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      Empowering Cambridge youth through data activism

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      For over 40 years, the Mayor’s Summer Youth Employment Program (MSYEP, or the Mayor’s Program) in Cambridge, Massachusetts, has been providing teenagers with their first work experience, but 2022 brought a new offering. Collaborating with MIT’s Personal Robots research group (PRG) and Responsible AI for Social Empowerment and Education (RAISE) this summer, MSYEP created a STEAM-focused learning site at the Institute. Eleven students joined the program to learn coding and programming skills through the lens of “Data Activism.”

      MSYEP’s partnership with MIT provides an opportunity for Cambridge high schoolers to gain exposure to more pathways for their future careers and education. The Mayor’s Program aims to respect students’ time and show the value of their work, so participants are compensated with an hourly wage as they learn workforce skills at MSYEP worksites. In conjunction with two ongoing research studies at MIT, PRG and RAISE developed the six-week Data Activism curriculum to equip students with critical-thinking skills so they feel prepared to utilize data science to challenge social injustice and empower their community.

      Rohan Kundargi, K-12 Community Outreach Administrator for MIT Office of Government and Community Relations (OGCR), says, “I see this as a model for a new type of partnership between MIT and Cambridge MSYEP. Specifically, an MIT research project that involves students from Cambridge getting paid to learn, research, and develop their own skills!”

      Cross-Cambridge collaboration

      Cambridge’s Office of Workforce Development initially contacted MIT OGCR about hosting a potential MSYEP worksite that taught Cambridge teens how to code. When Kundargi reached out to MIT pK-12 collaborators, MIT PRG’s graduate research assistant Raechel Walker proposed the Data Activism curriculum. Walker defines “data activism” as utilizing data, computing, and art to analyze how power operates in the world, challenge power, and empathize with people who are oppressed.

      Walker says, “I wanted students to feel empowered to incorporate their own expertise, talents, and interests into every activity. In order for students to fully embrace their academic abilities, they must remain comfortable with bringing their full selves into data activism.”

      As Kundargi and Walker recruited students for the Data Activism learning site, they wanted to make sure the cohort of students — the majority of whom are individuals of color — felt represented at MIT and felt they had the agency for their voice to be heard. “The pioneers in this field are people who look like them,” Walker says, speaking of well-known data activists Timnit Gebru, Rediet Abebe, and Joy Buolamwini.

      When the program began this summer, some of the students were not aware of the ways data science and artificial intelligence exacerbate systemic oppression in society, or some of the tools currently being used to mitigate those societal harms. As a result, Walker says, the students wanted to learn more about discriminatory design in every aspect of life. They were also interested in creating responsible machine learning algorithms and AI fairness metrics.

      A different side of STEAM

      The development and execution of the Data Activism curriculum contributed to Walker’s and postdoc Xiaoxue Du’s respective research at PRG. Walker is studying AI education, specifically creating and teaching data activism curricula for minoritized communities. Du’s research explores processes, assessments, and curriculum design that prepares educators to use, adapt, and integrate AI literacy curricula. Additionally, her research targets how to leverage more opportunities for students with diverse learning needs.

      The Data Activism curriculum utilizes a “libertatory computing” framework, a term Walker coined in her position paper with Professor Cynthia Breazeal, director of MIT RAISE, dean for digital learning, and head of PRG, and Eman Sherif, a then-undergraduate researcher from University of California at San Diego, titled “Liberty Computing for African American Students.” This framework ensures that students, especially minoritized students, acquire a sound racial identity, critical consciousness, collective obligation, liberation centered academic/achievement identity, as well as the activism skills to use computing to transform a multi-layered system of barriers in which racism persists. Walker says, “We encouraged students to demonstrate competency in every pillar because all of the pillars are interconnected and build upon each other.”

      Walker developed a series of interactive coding and project-based activities that focused on understanding systemic racism, utilizing data science to analyze systemic oppression, data drawing, responsible machine learning, how racism can be embedded into AI, and different AI fairness metrics.

      This was the students’ first time learning how to create data visualizations using the programming language Python and the data analysis tool Pandas. In one project meant to examine how different systems of oppression can affect different aspects of students’ own identities, students created datasets with data from their respective intersectional identities. Another activity highlighted African American achievements, where students analyzed two datasets about African American scientists, activists, artists, scholars, and athletes. Using the data visualizations, students then created zines about the African Americans who inspired them.

      RAISE hired Olivia Dias, Sophia Brady, Lina Henriquez, and Zeynep Yalcin through the MIT Undergraduate Research Opportunity Program (UROP) and PRG hired freelancer Matt Taylor to work with Walker on developing the curriculum and designing interdisciplinary experience projects. Walker and the four undergraduate researchers constructed an intersectional data analysis activity about different examples of systemic oppression. PRG also hired three high school students to test activities and offer insights about making the curriculum engaging for program participants. Throughout the program, the Data Activism team taught students in small groups, continually asked students how to improve each activity, and structured each lesson based on the students’ interests. Walker says Dias, Brady, Henriquez, and Yalcin were invaluable to cultivating a supportive classroom environment and helping students complete their projects.

      Cambridge Rindge and Latin School senior Nina works on her rubber block stamp that depicts the importance of representation in media and greater representation in the tech industry.

