Malone and Horton’s project will look to reshape the future of work with the help of lessons learned in the wake of the pandemic.

      The Covid-19 pandemic has been a major disrupter of work and employment, and it is not at all obvious how governments, businesses, and other organizations should manage the transition to a desirable state of employment as the pandemic recedes. Using natural language processing algorithms such as GPT-4, this project will look to identify new ways that companies can use AI to better match applicants to necessary jobs, create new types of jobs, assess skill training needed, and identify interventions to help include women and other groups whose employment was disproportionately affected by the pandemic.

      In addition to Malone and Horton, the research team will include Rob Laubacher, associate director and research scientist at the MIT Center for Collective Intelligence, and Kathleen Kennedy, executive director at the MIT Center for Collective Intelligence and senior director at MIT Horizon. The team will also include Nitu Nivedita, managing director of artificial intelligence at Accenture, and Thomas Hancock, data science senior manager at Accenture. More

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      Understanding viral justice

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

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

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

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

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

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

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

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

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

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      Study: Covid-19 has reduced diverse urban interactions

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      The Covid-19 pandemic has reduced how often urban residents intersect with people from different income brackets, according to a new study led by MIT researchers.

      Examining the movement of people in four U.S. cities before and after the onset of the pandemic, the study found a 15 to 30 percent decrease in the number of visits residents were making to areas that are socioeconomically different than their own. In turn, this has reduced people’s opportunities to interact with others from varied social and economic spheres.

      “Income diversity of urban encounters decreased during the pandemic, and not just in the lockdown stages,” says Takahiro Yabe, a postdoc at the Media Lab and co-author of a newly published paper detailing the study’s results. “It decreased in the long term as well, after mobility patterns recovered.”

      Indeed, the study found a large immediate dropoff in urban movement in the spring of 2020, when new policies temporarily shuttered many types of institutions and businesses in the U.S. and much of the world due to the emergence of the deadly Covid-19 virus. But even after such restrictions were lifted and the overall amount of urban movement approached prepandemic levels, movement patterns within cities have narrowed; people now visit fewer places.

      “We see that changes like working from home, less exploration, more online shopping, all these behaviors add up,” says Esteban Moro, a research scientist at MIT’s Sociotechnical Systems Research Center (SSRC) and another of the paper’s co-authors. “Working from home is amazing and shopping online is great, but we are not seeing each other at the rates we were before.”

      The paper, “Behavioral changes during the Covid-19 pandemic decreased income diversity of urban encounters,” appears in Nature Communications. The co-authors are Yabe; Bernardo García Bulle Bueno, a doctoral candidate at MIT’s Institute for Data, Systems, and Society (IDSS); Xiaowen Dong, an associate professor at Oxford University; Alex Pentland, professor of media arts and sciences at MIT and the Toshiba Professor at the Media Lab; and Moro, who is also an associate professor at the University Carlos III of Madrid.

      A decline in exploration

      To conduct the study, the researchers examined anonymized cellphone data from 1 million users over a three-year period, starting in early 2019, with data focused on four U.S. cities: Boston, Dallas, Los Angeles, and Seattle. The researchers recorded visits to 433,000 specific “point of interest” locations in those cities, corroborated in part with records from Infogroup’s U.S. Business Database, an annual census of company information.  

      The researchers used U.S. Census Bureau data to categorize the socioeconomic status of the people in the study, placing everyone into one of four income quartiles, based on the average income of the census block (a small area) in which they live. The scholars made the same income-level assessment for every census block in the four cities, then recorded instances in which someone spent from 10 minutes to four hours in a census block other than their own, to see how often people visited areas in different income quartiles. 

      Ultimately, the researchers found that by late 2021, the amount of urban movement overall was returning to prepandemic levels, but the scope of places residents were visiting had become more restricted.

      Among other things, people made many fewer visits to museums, leisure venues, transport sites, and coffee shops. Visits to grocery stores remained fairly constant — but people tend not to leave their socioeconomic circles for grocery shopping.

