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

    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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    Exploring emerging topics in artificial intelligence policy

    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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    Living better with algorithms

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

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

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

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

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

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

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

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

    Auditing social media algorithms

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

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

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

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

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

    Who gets good outcomes and who gets bad ones?

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

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

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

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

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

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

    What is the effect of X on Y?

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

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

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

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

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

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

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

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

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    3 Questions: Designing software for research ethics

    Data are arguably the world’s hottest form of currency, clocking in zeros and ones that hold ever more weight than before. But with all of our personal information being crunched into dynamite for enterprise solutions and the like, with a lack of consumer data protection, are we all getting left behind? 

    Jonathan Zong, a PhD candidate in electrical engineering and computer science at MIT, and an affiliate of the Computer Science and Artificial Intelligence Laboratory, thinks consent can be baked into the design of the software that gathers our data for online research. He created Bartleby, a system for debriefing research participants and eliciting their views about social media research that involved them. Using Bartleby, he says, researchers can automatically direct each of their study participants to a website where they can learn about their involvement in research, view what data researchers collected about them, and give feedback. Most importantly, participants can use the website to opt out and request to delete their data.  

    Zong and his co-author, Nathan Matias SM ’13, PhD ’17, evaluated Bartleby by debriefing thousands of participants in observational and experimental studies on Twitter and Reddit. They found that Bartleby addresses procedural concerns by creating opportunities for participants to exercise autonomy, and the tool enabled substantive, value-driven conversations about participant voice and power. Here, Zong discusses the implications of their recent work as well as the future of social, ethical, and responsible computing.

    Q: Many leading tech ethicists and policymakers believe it’s impossible to keep people informed about their involvement in research and how their data are used. How has your work changed that?

    A: When Congress asked Mark Zuckerberg in 2018 about Facebook’s obligations to keep users informed about how their data is used, his answer was effectively that all users had the opportunity to read the privacy policy, and that being any clearer would be too difficult. Tech elites often blanket-statement that ethics is complicated, and proceed with their objective anyway. Many have claimed it’s impossible to fulfill ethical responsibilities to users at scale, so why try? But by creating Bartleby, a system for debriefing participants and eliciting their views about studies that involved them, we built something that shows that it’s not only very possible, but actually pretty easy to do. In a lot of situations, letting people know we want their data and explaining why we think it’s worth it is the bare minimum we could be doing.

    Q: Can ethical challenges be solved with a software tool?

    A: Off-the-shelf software actually can make a meaningful difference in respecting people’s autonomy. Ethics regulations almost never require a debriefing process for online studies. But because we used Bartleby, people had a chance to make an informed decision. It’s a chance they otherwise wouldn’t have had.

    At the same time, we realized that using Bartleby shined a light on deeper ethics questions that required substantive reflection. For example, most people are just trying to go about their lives and ignore the messages we send them, while others reply with concerns that aren’t even always about the research. Even if indirectly, these instances help signal nuances that research participants care about.

    Where might our values as researchers differ from participants’ values? How do the power structures that shape researchers’ interaction with users and communities affect our ability to see those differences? Using software to deliver ethics procedures helps bring these questions to light. But rather than expecting definitive answers that work in every situation, we should be thinking about how using software to create opportunities for participant voice and power challenges and invites us to reflect on how we address conflicting values.

    Q: How does your approach to design help suggest a way forward for social, ethical, and responsible computing?

    A: In addition to presenting the software tool, our peer-reviewed article on Bartleby also demonstrates a theoretical framework for data ethics, inspired by ideas in feminist philosophy. Because my work spans software design, empirical social science, and philosophy, I often think about the things I want people to take away in terms of interdisciplinary bridges I want to build. 

    I hope people look at Bartleby and see that ethics is an exciting area for technical innovation that can be tested empirically — guided by a clear-headed understanding of values. Umberto Eco, a philosopher, wrote that “form must not be a vehicle for thought, it must be a way of thinking.” In other words, designing software isn’t just about putting ideas we’ve already had into a computational form. Design is also a way we can think new ideas into existence, produce new ways of knowing and doing, and imagine alternative futures. More

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    Estimating the informativeness of data

    Not all data are created equal. But how much information is any piece of data likely to contain? This question is central to medical testing, designing scientific experiments, and even to everyday human learning and thinking. MIT researchers have developed a new way to solve this problem, opening up new applications in medicine, scientific discovery, cognitive science, and artificial intelligence.

