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    A data-driven approach to making better choices

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    Imagine a world in which some important decision — a judge’s sentencing recommendation, a child’s treatment protocol, which person or business should receive a loan — was made more reliable because a well-designed algorithm helped a key decision-maker arrive at a better choice. A new MIT economics course is investigating these interesting possibilities.Class 14.163 (Algorithms and Behavioral Science) is a new cross-disciplinary course focused on behavioral economics, which studies the cognitive capacities and limitations of human beings. The course was co-taught this past spring by assistant professor of economics Ashesh Rambachan and visiting lecturer Sendhil Mullainathan.Rambachan studies the economic applications of machine learning, focusing on algorithmic tools that drive decision-making in the criminal justice system and consumer lending markets. He also develops methods for determining causation using cross-sectional and dynamic data.Mullainathan will soon join the MIT departments of Electrical Engineering and Computer Science and Economics as a professor. His research uses machine learning to understand complex problems in human behavior, social policy, and medicine. Mullainathan co-founded the Abdul Latif Jameel Poverty Action Lab (J-PAL) in 2003.The new course’s goals are both scientific (to understand people) and policy-driven (to improve society by improving decisions). Rambachan believes that machine-learning algorithms provide new tools for both the scientific and applied goals of behavioral economics.“The course investigates the deployment of computer science, artificial intelligence (AI), economics, and machine learning in service of improved outcomes and reduced instances of bias in decision-making,” Rambachan says.There are opportunities, Rambachan believes, for constantly evolving digital tools like AI, machine learning, and large language models (LLMs) to help reshape everything from discriminatory practices in criminal sentencing to health-care outcomes among underserved populations.Students learn how to use machine learning tools with three main objectives: to understand what they do and how they do it, to formalize behavioral economics insights so they compose well within machine learning tools, and to understand areas and topics where the integration of behavioral economics and algorithmic tools might be most fruitful.Students also produce ideas, develop associated research, and see the bigger picture. They’re led to understand where an insight fits and see where the broader research agenda is leading. Participants can think critically about what supervised LLMs can (and cannot) do, to understand how to integrate those capacities with the models and insights of behavioral economics, and to recognize the most fruitful areas for the application of what investigations uncover.The dangers of subjectivity and biasAccording to Rambachan, behavioral economics acknowledges that biases and mistakes exist throughout our choices, even absent algorithms. “The data used by our algorithms exist outside computer science and machine learning, and instead are often produced by people,” he continues. “Understanding behavioral economics is therefore essential to understanding the effects of algorithms and how to better build them.”Rambachan sought to make the course accessible regardless of attendees’ academic backgrounds. The class included advanced degree students from a variety of disciplines.By offering students a cross-disciplinary, data-driven approach to investigating and discovering ways in which algorithms might improve problem-solving and decision-making, Rambachan hopes to build a foundation on which to redesign existing systems of jurisprudence, health care, consumer lending, and industry, to name a few areas.“Understanding how data are generated can help us understand bias,” Rambachan says. “We can ask questions about producing a better outcome than what currently exists.”Useful tools for re-imagining social operationsEconomics doctoral student Jimmy Lin was skeptical about the claims Rambachan and Mullainathan made when the class began, but changed his mind as the course continued.“Ashesh and Sendhil started with two provocative claims: The future of behavioral science research will not exist without AI, and the future of AI research will not exist without behavioral science,” Lin says. “Over the course of the semester, they deepened my understanding of both fields and walked us through numerous examples of how economics informed AI research and vice versa.”Lin, who’d previously done research in computational biology, praised the instructors’ emphasis on the importance of a “producer mindset,” thinking about the next decade of research rather than the previous decade. “That’s especially important in an area as interdisciplinary and fast-moving as the intersection of AI and economics — there isn’t an old established literature, so you’re forced to ask new questions, invent new methods, and create new bridges,” he says.The speed of change to which Lin alludes is a draw for him, too. “We’re seeing black-box AI methods facilitate breakthroughs in math, biology, physics, and other scientific disciplines,” Lin  says. “AI can change the way we approach intellectual discovery as researchers.”An interdisciplinary future for economics and social systemsStudying traditional economic tools and enhancing their value with AI may yield game-changing shifts in how institutions and organizations teach and empower leaders to make choices.“We’re learning to track shifts, to adjust frameworks and better understand how to deploy tools in service of a common language,” Rambachan says. “We must continually interrogate the intersection of human judgment, algorithms, AI, machine learning, and LLMs.”Lin enthusiastically recommended the course regardless of students’ backgrounds. “Anyone broadly interested in algorithms in society, applications of AI across academic disciplines, or AI as a paradigm for scientific discovery should take this class,” he says. “Every lecture felt like a goldmine of perspectives on research, novel application areas, and inspiration on how to produce new, exciting ideas.”The course, Rambachan says, argues that better-built algorithms can improve decision-making across disciplines. “By building connections between economics, computer science, and machine learning, perhaps we can automate the best of human choices to improve outcomes while minimizing or eliminating the worst,” he says.Lin remains excited about the course’s as-yet unexplored possibilities. “It’s a class that makes you excited about the future of research and your own role in it,” he says. 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      Educating national security leaders on artificial intelligence

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      Understanding artificial intelligence and how it relates to matters of national security has become a top priority for military and government leaders in recent years. A new three-day custom program entitled “Artificial Intelligence for National Security Leaders” — AI4NSL for short — aims to educate leaders who may not have a technical background on the basics of AI, machine learning, and data science, and how these topics intersect with national security.