      “Early in the pandemic, people reduced their mobility radius significantly,” Yabe says. “By late 2021, that decrease flattened out, and the average dwell time people spent at places other than work and home recovered to prepandemic levels. What’s different is that exploration substantially decreased, around 5 to 10 percent. We also see less visitation to fun places.” He adds: “Museums are the most diverse places you can find, parks — they took the biggest hit during the pandemic. Places that are [more] segregated, like grocery stores, did not.”

      Overall, Moro notes, “When we explore less, we go to places that are less diverse.”

      Different cities, same pattern

      Because the study encompassed four cities with different types of policies about reopening public sites and businesses during the pandemic, the researchers could also evaluate what impact public health policies had on urban movement. But even in these different settings, the same phenomenon emerged, with a narrower range of mobility occurring by late 2021.

      “Despite the substantial differences in how cities dealt with Covid-19, the decrease in diversity and the behavioral changes were surprisingly similar across the four cities,” Yabe observes.

      The researchers emphasize that these changes in urban movement can have long-term societal effects. Prior research has shown a significant association between a diversity of social connections and greater economic success for people in lower-income groups. And while some interactions between people in different income quartiles might be brief and transactional, the evidence suggests that, on aggregate, other more substantial connections have also been reduced. Additionally, the scholars note, the narrowing of experience can also weaken civic ties and valuable political connections.

      “It’s creating an urban fabric that is actually more brittle, in the sense that we are less exposed to other people,” Moro says. “We don’t get to know other people in the city, and that is very important for policies and public opinion. We need to convince people that new policies and laws would be fair. And the only way to do that is to know other people’s needs. If we don’t see them around the city, that will be impossible.”

      At the same time, Yabe adds, “I think there is a lot we can do from a policy standpoint to bring people back to places that used to be a lot more diverse.” The researchers are currently developing further studies related to cultural and public institutions, as well and transportation issues, to try to evaluate urban connectivity in additional detail.

      “The quantity of our mobility has recovered,” Yabe says. “The quality has really changed, and we’re more segregated as a result.” More

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      Q&A: A fresh look at data science

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      As the leaders of a developing field, data scientists must often deal with a frustratingly slippery question: What is data science, precisely, and what is it good for?

      Alfred Spector is a visiting scholar in the MIT Department of Electrical Engineering and Computer Science (EECS), an influential developer of distributed computing systems and applications, and a successful tech executive with companies including IBM and Google. Along with three co-authors — Peter Norvig at Stanford University and Google, Chris Wiggins at Columbia University and The New York Times, and Jeannette M. Wing at Columbia — Spector recently published “Data Science in Context: Foundations, Challenges, Opportunities” (Cambridge University Press), which provides a broad, conversational overview of the wide-ranging field driving change in sectors ranging from health care to transportation to commerce to entertainment. 

      Here, Spector talks about data-driven life, what makes a good data scientist, and how his book came together during the height of the Covid-19 pandemic.

      Q: One of the most common buzzwords Americans hear is “data-driven,” but many might not know what that term is supposed to mean. Can you unpack it for us?

      A: Data-driven broadly refers to techniques or algorithms powered by data — they either provide insight or reach conclusions, say, a recommendation or a prediction. The algorithms power models which are increasingly woven into the fabric of science, commerce, and life, and they often provide excellent results. The list of their successes is really too long to even begin to list. However, one concern is that the proliferation of data makes it easy for us as students, scientists, or just members of the public to jump to erroneous conclusions. As just one example, our own confirmation biases make us prone to believing some data elements or insights “prove” something we already believe to be true. Additionally, we often tend to see causal relationships where the data only shows correlation. It might seem paradoxical, but data science makes critical reading and analysis of data all the more important.

      Q: What, to your mind, makes a good data scientist?