      Photo: Katherine Ouellette

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      Student Nina says, “It’s opened my eyes to a different side of STEM. I didn’t know what ‘data’ meant before this program, or how intersectionality can affect AI and data.” Before MSYEP, Nina took Intro to Computer Science and AP Computer Science, but she has been coding since Girls Who Code first sparked her interest in middle school. “The community was really nice. I could talk with other girls. I saw there needs to be more women in STEM, especially in coding.” Now she’s interested in applying to colleges with strong computer science programs so she can pursue a coding-related career.

      From MSYEP to the mayor’s office

      Mayor Sumbul Siddiqui visited the Data Activism learning site on Aug. 9, accompanied by Breazeal. A graduate of MSYEP herself, Siddiqui says, “Through hands-on learning through computer programming, Cambridge high school students have the unique opportunity to see themselves as data scientists. Students were able learn ways to combat discrimination that occurs through artificial intelligence.” In an Instagram post, Siddiqui also said, “I had a blast visiting the students and learning about their projects.”

      Students worked on an activity that asked them to envision how data science might be used to support marginalized communities. They transformed their answers into block-printed T-shirt designs, carving pictures of their hopes into rubber block stamps. Some students focused on the importance of data privacy, like Jacob T., who drew a birdcage to represent data stored and locked away by third party apps. He says, “I want to open that cage and restore my data to myself and see what can be done with it.”

      The subject of Cambridge Community Charter School student Jacob T.’s project was the importance of data privacy. For his T-shirt design, he drew a birdcage to represent data stored and locked away by third party apps. (From right to left:) Breazeal, Jacob T. Kiki, Raechel Walker, and Zeynep Yalcin.

      Photo: Katherine Ouellette

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      Many students wanted to see more representation in both the media they consume and across various professional fields. Nina talked about the importance of representation in media and how that could contribute to greater representation in the tech industry, while Kiki talked about encouraging more women to pursue STEM fields. Jesmin said, “I wanted to show that data science is accessible to everyone, no matter their origin or language you speak. I wrote ‘hello’ in Bangla, Arabic, and English, because I speak all three languages and they all resonate with me.”

      Student Jesmin (left) explains the concept of her T-shirt design to Mayor Siddiqui. She wants data science to be accessible to everyone, no matter their origin or language, so she drew a globe and wrote ‘hello’ in the three languages she speaks: Bangla, Arabic, and English.

      Photo: Katherine Ouellette

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      “Overall, I hope the students continue to use their data activism skills to re-envision a society that supports marginalized groups,” says Walker. “Moreover, I hope they are empowered to become data scientists and understand how their race can be a positive part of their identity.” More

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      Caspar Hare, Georgia Perakis named associate deans of Social and Ethical Responsibilities of Computing

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      Caspar Hare and Georgia Perakis have been appointed the new associate deans of the Social and Ethical Responsibilities of Computing (SERC), a cross-cutting initiative in the MIT Stephen A. Schwarzman College of Computing. Their new roles will take effect on Sept. 1.

      “Infusing social and ethical aspects of computing in academic research and education is a critical component of the college mission,” says Daniel Huttenlocher, dean of the MIT Schwarzman College of Computing and the Henry Ellis Warren Professor of Electrical Engineering and Computer Science. “I look forward to working with Caspar and Georgia on continuing to develop and advance SERC and its reach across MIT. Their complementary backgrounds and their broad connections across MIT will be invaluable to this next chapter of SERC.”

      Caspar Hare

      Hare is a professor of philosophy in the Department of Linguistics and Philosophy. A member of the MIT faculty since 2003, his main interests are in ethics, metaphysics, and epistemology. The general theme of his recent work has been to bring ideas about practical rationality and metaphysics to bear on issues in normative ethics and epistemology. He is the author of two books: “On Myself, and Other, Less Important Subjects” (Princeton University Press 2009), about the metaphysics of perspective, and “The Limits of Kindness” (Oxford University Press 2013), about normative ethics.

      Georgia Perakis

      Perakis is the William F. Pounds Professor of Management and professor of operations research, statistics, and operations management at the MIT Sloan School of Management, where she has been a faculty member since 1998. She investigates the theory and practice of analytics and its role in operations problems and is particularly interested in how to solve complex and practical problems in pricing, revenue management, supply chains, health care, transportation, and energy applications, among other areas. Since 2019, she has been the co-director of the Operations Research Center, an interdepartmental PhD program that jointly reports to MIT Sloan and the MIT Schwarzman College of Computing, a role in which she will remain. Perakis will also assume an associate dean role at MIT Sloan in recognition of her leadership.

      Hare and Perakis succeed David Kaiser, the Germeshausen Professor of the History of Science and professor of physics, and Julie Shah, the H.N. Slater Professor of Aeronautics and Astronautics, who will be stepping down from their roles at the conclusion of their three-year term on Aug. 31.

      “My deepest thanks to Dave and Julie for their tremendous leadership of SERC and contributions to the college as associate deans,” says Huttenlocher.

      SERC impact

      As the inaugural associate deans of SERC, Kaiser and Shah have been responsible for advancing a mission to incorporate humanist, social science, social responsibility, and civic perspectives into MIT’s teaching, research, and implementation of computing. In doing so, they have engaged dozens of faculty members and thousands of students from across MIT during these first three years of the initiative.

      They have brought together people from a broad array of disciplines to collaborate on crafting original materials such as active learning projects, homework assignments, and in-class demonstrations. A collection of these materials was recently published and is now freely available to the world via MIT OpenCourseWare.

