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    Exact symbolic artificial intelligence for faster, better assessment of AI fairness

    The justice system, banks, and private companies use algorithms to make decisions that have profound impacts on people’s lives. Unfortunately, those algorithms are sometimes biased — disproportionately impacting people of color as well as individuals in lower income classes when they apply for loans or jobs, or even when courts decide what bail should be set while a person awaits trial.

    MIT researchers have developed a new artificial intelligence programming language that can assess the fairness of algorithms more exactly, and more quickly, than available alternatives.

    Their Sum-Product Probabilistic Language (SPPL) is a probabilistic programming system. Probabilistic programming is an emerging field at the intersection of programming languages and artificial intelligence that aims to make AI systems much easier to develop, with early successes in computer vision, common-sense data cleaning, and automated data modeling. Probabilistic programming languages make it much easier for programmers to define probabilistic models and carry out probabilistic inference — that is, work backward to infer probable explanations for observed data.

    “There are previous systems that can solve various fairness questions. Our system is not the first; but because our system is specialized and optimized for a certain class of models, it can deliver solutions thousands of times faster,” says Feras Saad, a PhD student in electrical engineering and computer science (EECS) and first author on a recent paper describing the work. Saad adds that the speedups are not insignificant: The system can be up to 3,000 times faster than previous approaches.

    SPPL gives fast, exact solutions to probabilistic inference questions such as “How likely is the model to recommend a loan to someone over age 40?” or “Generate 1,000 synthetic loan applicants, all under age 30, whose loans will be approved.” These inference results are based on SPPL programs that encode probabilistic models of what kinds of applicants are likely, a priori, and also how to classify them. Fairness questions that SPPL can answer include “Is there a difference between the probability of recommending a loan to an immigrant and nonimmigrant applicant with the same socioeconomic status?” or “What’s the probability of a hire, given that the candidate is qualified for the job and from an underrepresented group?”

    SPPL is different from most probabilistic programming languages, as SPPL only allows users to write probabilistic programs for which it can automatically deliver exact probabilistic inference results. SPPL also makes it possible for users to check how fast inference will be, and therefore avoid writing slow programs. In contrast, other probabilistic programming languages such as Gen and Pyro allow users to write down probabilistic programs where the only known ways to do inference are approximate — that is, the results include errors whose nature and magnitude can be hard to characterize.

    Error from approximate probabilistic inference is tolerable in many AI applications. But it is undesirable to have inference errors corrupting results in socially impactful applications of AI, such as automated decision-making, and especially in fairness analysis.

    Jean-Baptiste Tristan, associate professor at Boston College and former research scientist at Oracle Labs, who was not involved in the new research, says, “I’ve worked on fairness analysis in academia and in real-world, large-scale industry settings. SPPL offers improved flexibility and trustworthiness over other PPLs on this challenging and important class of problems due to the expressiveness of the language, its precise and simple semantics, and the speed and soundness of the exact symbolic inference engine.”

    SPPL avoids errors by restricting to a carefully designed class of models that still includes a broad class of AI algorithms, including the decision tree classifiers that are widely used for algorithmic decision-making. SPPL works by compiling probabilistic programs into a specialized data structure called a “sum-product expression.” SPPL further builds on the emerging theme of using probabilistic circuits as a representation that enables efficient probabilistic inference. This approach extends prior work on sum-product networks to models and queries expressed via a probabilistic programming language. However, Saad notes that this approach comes with limitations: “SPPL is substantially faster for analyzing the fairness of a decision tree, for example, but it can’t analyze models like neural networks. Other systems can analyze both neural networks and decision trees, but they tend to be slower and give inexact answers.”

    “SPPL shows that exact probabilistic inference is practical, not just theoretically possible, for a broad class of probabilistic programs,” says Vikash Mansinghka, an MIT principal research scientist and senior author on the paper. “In my lab, we’ve seen symbolic inference driving speed and accuracy improvements in other inference tasks that we previously approached via approximate Monte Carlo and deep learning algorithms. We’ve also been applying SPPL to probabilistic programs learned from real-world databases, to quantify the probability of rare events, generate synthetic proxy data given constraints, and automatically screen data for probable anomalies.”