    In theory, the 1948 paper, “A Mathematical Theory of Communication,” by the late MIT Professor Emeritus Claude Shannon answered this question definitively. One of Shannon’s breakthrough results is the idea of entropy, which lets us quantify the amount of information inherent in any random object, including random variables that model observed data. Shannon’s results created the foundations of information theory and modern telecommunications. The concept of entropy has also proven central to computer science and machine learning.

    The challenge of estimating entropy

    Unfortunately, the use of Shannon’s formula can quickly become computationally intractable. It requires precisely calculating the probability of the data, which in turn requires calculating every possible way the data could have arisen under a probabilistic model. If the data-generating process is very simple — for example, a single toss of a coin or roll of a loaded die — then calculating entropies is straightforward. But consider the problem of medical testing, where a positive test result is the result of hundreds of interacting variables, all unknown. With just 10 unknowns, there are already 1,000 possible explanations for the data. With a few hundred, there are more possible explanations than atoms in the known universe, which makes calculating the entropy exactly an unmanageable problem.

    MIT researchers have developed a new method to estimate good approximations to many information quantities such as Shannon entropy by using probabilistic inference. The work appears in a paper presented at AISTATS 2022 by authors Feras Saad ’16, MEng ’16, a PhD candidate in electrical engineering and computer science; Marco-Cusumano Towner PhD ’21; and Vikash Mansinghka ’05, MEng ’09, PhD ’09, a principal research scientist in the Department of Brain and Cognitive Sciences. The key insight is, rather than enumerate all explanations, to instead use probabilistic inference algorithms to first infer which explanations are probable and then use these probable explanations to construct high-quality entropy estimates. The paper shows that this inference-based approach can be much faster and more accurate than previous approaches.

    Estimating entropy and information in a probabilistic model is fundamentally hard because it often requires solving a high-dimensional integration problem. Many previous works have developed estimators of these quantities for certain special cases, but the new estimators of entropy via inference (EEVI) offer the first approach that can deliver sharp upper and lower bounds on a broad set of information-theoretic quantities. An upper and lower bound means that although we don’t know the true entropy, we can get a number that is smaller than it and a number that is higher than it.

    “The upper and lower bounds on entropy delivered by our method are particularly useful for three reasons,” says Saad. “First, the difference between the upper and lower bounds gives a quantitative sense of how confident we should be about the estimates. Second, by using more computational effort we can drive the difference between the two bounds to zero, which ‘squeezes’ the true value with a high degree of accuracy. Third, we can compose these bounds to form estimates of many other quantities that tell us how informative different variables in a model are of one another.”

    Solving fundamental problems with data-driven expert systems

    Saad says he is most excited about the possibility that this method gives for querying probabilistic models in areas like machine-assisted medical diagnoses. He says one goal of the EEVI method is to be able to solve new queries using rich generative models for things like liver disease and diabetes that have already been developed by experts in the medical domain. For example, suppose we have a patient with a set of observed attributes (height, weight, age, etc.) and observed symptoms (nausea, blood pressure, etc.). Given these attributes and symptoms, EEVI can be used to help determine which medical tests for symptoms the physician should conduct to maximize information about the absence or presence of a given liver disease (like cirrhosis or primary biliary cholangitis).

    For insulin diagnosis, the authors showed how to use the method for computing optimal times to take blood glucose measurements that maximize information about a patient’s insulin sensitivity, given an expert-built probabilistic model of insulin metabolism and the patient’s personalized meal and medication schedule. As routine medical tracking like glucose monitoring moves away from doctor’s offices and toward wearable devices, there are even more opportunities to improve data acquisition, if the value of the data can be estimated accurately in advance.