      In February 2021, they launched the MIT Case Studies in Social and Ethical Responsibilities of Computing for undergraduate instruction across a range of classes and fields of study. The specially commissioned and peer-reviewed cases are based on original research and are brief by design. Three issues have been published to date and a fourth will be released later this summer. Kaiser will continue to oversee the successful new series as editor.

      Last year, 60 undergraduates, graduate students, and postdocs joined a community of SERC Scholars to help advance SERC efforts in the college. The scholars participate in unique opportunities throughout, such as the summer Experiential Ethics program. A multidisciplinary team of graduate students last winter worked with the instructors and teaching assistants of class 6.036 (Introduction to Machine Learning), MIT’s largest machine learning course, to infuse weekly labs with material covering ethical computing, data and model bias, and fairness in machine learning through SERC.

      Through efforts such as these, SERC has had a substantial impact at MIT and beyond. Over the course of their tenure, Kaiser and Shah have engaged about 80 faculty members, and more than 2,100 students took courses that included new SERC content in the last year alone. SERC’s reach extended well beyond engineering students, with about 500 exposed to SERC content through courses offered in the School of Humanities, Arts, and Social Sciences, the MIT Sloan School of Management, and the School of Architecture and Planning. More

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      Researchers discover major roadblock in alleviating network congestion

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      When users want to send data over the internet faster than the network can handle, congestion can occur — the same way traffic congestion snarls the morning commute into a big city.

      Computers and devices that transmit data over the internet break the data down into smaller packets and use a special algorithm to decide how fast to send those packets. These congestion control algorithms seek to fully discover and utilize available network capacity while sharing it fairly with other users who may be sharing the same network. These algorithms try to minimize delay caused by data waiting in queues in the network.

      Over the past decade, researchers in industry and academia have developed several algorithms that attempt to achieve high rates while controlling delays. Some of these, such as the BBR algorithm developed by Google, are now widely used by many websites and applications.

      But a team of MIT researchers has discovered that these algorithms can be deeply unfair. In a new study, they show there will always be a network scenario where at least one sender receives almost no bandwidth compared to other senders; that is, a problem known as “starvation” cannot be avoided.

      “What is really surprising about this paper and the results is that when you take into account the real-world complexity of network paths and all the things they can do to data packets, it is basically impossible for delay-controlling congestion control algorithms to avoid starvation using current methods,” says Mohammad Alizadeh, associate professor of electrical engineering and computer science (EECS).

      While Alizadeh and his co-authors weren’t able to find a traditional congestion control algorithm that could avoid starvation, there may be algorithms in a different class that could prevent this problem. Their analysis also suggests that changing how these algorithms work, so that they allow for larger variations in delay, could help prevent starvation in some network situations.

      Alizadeh wrote the paper with first author and EECS graduate student Venkat Arun and senior author Hari Balakrishnan, the Fujitsu Professor of Computer Science and Artificial Intelligence. The research will be presented at the ACM Special Interest Group on Data Communications (SIGCOMM) conference.

      Controlling congestion

      Congestion control is a fundamental problem in networking that researchers have been trying to tackle since the 1980s.

      A user’s computer does not know how fast to send data packets over the network because it lacks information, such as the quality of the network connection or how many other senders are using the network. Sending packets too slowly makes poor use of the available bandwidth. But sending them too quickly can overwhelm the network, and in doing so, packets will start to get dropped. These packets must be resent, which leads to longer delays. Delays can also be caused by packets waiting in queues for a long time.

      Congestion control algorithms use packet losses and delays as signals to infer congestion and decide how fast to send data. But the internet is complicated, and packets can be delayed and lost for reasons unrelated to network congestion. For instance, data could be held up in a queue along the way and then released with a burst of other packets, or the receiver’s acknowledgement might be delayed. The authors call delays that are not caused by congestion “jitter.”

      Even if a congestion control algorithm measures delay perfectly, it can’t tell the difference between delay caused by congestion and delay caused by jitter. Delay caused by jitter is unpredictable and confuses the sender. Because of this ambiguity, users start estimating delay differently, which causes them to send packets at unequal rates. Eventually, this leads to a situation where starvation occurs and someone gets shut out completely, Arun explains.

      “We started the project because we lacked a theoretical understanding of congestion control behavior in the presence of jitter. To place it on a firmer theoretical footing, we built a mathematical model that was simple enough to think about, yet able to capture some of the complexities of the internet. It has been very rewarding to have math tell us things we didn’t know and that have practical relevance,” he says.

      Studying starvation

      The researchers fed their mathematical model to a computer, gave it a series of commonly used congestion control algorithms, and asked the computer to find an algorithm that could avoid starvation, using their model.

      “We couldn’t do it. We tried every algorithm that we are aware of, and some new ones we made up. Nothing worked. The computer always found a situation where some people get all the bandwidth and at least one person gets basically nothing,” Arun says.

      The researchers were surprised by this result, especially since these algorithms are widely believed to be reasonably fair. They started suspecting that it may not be possible to avoid starvation, an extreme form of unfairness. This motivated them to define a class of algorithms they call “delay-convergent algorithms” that they proved will always suffer from starvation under their network model. All existing congestion control algorithms that control delay (that the researchers are aware of) are delay-convergent.

      The fact that such simple failure modes of these widely used algorithms remained unknown for so long illustrates how difficult it is to understand algorithms through empirical testing alone, Arun adds. It underscores the importance of a solid theoretical foundation.