      A: [In talking to students and colleagues] I optimistically emphasize the power of data science and the importance of gaining the computational, statistical, and machine learning skills to apply it. But, I also remind students that we are obligated to solve problems well. In our book, Chris [Wiggins] paraphrases danah boyd, who says that a successful application of data science is not one that merely meets some technical goal, but one that actually improves lives. More specifically, I exhort practitioners to provide a real solution to problems, or else clearly identify what we are not solving so that people see the limitations of our work. We should be extremely clear so that we do not generate harmful results or lead others to erroneous conclusions. I also remind people that all of us, including scientists and engineers, are human and subject to the same human foibles as everyone else, such as various biases. 

      Q: You discuss Covid-19 in your book. While some short-range models for mortality were very accurate during the heart of the pandemic, you note the failure of long-range models to predict any of 2020’s four major geotemporal Covid waves in the United States. Do you feel Covid was a uniquely hard situation to model? 

      A: Covid was particularly difficult to predict over the long term because of many factors — the virus was changing, human behavior was changing, political entities changed their minds. Also, we didn’t have fine-grained mobility data (perhaps, for good reasons), and we lacked sufficient scientific understanding of the virus, particularly in the first year.

      I think there are many other domains which are similarly difficult. Our book teases out many reasons why data-driven models may not be applicable. Perhaps it’s too difficult to get or hold the necessary data. Perhaps the past doesn’t predict the future. If data models are being used in life-and-death situations, we may not be able to make them sufficiently dependable; this is particularly true as we’ve seen all the motivations that bad actors have to find vulnerabilities. So, as we continue to apply data science, we need to think through all the requirements we have, and the capability of the field to meet them. They often align, but not always. And, as data science seeks to solve problems into ever more important areas such as human health, education, transportation safety, etc., there will be many challenges.

      Q: Let’s talk about the power of good visualization. You mention the popular, early 2000’s Baby Name Voyager website as one that changed your view on the importance of data visualization. Tell us how that happened. 

      A: That website, recently reborn as the Name Grapher, had two characteristics that I thought were brilliant. First, it had a really natural interface, where you type the initial characters of a name and it shows a frequency graph of all the names beginning with those letters, and their popularity over time. Second, it’s so much better than a spreadsheet with 140 columns representing years and rows representing names, despite the fact it contains no extra information. It also provided instantaneous feedback with its display graph dynamically changing as you type. To me, this showed the power of a very simple transformation that is done correctly.

      Q: When you and your co-authors began planning “Data Science In Context,” what did you hope to offer?

      A: We portray present data science as a field that’s already had enormous benefits, that provides even more future opportunities, but one that requires equally enormous care in its use. Referencing the word “context” in the title, we explain that the proper use of data science must consider the specifics of the application, the laws and norms of the society in which the application is used, and even the time period of its deployment. And, importantly for an MIT audience, the practice of data science must go beyond just the data and the model to the careful consideration of an application’s objectives, its security, privacy, abuse, and resilience risks, and even the understandability it conveys to humans. Within this expansive notion of context, we finally explain that data scientists must also carefully consider ethical trade-offs and societal implications.

      Q: How did you keep focus throughout the process?

      A: Much like in open-source projects, I played both the coordinating author role and also the role of overall librarian of all the material, but we all made significant contributions. Chris Wiggins is very knowledgeable on the Belmont principles and applied ethics; he was the major contributor of those sections. Peter Norvig, as the coauthor of a bestselling AI textbook, was particularly involved in the sections on building models and causality. Jeannette Wing worked with me very closely on our seven-element Analysis Rubric and recognized that a checklist for data science practitioners would end up being one of our book’s most important contributions. 

      From a nuts-and-bolts perspective, we wrote the book during Covid, using one large shared Google doc with weekly video conferences. Amazingly enough, Chris, Jeannette, and I didn’t meet in person at all, and Peter and I met only once — sitting outdoors on a wooden bench on the Stanford campus.

      Q: That is an unusual way to write a book! Do you recommend it?

      A: It would be nice to have had more social interaction, but a shared document, at least with a coordinating author, worked pretty well for something up to this size. The benefit is that we always had a single, coherent textual base, not dissimilar to how a programming team works together.