    Vikash Mansinghka, senior author on the paper, adds, “We’ve shown that probabilistic inference algorithms can be used to estimate rigorous bounds on information measures that AI engineers often think of as intractable to calculate. This opens up many new applications. It also shows that inference may be more computationally fundamental than we thought. It also helps to explain how human minds might be able to estimate the value of information so pervasively, as a central building block of everyday cognition, and help us engineer AI expert systems that have these capabilities.”

    The paper, “Estimators of Entropy and Information via Inference in Probabilistic Models,” was presented at AISTATS 2022. More

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    MIT Schwarzman College of Computing unveils Break Through Tech AI

    Aimed at driving diversity and inclusion in artificial intelligence, the MIT Stephen A. Schwarzman College of Computing is launching Break Through Tech AI, a new program to bridge the talent gap for women and underrepresented genders in AI positions in industry.

    Break Through Tech AI will provide skills-based training, industry-relevant portfolios, and mentoring to qualified undergraduate students in the Greater Boston area in order to position them more competitively for careers in data science, machine learning, and artificial intelligence. The free, 18-month program will also provide each student with a stipend for participation to lower the barrier for those typically unable to engage in an unpaid, extra-curricular educational opportunity.

    “Helping position students from diverse backgrounds to succeed in fields such as data science, machine learning, and artificial intelligence is critical for our society’s future,” says Daniel Huttenlocher, dean of the MIT Schwarzman College of Computing and Henry Ellis Warren Professor of Electrical Engineering and Computer Science. “We look forward to working with students from across the Greater Boston area to provide them with skills and mentorship to help them find careers in this competitive and growing industry.”

    The college is collaborating with Break Through Tech — a national initiative launched by Cornell Tech in 2016 to increase the number of women and underrepresented groups graduating with degrees in computing — to host and administer the program locally. In addition to Boston, the inaugural artificial intelligence and machine learning program will be offered in two other metropolitan areas — one based in New York hosted by Cornell Tech and another in Los Angeles hosted by the University of California at Los Angeles Samueli School of Engineering.

    “Break Through Tech’s success at diversifying who is pursuing computer science degrees and careers has transformed lives and the industry,” says Judith Spitz, executive director of Break Through Tech. “With our new collaborators, we can apply our impactful model to drive inclusion and diversity in artificial intelligence.”

    The new program will kick off this summer at MIT with an eight-week, skills-based online course and in-person lab experience that teaches industry-relevant tools to build real-world AI solutions. Students will learn how to analyze datasets and use several common machine learning libraries to build, train, and implement their own ML models in a business context.

    Following the summer course, students will be matched with machine-learning challenge projects for which they will convene monthly at MIT and work in teams to build solutions and collaborate with an industry advisor or mentor throughout the academic year, resulting in a portfolio of resume-quality work. The participants will also be paired with young professionals in the field to help build their network, prepare their portfolio, practice for interviews, and cultivate workplace skills.

    “Leveraging the college’s strong partnership with industry, Break Through AI will offer unique opportunities to students that will enhance their portfolio in machine learning and AI,” says Asu Ozdaglar, deputy dean of academics of the MIT Schwarzman College of Computing and head of the Department of Electrical Engineering and Computer Science. Ozdaglar, who will be the MIT faculty director of Break Through Tech AI, adds: “The college is committed to making computing inclusive and accessible for all. We’re thrilled to host this program at MIT for the Greater Boston area and to do what we can to help increase diversity in computing fields.”

    Break Through Tech AI is part of the MIT Schwarzman College of Computing’s focus to advance diversity, equity, and inclusion in computing. The college aims to improve and create programs and activities that broaden participation in computing classes and degree programs, increase the diversity of top faculty candidates in computing fields, and ensure that faculty search and graduate admissions processes have diverse slates of candidates and interviews.

    “By engaging in activities like Break Through Tech AI that work to improve the climate for underrepresented groups, we’re taking an important step toward creating more welcoming environments where all members can innovate and thrive,” says Alana Anderson, assistant dean for diversity, equity and inclusion for the Schwarzman College of Computing. More

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    Unlocking new doors to artificial intelligence

    Artificial intelligence research is constantly developing new hypotheses that have the potential to benefit society and industry; however, sometimes these benefits are not fully realized due to a lack of engineering tools. To help bridge this gap, graduate students in the MIT Department of Electrical Engineering and Computer Science’s 6-A Master of Engineering (MEng) Thesis Program work with some of the most innovative companies in the world and collaborate on cutting-edge projects, while contributing to and completing their MEng thesis.