      But all hope is not lost. While all the algorithms they tested failed, there may be other algorithms which are not delay-convergent that might be able to avoid starvation This suggests that one way to fix the problem might be to design congestion control algorithms that vary the delay range more widely, so the range is larger than any delay that might occur due to jitter in the network.

      “To control delays, algorithms have tried to also bound the variations in delay about a desired equilibrium, but there is nothing wrong in potentially creating greater delay variation to get better measurements of congestive delays. It is just a new design philosophy you would have to adopt,” Balakrishnan adds.

      Now, the researchers want to keep pushing to see if they can find or build an algorithm that will eliminate starvation. They also want to apply this approach of mathematical modeling and computational proofs to other thorny, unsolved problems in networked systems.

      “We are increasingly reliant on computer systems for very critical things, and we need to put their reliability on a firmer conceptual footing. We’ve shown the surprising things you can discover when you put in the time to come up with these formal specifications of what the problem actually is,” says Alizadeh.

      The NASA University Leadership Initiative (grant #80NSSC20M0163) provided funds to assist the authors with their research, but the research paper solely reflects the opinions and conclusions of its authors and not any NASA entity. This work was also partially funded by the National Science Foundation, award number 1751009. More

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      A technique to improve both fairness and accuracy in artificial intelligence

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      For workers who use machine-learning models to help them make decisions, knowing when to trust a model’s predictions is not always an easy task, especially since these models are often so complex that their inner workings remain a mystery.

      Users sometimes employ a technique, known as selective regression, in which the model estimates its confidence level for each prediction and will reject predictions when its confidence is too low. Then a human can examine those cases, gather additional information, and make a decision about each one manually.

      But while selective regression has been shown to improve the overall performance of a model, researchers at MIT and the MIT-IBM Watson AI Lab have discovered that the technique can have the opposite effect for underrepresented groups of people in a dataset. As the model’s confidence increases with selective regression, its chance of making the right prediction also increases, but this does not always happen for all subgroups.

      For instance, a model suggesting loan approvals might make fewer errors on average, but it may actually make more wrong predictions for Black or female applicants. One reason this can occur is due to the fact that the model’s confidence measure is trained using overrepresented groups and may not be accurate for these underrepresented groups.

      Once they had identified this problem, the MIT researchers developed two algorithms that can remedy the issue. Using real-world datasets, they show that the algorithms reduce performance disparities that had affected marginalized subgroups.

      “Ultimately, this is about being more intelligent about which samples you hand off to a human to deal with. Rather than just minimizing some broad error rate for the model, we want to make sure the error rate across groups is taken into account in a smart way,” says senior MIT author Greg Wornell, the Sumitomo Professor in Engineering in the Department of Electrical Engineering and Computer Science (EECS) who leads the Signals, Information, and Algorithms Laboratory in the Research Laboratory of Electronics (RLE) and is a member of the MIT-IBM Watson AI Lab.

      Joining Wornell on the paper are co-lead authors Abhin Shah, an EECS graduate student, and Yuheng Bu, a postdoc in RLE; as well as Joshua Ka-Wing Lee SM ’17, ScD ’21 and Subhro Das, Rameswar Panda, and Prasanna Sattigeri, research staff members at the MIT-IBM Watson AI Lab. The paper will be presented this month at the International Conference on Machine Learning.

      To predict or not to predict

      Regression is a technique that estimates the relationship between a dependent variable and independent variables. In machine learning, regression analysis is commonly used for prediction tasks, such as predicting the price of a home given its features (number of bedrooms, square footage, etc.) With selective regression, the machine-learning model can make one of two choices for each input — it can make a prediction or abstain from a prediction if it doesn’t have enough confidence in its decision.

      When the model abstains, it reduces the fraction of samples it is making predictions on, which is known as coverage. By only making predictions on inputs that it is highly confident about, the overall performance of the model should improve. But this can also amplify biases that exist in a dataset, which occur when the model does not have sufficient data from certain subgroups. This can lead to errors or bad predictions for underrepresented individuals.

      The MIT researchers aimed to ensure that, as the overall error rate for the model improves with selective regression, the performance for every subgroup also improves. They call this monotonic selective risk.

      “It was challenging to come up with the right notion of fairness for this particular problem. But by enforcing this criteria, monotonic selective risk, we can make sure the model performance is actually getting better across all subgroups when you reduce the coverage,” says Shah.

      Focus on fairness

      The team developed two neural network algorithms that impose this fairness criteria to solve the problem.

      One algorithm guarantees that the features the model uses to make predictions contain all information about the sensitive attributes in the dataset, such as race and sex, that is relevant to the target variable of interest. Sensitive attributes are features that may not be used for decisions, often due to laws or organizational policies. The second algorithm employs a calibration technique to ensure the model makes the same prediction for an input, regardless of whether any sensitive attributes are added to that input.

      The researchers tested these algorithms by applying them to real-world datasets that could be used in high-stakes decision making. One, an insurance dataset, is used to predict total annual medical expenses charged to patients using demographic statistics; another, a crime dataset, is used to predict the number of violent crimes in communities using socioeconomic information. Both datasets contain sensitive attributes for individuals.

      When they implemented their algorithms on top of a standard machine-learning method for selective regression, they were able to reduce disparities by achieving lower error rates for the minority subgroups in each dataset. Moreover, this was accomplished without significantly impacting the overall error rate.