    The new SPPL probabilistic programming language was presented in June at the ACM SIGPLAN International Conference on Programming Language Design and Implementation (PLDI), in a paper that Saad co-authored with MIT EECS Professor Martin Rinard and Mansinghka. SPPL is implemented in Python and is available open source. More

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    Finding common ground in Malden

    When disparate groups convene around a common goal, exciting things can happen.

    That is the inspiring story unfolding in Malden, Massachusetts, a city of about 60,000 — nearly half people of color — where a new type of community coalition continues to gain momentum on its plan to build a climate-resilient waterfront park along its river. The Malden River Works (MRW) project, recipient of the inaugural Leventhal City Prize, is seeking to connect to a contiguous greenway network where neighboring cities already have visitors coming to their parks and enjoying recreational boating. More important, the MRW is changing the model for how cities address civic growth, community engagement, equitable climate resilience, and environmental justice.                                                                                        

    The MRW’s steering committee consists of eight resident leaders of color, a resident environmental advocate, and three city representatives. One of the committee’s primary responsibilities is providing direction to the MRW’s project team, which includes urban designers, watershed and climate resilience planners, and a community outreach specialist. MIT’s Kathleen Vandiver, director of the Community Outreach Education and Engagement Core at MIT’s Center for Environmental Health Sciences (CEHS), and Marie Law Adams MArch ’06, a lecturer in the School of Architecture and Planning’s Department of Urban Studies and Planning (DUSP), serve on the project team.

    “This governance structure is somewhat unusual,” says Adams. “More typical is having city government as the primary decision-maker. It is important that one of the first things our team did was build a steering committee that is the decision maker on this project.”

    Evan Spetrini ’18 is the senior planner and policy manager for the Malden Redevelopment Authority and sits on both the steering committee and project team. He says placing the decision-making power with the steering committee and building it to be representative of marginalized communities was intentional. 

    “Changing that paradigm of power and decision-making in planning processes was the way we approached social resilience,” says Spetrini. “We have always intended this project to be a model for future planning projects in Malden.”

    This model ushers in a new history chapter for a city founded in 1640.

    Located about six miles north of Boston, Malden was home to mills and factories that used the Malden River for power, and a site for industrial waste over the last two centuries. Decades after the city’s industrial decline, there is little to no public access to the river. Many residents were not even aware there was a river in their city. Before the project was under way, Vandiver initiated a collaborative effort to evaluate the quality of the river’s water. Working with the Mystic River Watershed Association, Gradient Corporation, and CEHS, water samples were tested and a risk analysis conducted.

    “Having the study done made it clear the public could safely enjoy boating on the water,” says Vandiver. “It was a breakthrough that allowed people to see the river as an amenity.”

    A team effort

    Marcia Manong had never seen the river, but the Malden resident was persuaded to join the steering committee with the promise the project would be inclusive and of value to the community. Manong has been involved with civic engagement most of her life in the United States and for 20 years in South Africa.

    “It wasn’t going to be a marginalized, token-ized engagement,” says Manong. “It was clear to me that they were looking for people that would actually be sitting at the table.”

    Manong agreed to recruit additional people of color to join the team. From the beginning, she says, language was a huge barrier, given that nearly half of Malden’s residents do not speak English at home. Finding the translation efforts at their public events to be inadequate, the steering committee directed more funds to be made available for translation in several languages when public meetings began being held over Zoom this past year.

    “It’s unusual for most cities to spend this money, but our population is so diverse that we require it,” says Manong. “We have to do it. If the steering committee wasn’t raising this issue with the rest of the team, perhaps this would be overlooked.”

    Another alteration the steering committee has made is how the project engages with the community. While public attendance at meetings had been successful before the pandemic, Manong says they are “constantly working” to reach new people. One method has been to request invitations to attend the virtual meetings of other organizations to keep them apprised of the project.

    “We’ve said that people feel most comfortable when they’re in their own surroundings, so why not go where the people are instead of trying to get them to where we are,” says Manong.

    Buoyed by the $100,000 grant from MIT’s Norman B. Leventhal Center for Advanced Urbanism (LCAU) in 2019, the project team worked with Malden’s Department of Public Works, which is located along the river, to redesign its site and buildings and to study how to create a flood-resistant public open space as well as an elevated greenway path, connecting with other neighboring cities’ paths. The park’s plans also call for 75 new trees to reduce urban heat island effect, open lawn for gathering, and a dock for boating on the river.