      This is a condensed, edited version of a longer interview that originally appeared on the MIT EECS website. More

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      MIT Policy Hackathon produces new solutions for technology policy challenges

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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      Ad hoc committee releases report on remote teaching best practices for on-campus education

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      The Ad Hoc Committee on Leveraging Best Practices from Remote Teaching for On-Campus Education has released a report that captures how instructors are weaving lessons learned from remote teaching into in-person classes. Despite the challenges imposed by teaching and learning remotely during the Covid-19 pandemic, the report says, “there were seeds planted then that, we hope, will bear fruit in the coming years.”

      “In the long run, one of the best things about having lived through our remote learning experience may be the intense and broad focus on pedagogy that it necessitated,” the report continues. “In a moment when nobody could just teach the way they had always done before, all of us had to go back to first principles and ask ourselves: What are our learning goals for our students? How can we best help them to achieve these goals?”

      The committee’s work is a direct response to one of the Refinement and Implementation Committees (RIC) formed as part of Task Force 2021 and Beyond. Led by co-chairs Krishna Rajagopal, the William A. M. Burden Professor of Physics, and Janet Rankin, director of the MIT Teaching + Learning Lab, the committee engaged with faculty and instructional staff, associate department heads, and undergraduate and graduate officers across MIT.

      The findings are distilled into four broad themes:

      Community, Well-being, and Belonging. Conversations revealed new ways that instructors cultivated these key interrelated concepts, all of which are fundamental to student learning and success. Many instructors focused more on supporting well-being and building community and belonging during the height of the pandemic precisely because the MIT community, and everyone in it, was under such great stress. Some of the resulting practices are continuing, the committee found. Examples include introducing simple gestures, such as start-of-class welcoming practices, and providing extensions and greater flexibility on student assignments. Also, many across MIT felt that the week-long Thanksgiving break offered in 2020 should become a permanent fixture in the academic calendar, because it enhances the well-being of both students and instructors at a time in the fall semester when everyone’s batteries need recharging. 
      Enhancing Engagement. The committee found a variety of practices that have enhanced engagement between students and instructors; among students; and among instructors. For example, many instructors have continued to offer some office hours on Zoom, which seems to reduce barriers to participation for many students, while offering in-person office hours for those who want to take advantage of opportunities for more open-ended conversations. Several departments increased their usage of undergraduate teaching assistants (UTAs) in ways that make students’ learning experience more engaging and give the UTAs a real teaching experience. In addition, many instructors are leveraging out-of-class communication spaces like Slack, Perusall, and Piazza so students can work together, ask questions, and share ideas. 
      Enriching and Augmenting the Learning Environment. The report presents two ways in which instructors have enhanced learning within the classroom: through blended learning and by incorporating authentic experiences. Although blended learning techniques are not new at MIT, after having made it through remote teaching many faculty have found new ways to combine synchronous in-person teaching with asynchronous activities for on-campus students, such as pre-class or pre-lab sequences of videos with exercises interspersed, take-home lab kits, auto-graded online problems that give students immediate feedback, and recorded lab experiences for subsequent review. In addition, instructors found many creative ways to make students’ learning more authentic by going on virtual field trips, using Zoom to bring experts from around the world into MIT classrooms or to enable interactions with students at other universities, and live-streaming experiments that students could not otherwise experience since they cannot be performed in a teaching lab.   
       Assessing Learning. For all its challenges, the report notes, remote teaching prompted instructors to take a step back and think about what they wanted students to learn, how to support it, and how to measure it. The committee found a variety of examples of alternatives to traditional assessments, such as papers or timed, written exams, that instructors tried during the pandemic and are continuing to use. These alternatives include shorter, more frequent, lower-stakes assessments; oral exams or debates; asynchronous, open-book/notes exams; virtual poster sessions; alternate grading schemes; and uploading paper psets and exams into Gradescope to use its logistics and rubrics to improve grading effectiveness and efficiency.
      A large portion of the report is devoted to an extensive, annotated list of best practices from remote instruction that are being used in the classroom. Interestingly, Rankin says, “so many of the strategies and practices developed and used during the pandemic are based on, and supported by, solid educational research.”