      “We see that if we don’t impose certain constraints, in cases where the model is really confident, it could actually be making more errors, which could be very costly in some applications, like health care. So if we reverse the trend and make it more intuitive, we will catch a lot of these errors. A major goal of this work is to avoid errors going silently undetected,” Sattigeri says.

      The researchers plan to apply their solutions to other applications, such as predicting house prices, student GPA, or loan interest rate, to see if the algorithms need to be calibrated for those tasks, says Shah. They also want to explore techniques that use less sensitive information during the model training process to avoid privacy issues.

      And they hope to improve the confidence estimates in selective regression to prevent situations where the model’s confidence is low, but its prediction is correct. This could reduce the workload on humans and further streamline the decision-making process, Sattigeri says.

      This research was funded, in part, by the MIT-IBM Watson AI Lab and its member companies Boston Scientific, Samsung, and Wells Fargo, and by the National Science Foundation. More

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      Building explainability into the components of machine-learning models

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      Explanation methods that help users understand and trust machine-learning models often describe how much certain features used in the model contribute to its prediction. For example, if a model predicts a patient’s risk of developing cardiac disease, a physician might want to know how strongly the patient’s heart rate data influences that prediction.

      But if those features are so complex or convoluted that the user can’t understand them, does the explanation method do any good?

      MIT researchers are striving to improve the interpretability of features so decision makers will be more comfortable using the outputs of machine-learning models. Drawing on years of field work, they developed a taxonomy to help developers craft features that will be easier for their target audience to understand.

      “We found that out in the real world, even though we were using state-of-the-art ways of explaining machine-learning models, there is still a lot of confusion stemming from the features, not from the model itself,” says Alexandra Zytek, an electrical engineering and computer science PhD student and lead author of a paper introducing the taxonomy.

      To build the taxonomy, the researchers defined properties that make features interpretable for five types of users, from artificial intelligence experts to the people affected by a machine-learning model’s prediction. They also offer instructions for how model creators can transform features into formats that will be easier for a layperson to comprehend.

      They hope their work will inspire model builders to consider using interpretable features from the beginning of the development process, rather than trying to work backward and focus on explainability after the fact.

      MIT co-authors include Dongyu Liu, a postdoc; visiting professor Laure Berti-Équille, research director at IRD; and senior author Kalyan Veeramachaneni, principal research scientist in the Laboratory for Information and Decision Systems (LIDS) and leader of the Data to AI group. They are joined by Ignacio Arnaldo, a principal data scientist at Corelight. The research is published in the June edition of the Association for Computing Machinery Special Interest Group on Knowledge Discovery and Data Mining’s peer-reviewed Explorations Newsletter.

      Real-world lessons

      Features are input variables that are fed to machine-learning models; they are usually drawn from the columns in a dataset. Data scientists typically select and handcraft features for the model, and they mainly focus on ensuring features are developed to improve model accuracy, not on whether a decision-maker can understand them, Veeramachaneni explains.

      For several years, he and his team have worked with decision makers to identify machine-learning usability challenges. These domain experts, most of whom lack machine-learning knowledge, often don’t trust models because they don’t understand the features that influence predictions.

      For one project, they partnered with clinicians in a hospital ICU who used machine learning to predict the risk a patient will face complications after cardiac surgery. Some features were presented as aggregated values, like the trend of a patient’s heart rate over time. While features coded this way were “model ready” (the model could process the data), clinicians didn’t understand how they were computed. They would rather see how these aggregated features relate to original values, so they could identify anomalies in a patient’s heart rate, Liu says.

      By contrast, a group of learning scientists preferred features that were aggregated. Instead of having a feature like “number of posts a student made on discussion forums” they would rather have related features grouped together and labeled with terms they understood, like “participation.”

      “With interpretability, one size doesn’t fit all. When you go from area to area, there are different needs. And interpretability itself has many levels,” Veeramachaneni says.

      The idea that one size doesn’t fit all is key to the researchers’ taxonomy. They define properties that can make features more or less interpretable for different decision makers and outline which properties are likely most important to specific users.

      For instance, machine-learning developers might focus on having features that are compatible with the model and predictive, meaning they are expected to improve the model’s performance.

      On the other hand, decision makers with no machine-learning experience might be better served by features that are human-worded, meaning they are described in a way that is natural for users, and understandable, meaning they refer to real-world metrics users can reason about.

      “The taxonomy says, if you are making interpretable features, to what level are they interpretable? You may not need all levels, depending on the type of domain experts you are working with,” Zytek says.

      Putting interpretability first

      The researchers also outline feature engineering techniques a developer can employ to make features more interpretable for a specific audience.

      Feature engineering is a process in which data scientists transform data into a format machine-learning models can process, using techniques like aggregating data or normalizing values. Most models also can’t process categorical data unless they are converted to a numerical code. These transformations are often nearly impossible for laypeople to unpack.

      Creating interpretable features might involve undoing some of that encoding, Zytek says. For instance, a common feature engineering technique organizes spans of data so they all contain the same number of years. To make these features more interpretable, one could group age ranges using human terms, like infant, toddler, child, and teen. Or rather than using a transformed feature like average pulse rate, an interpretable feature might simply be the actual pulse rate data, Liu adds.

      “In a lot of domains, the tradeoff between interpretable features and model accuracy is actually very small. When we were working with child welfare screeners, for example, we retrained the model using only features that met our definitions for interpretability, and the performance decrease was almost negligible,” Zytek says.