    During a portion of the last year, four 6-A MEng students teamed up and completed an internship with IBM Research’s advanced prototyping team through the MIT-IBM Watson AI Lab on AI projects, often developing web applications to solve a real-world issue or business use cases. Here, the students worked alongside AI engineers, user experience engineers, full-stack researchers, and generalists to accommodate project requests and receive thesis advice, says Lee Martie, IBM research staff member and 6-A manager. The students’ projects ranged from generating synthetic data to allow for privacy-sensitive data analysis to using computer vision to identify actions in video that allows for monitoring human safety and tracking build progress on a construction site.

    “I appreciated all of the expertise from the team and the feedback,” says 6-A graduate Violetta Jusiega ’21, who participated in the program. “I think that working in industry gives the lens of making sure that the project’s needs are satisfied and [provides the opportunity] to ground research and make sure that it is helpful for some use case in the future.”

    Jusiega’s research intersected the fields of computer vision and design to focus on data visualization and user interfaces for the medical field. Working with IBM, she built an application programming interface (API) that let clinicians interact with a medical treatment strategy AI model, which was deployed in the cloud. Her interface provided a medical decision tree, as well as some prescribed treatment plans. After receiving feedback on her design from physicians at a local hospital, Jusiega developed iterations of the API and how the results where displayed, visually, so that it would be user-friendly and understandable for clinicians, who don’t usually code. She says that, “these tools are often not acquired into the field because they lack some of these API principles which become more important in an industry where everything is already very fast paced, so there’s little time to incorporate a new technology.” But this project might eventually allow for industry deployment. “I think this application has a bunch of potential, whether it does get picked up by clinicians or whether it’s simply used in research. It’s very promising and very exciting to see how technology can help us modify, or I can improve, the health-care field to be even more custom-tailored towards patients and giving them the best care possible,” she says.

    Another 6-A graduate student, Spencer Compton, was also considering aiding professionals to make more informed decisions, for use in settings including health care, but he was tackling it from a causal perspective. When given a set of related variables, Compton was investigating if there was a way to determine not just correlation, but the cause-and-effect relationship between them (the direction of the interaction) from the data alone. For this, he and his collaborators from IBM Research and Purdue University turned to a field of math called information theory. With the goal of designing an algorithm to learn complex networks of causal relationships, Compton used ideas relating to entropy, the randomness in a system, to help determine if a causal relationship is present and how variables might be interacting. “When judging an explanation, people often default to Occam’s razor” says Compton. “We’re more inclined to believe a simpler explanation than a more complex one.” In many cases, he says, it seemed to perform well. For instance, they were able to consider variables such as lung cancer, pollution, and X-ray findings. He was pleased that his research allowed him to help create a framework of “entropic causal inference” that could aid in safe and smart decisions in the future, in a satisfying way. “The math is really surprisingly deep, interesting, and complex,” says Compton. “We’re basically asking, ‘when is the simplest explanation correct?’ but as a math question.”

    Determining relationships within data can sometimes require large volumes of it to suss out patterns, but for data that may contain sensitive information, this may not be available. For her master’s work, Ivy Huang worked with IBM Research to generate synthetic tabular data using a natural language processing tool called a transformer model, which can learn and predict future values from past values. Trained on real data, the model can produce new data with similar patterns, properties, and relationships without restrictions like privacy, availability, and access that might come with real data in financial transactions and electronic medical records. Further, she created an API and deployed the model in an IBM cluster, which allowed users increased access to the model and abilities to query it without compromising the original data.