      The report concludes with one broad recommendation: that all faculty and instructors read the findings and experiment with some of the best practices in their own instruction. “Our hope is that the practices shared in the report will continue to be adopted, adapted, and expanded by members of the teaching community at MIT, and that instructors’ openness in sharing and learning from each will continue,” Rankin says.

      Two additional, specific recommendations are included in the report. First, the committee endorses the RIC 16 recommendation that a Classroom Advisory Board be created to provide strategic input grounded in evolving pedagogy about future classroom use and technology needs. In its conversations, the committee found a number of ways that remote teaching and learning have impacted students’ and instructors’ perceptions as they have returned to the classroom. For example, during the pandemic students benefited from being able to see everyone else’s faces on Zoom. As a result, some instructors would prefer classrooms that enable students to face each other, such as semi-circular classrooms instead of rectangular ones.

      More generally, the committee concluded, MIT needs classrooms with seats and tables that can be quickly and flexibly reconfigured to facilitate varying pedagogical objectives. The Classroom Advisory Board could also examine classroom technology; this includes the role of videoconferencing to create authentic engagement between MIT students and people far from campus, and blended learning that allows students to experience more of the in-classroom engagement with their peers and instructors from which the “magic of MIT” originates.

      Second, the committee recommends that an implementation group be formed to investigate the possibility of changing the MIT academic calendar to create a one-week break over Thanksgiving. “Finalizing an implementation plan will require careful consideration of various significant logistical challenges,” the report says. “However, the resulting gains to both well-being and learning from this change to the fall calendar make doing so worthwhile.”

      Rankin notes that the report findings dovetail with the recently released MIT Strategic Action Plan for Belonging, Achievement and Composition. “I believe that one of the most important things that became really apparent during remote teaching was that community, inclusion, and belonging really matter and are necessary for both learning and teaching, and that instructors can and should play a central role in creating structures and processes to support them in their classrooms and other learning environments,” she says.

      Rajagopal finds it inspiring that “during a time of intense stress — that nobody ever wants to relive — there was such an intense focus on how we teach and how our students learn that, today, in essentially every direction we look we see colleagues improving on-campus education for tomorrow. I hope that the report will help instructors across the Institute, and perhaps elsewhere, learn from each other. Its readers will see, as our committee did, new ways in which students and instructors are finding those moments, those interactions, where the magic of MIT is created.”

      In addition to the report, the co-chairs recommend two other valuable remote teaching resources: a video interview series, TLL’s Fresh Perspectives, and Open Learning’s collection of examples of how MIT faculty and instructors leveraged digital technology to support and transform teaching and learning during the heart of the pandemic. More

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      Companies use MIT research to identify and respond to supply chain risks

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      In February 2020, MIT professor David Simchi-Levi predicted the future. In an article in Harvard Business Review, he and his colleague warned that the new coronavirus outbreak would throttle supply chains and shutter tens of thousands of businesses across North America and Europe by mid-March.

      For Simchi-Levi, who had developed new models of supply chain resiliency and advised major companies on how to best shield themselves from supply chain woes, the signs of disruption were plain to see. Two years later, the professor of engineering systems at the MIT Schwarzman College of Computing and the Department of Civil and Environmental Engineering, and director of the MIT Data Science Lab has found a “flood of interest” from companies anxious to apply his Risk Exposure Index (REI) research to identify and respond to hidden risks in their own supply chains.

      His work on “stress tests” for critical supply chains and ways to guide global supply chain recovery were included in the 2022 Economic Report of the President presented to the U.S. Congress in April.

      It is rare that data science research can influence policy at the highest levels, Simchi-Levi says, but his models reflect something that business needs now: a new world of continuing global crisis, without relying on historical precedent.

      “What the last two years showed is that you cannot plan just based on what happened last year or the last two years,” Simchi-Levi says.