      Building off this work, the researchers are developing a system that enables a model developer to handle complicated feature transformations in a more efficient manner, to create human-centered explanations for machine-learning models. This new system will also convert algorithms designed to explain model-ready datasets into formats that can be understood by decision makers. More

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      Exploring emerging topics in artificial intelligence policy

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      Members of the public sector, private sector, and academia convened for the second AI Policy Forum Symposium last month to explore critical directions and questions posed by artificial intelligence in our economies and societies.

      The virtual event, hosted by the AI Policy Forum (AIPF) — an undertaking by the MIT Schwarzman College of Computing to bridge high-level principles of AI policy with the practices and trade-offs of governing — brought together an array of distinguished panelists to delve into four cross-cutting topics: law, auditing, health care, and mobility.

      In the last year there have been substantial changes in the regulatory and policy landscape around AI in several countries — most notably in Europe with the development of the European Union Artificial Intelligence Act, the first attempt by a major regulator to propose a law on artificial intelligence. In the United States, the National AI Initiative Act of 2020, which became law in January 2021, is providing a coordinated program across federal government to accelerate AI research and application for economic prosperity and security gains. Finally, China recently advanced several new regulations of its own.

      Each of these developments represents a different approach to legislating AI, but what makes a good AI law? And when should AI legislation be based on binding rules with penalties versus establishing voluntary guidelines?

      Jonathan Zittrain, professor of international law at Harvard Law School and director of the Berkman Klein Center for Internet and Society, says the self-regulatory approach taken during the expansion of the internet had its limitations with companies struggling to balance their interests with those of their industry and the public.

      “One lesson might be that actually having representative government take an active role early on is a good idea,” he says. “It’s just that they’re challenged by the fact that there appears to be two phases in this environment of regulation. One, too early to tell, and two, too late to do anything about it. In AI I think a lot of people would say we’re still in the ‘too early to tell’ stage but given that there’s no middle zone before it’s too late, it might still call for some regulation.”

      A theme that came up repeatedly throughout the first panel on AI laws — a conversation moderated by Dan Huttenlocher, dean of the MIT Schwarzman College of Computing and chair of the AI Policy Forum — was the notion of trust. “If you told me the truth consistently, I would say you are an honest person. If AI could provide something similar, something that I can say is consistent and is the same, then I would say it’s trusted AI,” says Bitange Ndemo, professor of entrepreneurship at the University of Nairobi and the former permanent secretary of Kenya’s Ministry of Information and Communication.

      Eva Kaili, vice president of the European Parliament, adds that “In Europe, whenever you use something, like any medication, you know that it has been checked. You know you can trust it. You know the controls are there. We have to achieve the same with AI.” Kalli further stresses that building trust in AI systems will not only lead to people using more applications in a safe manner, but that AI itself will reap benefits as greater amounts of data will be generated as a result.

      The rapidly increasing applicability of AI across fields has prompted the need to address both the opportunities and challenges of emerging technologies and the impact they have on social and ethical issues such as privacy, fairness, bias, transparency, and accountability. In health care, for example, new techniques in machine learning have shown enormous promise for improving quality and efficiency, but questions of equity, data access and privacy, safety and reliability, and immunology and global health surveillance remain at large.

      MIT’s Marzyeh Ghassemi, an assistant professor in the Department of Electrical Engineering and Computer Science and the Institute for Medical Engineering and Science, and David Sontag, an associate professor of electrical engineering and computer science, collaborated with Ziad Obermeyer, an associate professor of health policy and management at the University of California Berkeley School of Public Health, to organize AIPF Health Wide Reach, a series of sessions to discuss issues of data sharing and privacy in clinical AI. The organizers assembled experts devoted to AI, policy, and health from around the world with the goal of understanding what can be done to decrease barriers to access to high-quality health data to advance more innovative, robust, and inclusive research results while being respectful of patient privacy.

      Over the course of the series, members of the group presented on a topic of expertise and were tasked with proposing concrete policy approaches to the challenge discussed. Drawing on these wide-ranging conversations, participants unveiled their findings during the symposium, covering nonprofit and government success stories and limited access models; upside demonstrations; legal frameworks, regulation, and funding; technical approaches to privacy; and infrastructure and data sharing. The group then discussed some of their recommendations that are summarized in a report that will be released soon.

      One of the findings calls for the need to make more data available for research use. Recommendations that stem from this finding include updating regulations to promote data sharing to enable easier access to safe harbors such as the Health Insurance Portability and Accountability Act (HIPAA) has for de-identification, as well as expanding funding for private health institutions to curate datasets, amongst others. Another finding, to remove barriers to data for researchers, supports a recommendation to decrease obstacles to research and development on federally created health data. “If this is data that should be accessible because it’s funded by some federal entity, we should easily establish the steps that are going to be part of gaining access to that so that it’s a more inclusive and equitable set of research opportunities for all,” says Ghassemi. The group also recommends taking a careful look at the ethical principles that govern data sharing. While there are already many principles proposed around this, Ghassemi says that “obviously you can’t satisfy all levers or buttons at once, but we think that this is a trade-off that’s very important to think through intelligently.”

      In addition to law and health care, other facets of AI policy explored during the event included auditing and monitoring AI systems at scale, and the role AI plays in mobility and the range of technical, business, and policy challenges for autonomous vehicles in particular.