    Working with the advanced prototyping team, MEng candidate Brandon Perez also considered how to gather and investigate data with restrictions, but in his case it was to use computer vision frameworks, centered on an action recognition model, to identify construction site happenings. The team based their work on the Moments in Time dataset, which contains over a million three-second video clips with about 300 attached classification labels, and has performed well during AI training. However, the group needed more construction-based video data. For this, they used YouTube-8M. Perez built a framework for testing and fine-tuning existing object detection models and action recognition models that could plug into an automatic spatial and temporal localization tool — how they would identify and label particular actions in a video timeline. “I was satisfied that I was able to explore what made me curious, and I was grateful for the autonomy that I was given with this project,” says Perez. “I felt like I was always supported, and my mentor was a great support to the project.”

    “The kind of collaborations that we have seen between our MEng students and IBM researchers are exactly what the 6-A MEng Thesis program at MIT is all about,” says Tomas Palacios, professor of electrical engineering and faculty director of the MIT 6-A MEng Thesis program. “For more than 100 years, 6-A has been connecting MIT students with industry to solve together some of the most important problems in the world.” More

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    Professors Elchanan Mossel and Rosalind Picard named 2021 ACM Fellows

    The Association for Computing Machinery (ACM) has named MIT professors Elchanan Mossel and Rosalind Picard as fellows for outstanding accomplishments in computing and information technology.

    The ACM Fellows program recognizes wide-ranging and fundamental contributions in areas including algorithms, computer science education, cryptography, data security and privacy, medical informatics, and mobile and networked systems, among many other areas. The accomplishments of the 2021 ACM Fellows underpin important innovations that shape the technologies we use every day.

    Elchanan Mossel

    Mossel is a professor of mathematics and a member at the Statistics and Data Science Center of the MIT Institute for Data, Systems and Society. His research in discrete functional inequalities, isoperimetry, and hypercontractivity led to the proof that Majority is Stablest and confirmed the optimality of the Goemans-Williamson MAX-CUT algorithm under the unique games conjecture from computational complexity. His work on the reconstruction problem on trees provides optimal algorithms and bounds for phylogenetic reconstruction in molecular biology and has led to sharp results in the analysis of Gibbs samplers from statistical physics and inference problems on graphs. His research has resolved open problems in computational biology, machine learning, social choice theory, and economics.Mossel received a BS from the Open University in Israel in 1992, and MS (1997) and PhD (2000) degrees in mathematics from the Hebrew University of Jerusalem. He was a postdoc at the Microsoft Research Theory Group and a Miller Fellow at University of California at Berkeley. He joined the UC Berkeley faculty in 2003 as a professor of statistics and computer science, and spent leaves as a professor at the Weizmann Institute and at the Wharton School before joining MIT in 2016 as a full professor.

    In 2020, he received the Vannevar Bush Faculty Fellowship of the U.S. Department of Defense. Other distinctions include being named a Simons Investigator in Mathematics in 2019, being selected as a fellow of the AMS, and receiving a Sloan Research Fellowship, NSF CAREER Award, and the Bergmann Memorial Award from the U.S.-Israel Binational Science Foundation.

    “I am honored by this award,” says Mossel. “It makes me realize how fortunate I’ve been, working with creative and generous colleagues, and mentoring brilliant young minds.”

    Rosalind Picard

    Picard is a scientist, engineer, author, and professor of media arts and sciences at the MIT Media Lab. She is recognized as the founder of the field of affective computing, and has carried this research forward as head of the Media Lab’s Affective Computing research group. She is also a founding faculty chair of MIT’s MindHandHeart Initiative, and a faculty member of the MIT Center for Neurobiological Engineering. Picard is an IEEE fellow, and a member of the National Academy of Engineering. 

    Picard’s inventions are in use by thousands of research teams worldwide as well as in numerous products and services. She has co-founded two companies: Affectiva (now part of Smart Eye), providing emotion AI technologies now used by more than 25 percent of the Global Fortune 500, and Empatica, providing wearable sensors and analytics to improve health. Starting from inventions by Picard and her team, Empatica created the first AI-based smart watch cleared by the FDA (in neurology for monitoring seizures), which is now helping to bring potentially lifesaving help for people with epilepsy. 

    “This award makes me think of how blessed I am to work with so many amazing people here at MIT, especially at the Media Lab,” Picard notes. “Whenever any one of us has our contributions recognized, it is also a recognition of how special a place this is.” More