      He recalled the famous quote, sometimes attributed to hockey great Wayne Gretzsky, that good players don’t skate to where the puck is, but where the puck is going to be. “We are not focusing on the state of the supply chain right now, but what may happen six weeks from now, eight weeks from now, to prepare ourselves today to prevent the problems of the future.”

      Finding hidden risks

      At the heart of REI is a mathematical model of the supply chain that focuses on potential failures at different supply chain nodes — a flood at a supplier’s factory, or a shortage of raw materials at another factory, for instance. By calculating variables such as “time-to-recover” (TTR), which measures how long it will take a particular node to be back at full function, and time-to-survive (TTS), which identifies the maximum duration that the supply chain can match supply with demand after a disruption, the model focuses on the impact of disruption on the supply chain, rather than the cause of disruption.

      Even before the pandemic, catastrophic events such as the 2010 Iceland volcanic eruption and the 2011 Tohoku earthquake and tsunami in Japan were threatening these nodes. “For many years, companies from a variety of industries focused mostly on efficiency, cutting costs as much as possible, using strategies like outsourcing and offshoring,” Simchi-Levi says. “They were very successful doing this, but it has dramatically increased their exposure to risk.”

      Using their model, Simchi-Levi and colleagues began working with Ford Motor Company in 2013 to improve the company’s supply chain resiliency. The partnership uncovered some surprising hidden risks.

      To begin with, the researchers found out that Ford’s “strategic suppliers” — the nodes of the supply chain where the company spent large amount of money each year — had only moderate exposure to risk. Instead, the biggest risk “tended to come from tiny suppliers that provide Ford with components that cost about 10 cents,” says Simchi-Levi.

      The analysis also found that risky suppliers are everywhere across the globe. “There is this idea that if you just move suppliers closer to market, to demand, to North America or to Mexico, you increase the resiliency of your supply chain. That is not supported by our data,” he says.

      Rewards of resiliency

      By creating a virtual representation, or “digital twin,” of the Ford supply chain, the researchers were able to test out strategies at each node to see what would increase supply chain resiliency. Should the company invest in more warehouses to store a key component? Should it shift production of a component to another factory?

      Companies are sometimes reluctant to invest in supply chain resiliency, Simchi-Levi says, but the analysis isn’t just about risk. “It’s also going to help you identify savings opportunities. The company may be building a lot of misplaced, costly inventory, for instance, and our method helps them to identify these inefficiencies and cut costs.”

      Since working with Ford, Simchi-Levi and colleagues have collaborated with many other companies, including a partnership with Accenture, to scale the REI technology to a variety of industries including high-tech, industrial equipment, home improvement retailers, fashion retailers, and consumer packaged goods.

      Annette Clayton, the CEO of Schneider Electric North America and previously its chief supply chain officer, has worked with Simchi-Levi for 17 years. “When I first went to work for Schneider, I asked David and his team to help us look at resiliency and inventory positioning in order to make the best cost, delivery, flexibility, and speed trade-offs for the North American supply chain,” she says. “As the pandemic unfolded, the very learnings in supply chain resiliency we had worked on before became even more important and we partnered with David and his team again,”

      “We have used TTR and TTS to determine places where we need to develop and duplicate supplier capability, from raw materials to assembled parts. We increased inventories where our time-to-recover because of extended logistics times exceeded our time-to-survive,” Clayton adds. “We have used TTR and TTS to prioritize our workload in supplier development, procurement and expanding our own manufacturing capacity.”

      The REI approach can even be applied to an entire country’s economy, as the U.N. Office for Disaster Risk Reduction has done for developing countries such as Thailand in the wake of disastrous flooding in 2011.

      Simchi-Levi and colleagues have been motivated by the pandemic to enhance the REI model with new features. “Because we have started collaborating with more companies, we have realized some interesting, company-specific business constraints,” he says, which are leading to more efficient ways of calculating hidden risk. More

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      in Data Management & Statistics

      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