      The AI Policy Forum Symposium was an effort to bring together communities of practice with the shared aim of designing the next chapter of AI. In his closing remarks, Aleksander Madry, the Cadence Designs Systems Professor of Computing at MIT and faculty co-lead of the AI Policy Forum, emphasized the importance of collaboration and the need for different communities to communicate with each other in order to truly make an impact in the AI policy space.

      “The dream here is that we all can meet together — researchers, industry, policymakers, and other stakeholders — and really talk to each other, understand each other’s concerns, and think together about solutions,” Madry said. “This is the mission of the AI Policy Forum and this is what we want to enable.” More

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      in Data Management & Statistics

      Robots play with play dough

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      The inner child in many of us feels an overwhelming sense of joy when stumbling across a pile of the fluorescent, rubbery mixture of water, salt, and flour that put goo on the map: play dough. (Even if this happens rarely in adulthood.)

      While manipulating play dough is fun and easy for 2-year-olds, the shapeless sludge is hard for robots to handle. Machines have become increasingly reliable with rigid objects, but manipulating soft, deformable objects comes with a laundry list of technical challenges, and most importantly, as with most flexible structures, if you move one part, you’re likely affecting everything else. 

      Scientists from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and Stanford University recently let robots take their hand at playing with the modeling compound, but not for nostalgia’s sake. Their new system learns directly from visual inputs to let a robot with a two-fingered gripper see, simulate, and shape doughy objects. “RoboCraft” could reliably plan a robot’s behavior to pinch and release play dough to make various letters, including ones it had never seen. With just 10 minutes of data, the two-finger gripper rivaled human counterparts that teleoperated the machine — performing on-par, and at times even better, on the tested tasks. 

      “Modeling and manipulating objects with high degrees of freedom are essential capabilities for robots to learn how to enable complex industrial and household interaction tasks, like stuffing dumplings, rolling sushi, and making pottery,” says Yunzhu Li, CSAIL PhD student and author on a new paper about RoboCraft. “While there’s been recent advances in manipulating clothes and ropes, we found that objects with high plasticity, like dough or plasticine — despite ubiquity in those household and industrial settings — was a largely underexplored territory. With RoboCraft, we learn the dynamics models directly from high-dimensional sensory data, which offers a promising data-driven avenue for us to perform effective planning.” 

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      With undefined, smooth material, the whole structure needs to be accounted for before you can do any type of efficient and effective modeling and planning. By turning the images into graphs of little particles, coupled with algorithms, RoboCraft, using a graph neural network as the dynamics model, makes more accurate predictions about the material’s change of shapes. 

      Typically, researchers have used complex physics simulators to model and understand force and dynamics being applied to objects, but RoboCraft simply uses visual data. The inner-workings of the system relies on three parts to shape soft material into, say, an “R.” 

      The first part — perception — is all about learning to “see.” It uses cameras to collect raw, visual sensor data from the environment, which are then turned into little clouds of particles to represent the shapes. A graph-based neural network then uses said particle data to learn to “simulate” the object’s dynamics, or how it moves. Then, algorithms help plan the robot’s behavior so it learns to “shape” a blob of dough, armed with the training data from the many pinches. While the letters are a bit loose, they’re indubitably representative. 

      Besides cutesy shapes, the team is (actually) working on making dumplings from dough and a prepared filling. Right now, with just a two finger gripper, it’s a big ask. RoboCraft would need additional tools (a baker needs multiple tools to cook; so do robots) — a rolling pin, a stamp, and a mold. 

      A more far in the future domain the scientists envision is using RoboCraft for assistance with household tasks and chores, which could be of particular help to the elderly or those with limited mobility. To accomplish this, given the many obstructions that could take place, a much more adaptive representation of the dough or item would be needed, and as well as exploration into what class of models might be suitable to capture the underlying structural systems. 

      “RoboCraft essentially demonstrates that this predictive model can be learned in very data-efficient ways to plan motion. In the long run, we are thinking about using various tools to manipulate materials,” says Li. “If you think about dumpling or dough making, just one gripper wouldn’t be able to solve it. Helping the model understand and accomplish longer-horizon planning tasks, such as, how the dough will deform given the current tool, movements and actions, is a next step for future work.” 

      Li wrote the paper alongside Haochen Shi, Stanford master’s student; Huazhe Xu, Stanford postdoc; Zhiao Huang, PhD student at the University of California at San Diego; and Jiajun Wu, assistant professor at Stanford. They will present the research at the Robotics: Science and Systems conference in New York City. The work is in part supported by the Stanford Institute for Human-Centered AI (HAI), the Samsung Global Research Outreach (GRO) Program, the Toyota Research Institute (TRI), and Amazon, Autodesk, Salesforce, and Bosch. More

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      in Data Management & Statistics

      Researchers release open-source photorealistic simulator for autonomous driving

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      Hyper-realistic virtual worlds have been heralded as the best driving schools for autonomous vehicles (AVs), since they’ve proven fruitful test beds for safely trying out dangerous driving scenarios. Tesla, Waymo, and other self-driving companies all rely heavily on data to enable expensive and proprietary photorealistic simulators, since testing and gathering nuanced I-almost-crashed data usually isn’t the most easy or desirable to recreate. 

      To that end, scientists from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) created “VISTA 2.0,” a data-driven simulation engine where vehicles can learn to drive in the real world and recover from near-crash scenarios. What’s more, all of the code is being open-sourced to the public. 