    “The storm water infrastructure in these cities is old and isn’t going to be able to keep up with increased precipitation,” says Adams. “We’re looking for ways to store as much water as possible on the DPW site so we can hold it and release it more gradually into the river to avoid flooding.”

    The project along the 2.3-mile-long river continues to receive attention. Recently, the city of Malden was awarded a 2021 Accelerating Climate Resilience Grant of more than $50,000 from the state’s Metropolitan Area Planning Council and the Barr Foundation to support the project. Last fall, the project was awarded a $150,015 Municipal Vulnerability Preparedness Action Grant. Both awards are being directed to fund engineering work to refine the project’s design.

    “We — and in general, the planning profession — are striving to create more community empowerment in decision-making as to what happens to their community,” says Spetrini. “Putting the power in the community ensures that it’s actually responding to the needs of the community.”

    Contagious enthusiasm

    Manong says she’s happy she got involved with the project and believes the new governance structure is making a difference.

    “This project is definitely engaging with communities of color in a manner that is transformative and that is looking to build a long-lasting power dynamic built on trust,” she says. “It’s a new energized civic engagement and we’re making that happen. It’s very exciting.”

    Spetrini finds the challenge of creating an open space that’s publicly accessible and alongside an active work site professionally compelling.

    “There is a way to preserve the industrial employment base while also giving the public greater access to this natural resource,” he says. “It has real implications for other communities to follow this type of model.”

    Despite the pandemic this past year, enthusiasm for the project is palpable. For Spetrini, a Malden resident, it’s building “the first significant piece of what has been envisioned as the Malden River Greenway.” Adams sees the total project as a way to build social resilience as well as garnering community interest in climate resilience. For Vandiver, it’s the implications for improved community access.

    “From a health standpoint, everybody has learned from Covid-19 that the health aspects of walking in nature are really restorative,” says Vandiver. “Creating greater green space gives more attention to health issues. These are seemingly small side benefits, but they’re huge for mental health benefits.”

    Leventhal City Prize’s next cycle

    The Leventhal City Prize was established by the LCAU to catalyze innovative, interdisciplinary urban design, and planning approaches worldwide to improve both the environment and the quality of life for residents. Support for the LCAU was provided by the Muriel and Norman B. Leventhal Family Foundation and the Sherry and Alan Leventhal Family Foundation.

    “We’re thrilled with inaugural recipients of the award and the extensive work they’ve undertaken that is being held up as an exemplary model for others to learn from,” says Sarah Williams, LCAU director and a professor in DUSP. “Their work reflects the prize’s intent. We look forward to catalyzing these types of collaborative partnership in the next prize cycle.”

    Submissions for the next cycle of the Leventhal City Prize will open in early 2022.    More

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    Lockdowns reveal inequities in opportunities for walking activities

    Lockdowns saved lives during the global SARS-CoV-2 pandemic. But as much as they have slowed the spread of Covid-19, there have been some unintended consequences.

    New MIT research shows that lockdowns in 10 metropolitan areas throughout the United States led to a marked reduction in walking. These decreases were mostly seen among residents living in lower-income areas of the city, effectively reducing access to physical activity for minorities and people suffering from illnesses such as obesity and diabetes.

    “Walking is the cheapest, most accessible physical exercise that you can do,” says Esteban Moro, visiting research scientist in the MIT Connection Science Group and senior author on the Nature Communications paper published on June 16. “Places in which people have lower incomes, less park access, and more obesity prevalence were more affected by this walking reduction — which you can think of as another pandemic, the lack of access to affordable exercise.”

    The research focused on recreational versus utilitarian walking done by residents in the U.S. cities of New York, Los Angeles, Chicago, Boston, Miami, Dallas, San Francisco, Seattle, Philadelphia, and Washington D.C. (Utilitarian walking is defined as having a goal; for example, walking to the store or to public transportation. Recreational walking is a walk meant for leisure or exercise.)

    Comparing cellphone data from February 2020 to different time points throughout 2020 lockdowns, the researchers saw an average 70 percent decrease in the number of walks — which remained down by about 18 percent after loosened restrictions — a 50 percent decrease in distance walked, and a 72 percent decrease in utilitarian walking — which remained down by 39 percent even after restrictions were lifted.