      “Today, only companies have software like the type of simulation environments and capabilities of VISTA 2.0, and this software is proprietary. With this release, the research community will have access to a powerful new tool for accelerating the research and development of adaptive robust control for autonomous driving,” says MIT Professor and CSAIL Director Daniela Rus, senior author on a paper about the research. 

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      VISTA is a data-driven, photorealistic simulator for autonomous driving. It can simulate not just live video but LiDAR data and event cameras, and also incorporate other simulated vehicles to model complex driving situations. VISTA is open source and the code can be found below.

      VISTA 2.0 builds off of the team’s previous model, VISTA, and it’s fundamentally different from existing AV simulators since it’s data-driven — meaning it was built and photorealistically rendered from real-world data — thereby enabling direct transfer to reality. While the initial iteration supported only single car lane-following with one camera sensor, achieving high-fidelity data-driven simulation required rethinking the foundations of how different sensors and behavioral interactions can be synthesized. 

      Enter VISTA 2.0: a data-driven system that can simulate complex sensor types and massively interactive scenarios and intersections at scale. With much less data than previous models, the team was able to train autonomous vehicles that could be substantially more robust than those trained on large amounts of real-world data. 

      “This is a massive jump in capabilities of data-driven simulation for autonomous vehicles, as well as the increase of scale and ability to handle greater driving complexity,” says Alexander Amini, CSAIL PhD student and co-lead author on two new papers, together with fellow PhD student Tsun-Hsuan Wang. “VISTA 2.0 demonstrates the ability to simulate sensor data far beyond 2D RGB cameras, but also extremely high dimensional 3D lidars with millions of points, irregularly timed event-based cameras, and even interactive and dynamic scenarios with other vehicles as well.” 

      The team was able to scale the complexity of the interactive driving tasks for things like overtaking, following, and negotiating, including multiagent scenarios in highly photorealistic environments. 

      Training AI models for autonomous vehicles involves hard-to-secure fodder of different varieties of edge cases and strange, dangerous scenarios, because most of our data (thankfully) is just run-of-the-mill, day-to-day driving. Logically, we can’t just crash into other cars just to teach a neural network how to not crash into other cars.

      Recently, there’s been a shift away from more classic, human-designed simulation environments to those built up from real-world data. The latter have immense photorealism, but the former can easily model virtual cameras and lidars. With this paradigm shift, a key question has emerged: Can the richness and complexity of all of the sensors that autonomous vehicles need, such as lidar and event-based cameras that are more sparse, accurately be synthesized? 

      Lidar sensor data is much harder to interpret in a data-driven world — you’re effectively trying to generate brand-new 3D point clouds with millions of points, only from sparse views of the world. To synthesize 3D lidar point clouds, the team used the data that the car collected, projected it into a 3D space coming from the lidar data, and then let a new virtual vehicle drive around locally from where that original vehicle was. Finally, they projected all of that sensory information back into the frame of view of this new virtual vehicle, with the help of neural networks. 

      Together with the simulation of event-based cameras, which operate at speeds greater than thousands of events per second, the simulator was capable of not only simulating this multimodal information, but also doing so all in real time — making it possible to train neural nets offline, but also test online on the car in augmented reality setups for safe evaluations. “The question of if multisensor simulation at this scale of complexity and photorealism was possible in the realm of data-driven simulation was very much an open question,” says Amini. 

      With that, the driving school becomes a party. In the simulation, you can move around, have different types of controllers, simulate different types of events, create interactive scenarios, and just drop in brand new vehicles that weren’t even in the original data. They tested for lane following, lane turning, car following, and more dicey scenarios like static and dynamic overtaking (seeing obstacles and moving around so you don’t collide). With the multi-agency, both real and simulated agents interact, and new agents can be dropped into the scene and controlled any which way. 

      Taking their full-scale car out into the “wild” — a.k.a. Devens, Massachusetts — the team saw  immediate transferability of results, with both failures and successes. They were also able to demonstrate the bodacious, magic word of self-driving car models: “robust.” They showed that AVs, trained entirely in VISTA 2.0, were so robust in the real world that they could handle that elusive tail of challenging failures. 

      Now, one guardrail humans rely on that can’t yet be simulated is human emotion. It’s the friendly wave, nod, or blinker switch of acknowledgement, which are the type of nuances the team wants to implement in future work. 

      “The central algorithm of this research is how we can take a dataset and build a completely synthetic world for learning and autonomy,” says Amini. “It’s a platform that I believe one day could extend in many different axes across robotics. Not just autonomous driving, but many areas that rely on vision and complex behaviors. We’re excited to release VISTA 2.0 to help enable the community to collect their own datasets and convert them into virtual worlds where they can directly simulate their own virtual autonomous vehicles, drive around these virtual terrains, train autonomous vehicles in these worlds, and then can directly transfer them to full-sized, real self-driving cars.” 

      Amini and Wang wrote the paper alongside Zhijian Liu, MIT CSAIL PhD student; Igor Gilitschenski, assistant professor in computer science at the University of Toronto; Wilko Schwarting, AI research scientist and MIT CSAIL PhD ’20; Song Han, associate professor at MIT’s Department of Electrical Engineering and Computer Science; Sertac Karaman, associate professor of aeronautics and astronautics at MIT; and Daniela Rus, MIT professor and CSAIL director. The researchers presented the work at the IEEE International Conference on Robotics and Automation (ICRA) in Philadelphia. 

      This work was supported by the National Science Foundation and Toyota Research Institute. The team acknowledges the support of NVIDIA with the donation of the Drive AGX Pegasus. More