    On their face, these findings may not be surprising. When people couldn’t leave their homes, they walked less. But digging deeper into the data yields troubling insights. For example, people in lower-income regions are more likely to rely on public transportation. Lockdowns cut back on those services, meaning fewer people walking to trains and buses.

    Another statistic showed that people in higher-income areas reduced their number of utilitarian walks but were able to replace some of the lost movement with recreational walks around their neighborhoods or in nearby parks.

    “People in higher-income areas generally not only have a park nearby, but also have jobs that give them a degree of flexibility. Jobs that permit them to take a break and walk,” says Moro. “People in the low-income regions often don’t have the ability, the opportunity or even the facilities to actually do this.”

    How it was done

    The researchers used de-identified mobile data obtained through a partnership within the company Cuebiq’s Data for Good COVID-19 Collaborative program. The completely anonymized dataset consisted of GPS locations gathered from smartphone accelerometers from users who opted into the program. Moro and his collaborators took these data and, using specifically designed algorithms, determined when people walked, for how long, and for what purpose. They compared this information from before the pandemic, at different points throughout lockdown, and at a point when most restrictions had been eased. They matched the GPS-identified locations of the smartphones with census data to understand income level and other demographics.

    To make sure their dataset was robust, they only used information from areas that could reasonably be considered pedestrian. The researchers also acknowledge that the dataset may be incomplete, considering people may have occasionally walked without their phones on them.

    Leisure versus utilitarian walks were separated according to distance and/or destination. Utilitarian walks are usually shorter and involve stops at destinations other than the starting point. Leisure walks are longer and usually happen closer to home or in dedicated outdoor spaces.

    For example, many of the walks recorded pre-Covid-19 were short and occurred at around 7 a.m. and between 3 and 5 p.m., which would indicate a walking commute. These bouts of walking were replaced on weekends by short walks around noon.

    The key takeaway is that most walking in cities occurs with the goal of getting to a place. If people don’t have the opportunity to walk to places they need to go, they will reduce their walking activity overall. But when provided opportunity and access, people can supplement utilitarian activity with leisure walking.

    What can be done about it

    Taking into account the public health implications of physical inactivity, the authors argue a reduction in access to walking should be considered a second pandemic and be addressed with the same rigor as the Covid-19 pandemic.

    They suggest several tactical urbanization strategies (defined as non-permanent but easily accessible measures) to increase safety and appeal for both utilitarian and recreational walkers. Many of these have already been implemented in various cities around the world to ease economic and other hardships of the pandemic. Sections of city streets have been closed off to cars on weekends or other non-busy times to allow for pedestrian walking areas. Restaurants have been given curb space to allow for outdoor dining.

    “But most of these pop-up pedestrian areas happen in downtown, where people are high-income and have easier access to more walking opportunities,” notes Moro.

    The same attention needs to be paid to lower-income areas, the researchers argue. This study’s data showed that people explored their own neighborhoods in a recreational way more during lockdown than pre-pandemic. Such wanderings, the researcher say, should be encouraged by making any large, multi-lane intersections safer to cross for the elderly, sick, or those with young children. And local parks, usually seen as places for running laps, should be made more attractive destinations by adding amenities like water fountains, shaded pavilions, and hygiene and sanitation spaces.

    This study was unique in that its data came straight from mobile devices, rather than being self-reported in surveys. This more reliable method of tracking made this study more data-driven than other, similar efforts. And the geotagged data allowed the researchers to dig into socioeconomic trends associated with the findings.

    This is the team’s first analysis of physical activity during and just after lockdown. They hope to use lessons learned from this and planned follow-ups to encourage more permanent adoption of pedestrian-friendly pandemic-era changes.

    The Connection Science Group, co-led by faculty member Alex “Sandy” Pentland — who, along with Moro was a co-author on the paper along with six others from the UK, Brazil, and Australia — is part of the MIT Sociotechnical Systems Research Center within the MIT Institute for Data, Systems, and Society. The collaborative research exemplified in this study is core to the mission of the SSRC; in pairing computer science with public health, the group not only observes trends but also contextualizes data and use them to make improvements for everyone.

    “SSRC merges both the social and technological components of the research,” says Moro. “We’re not only building an analysis, but going beyond that to propose new policies and interventions to change what we are seeing for the better.” More