      “National security fundamentally is about two things: getting information out of sensors and processing that information. These are two things that AI excels at. The AI4NSL class engages national security leaders in understanding how to navigate the benefits and opportunities that AI affords, while also understanding its potential negative consequences,” says Aleksander Madry, the Cadence Design Systems Professor at MIT and one of the course’s faculty directors.

      Organized jointly by MIT’s School of Engineering, MIT Stephen A. Schwarzman College of Computing, and MIT Sloan Executive Education, AI4NSL wrapped up its fifth cohort in April. The course brings leaders from every branch of the U.S. military, as well as some foreign military leaders from NATO, to MIT’s campus, where they learn from faculty experts on a variety of technical topics in AI, as well as how to navigate organizational challenges that arise in this context.

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      AI for National Security Leaders | MIT Sloan Executive Education

      “We set out to put together a real executive education class on AI for senior national security leaders,” says Madry. “For three days, we are teaching these leaders not only an understanding of what this technology is about, but also how to best adopt these technologies organizationally.”

      The original idea sprang from discussions with senior U.S. Air Force (USAF) leaders and members of the Department of the Air Force (DAF)-MIT AI Accelerator in 2019.

      According to Major John Radovan, deputy director of the DAF-MIT AI Accelerator, in recent years it has become clear that national security leaders needed a deeper understanding of AI technologies and its implications on security, warfare, and military operations. In February 2020, Radovan and his team at the DAF-MIT AI Accelerator started building a custom course to help guide senior leaders in their discussions about AI.

      “This is the only course out there that is focused on AI specifically for national security,” says Radovan. “We didn’t want to make this course just for members of the Air Force — it had to be for all branches of the military. If we are going to operate as a joint force, we need to have the same vocabulary and the same mental models about how to use this technology.”

      After a pilot program in collaboration with MIT Open Learning and the MIT Computer Science and Artificial Intelligence Laboratory, Radovan connected with faculty at the School of Engineering and MIT Schwarzman College of Computing, including Madry, to refine the course’s curriculum. They enlisted the help of colleagues and faculty at MIT Sloan Executive Education to refine the class’s curriculum and cater the content to its audience. The result of this cross-school collaboration was a new iteration of AI4NSL, which was launched last summer.

      In addition to providing participants with a basic overview of AI technologies, the course places a heavy emphasis on organizational planning and implementation.

      “What we wanted to do was to create smart consumers at the command level. The idea was to present this content at a higher level so that people could understand the key frameworks, which will guide their thinking around the use and adoption of this material,” says Roberto Fernandez, the William F. Pounds Professor of Management and one of the AI4NSL instructors, as well as the other course’s faculty director.

      During the three-day course, instructors from MIT’s Department of Electrical Engineering and Computer Science, Department of Aeronautics and Astronautics, and MIT Sloan School of Management cover a wide range of topics.

      The first half of the course starts with a basic overview of concepts including AI, machine learning, deep learning, and the role of data. Instructors also present the problems and pitfalls of using AI technologies, including the potential for adversarial manipulation of machine learning systems, privacy challenges, and ethical considerations.

      In the middle of day two, the course shifts to examine the organizational perspective, encouraging participants to consider how to effectively implement these technologies in their own units.

      “What’s exciting about this course is the way it is formatted first in terms of understanding AI, machine learning, what data is, and how data feeds AI, and then giving participants a framework to go back to their units and build a strategy to make this work,” says Colonel Michelle Goyette, director of the Army Strategic Education Program at the Army War College and an AI4NSL participant.

      Throughout the course, breakout sessions provide participants with an opportunity to collaborate and problem-solve on an exercise together. These breakout sessions build upon one another as the participants are exposed to new concepts related to AI.

      “The breakout sessions have been distinctive because they force you to establish relationships with people you don’t know, so the networking aspect is key. Any time you can do more than receive information and actually get into the application of what you were taught, that really enhances the learning environment,” says Lieutenant General Brian Robinson, the commander of Air Education and Training Command for the USAF and an AI4NSL participant.

      This spirit of teamwork, collaboration, and bringing together individuals from different backgrounds permeates the three-day program. The AI4NSL classroom not only brings together national security leaders from all branches of the military, it also brings together faculty from three schools across MIT.

      “One of the things that’s most exciting about this program is the kind of overarching theme of collaboration,” says Rob Dietel, director of executive programs at Sloan School of Management. “We’re not drawing just from the MIT Sloan faculty, we’re bringing in top faculty from the Schwarzman College of Computing and the School of Engineering. It’s wonderful to be able to tap into those resources that are here on MIT’s campus to really make it the most impactful program that we can.”

      As new developments in generative AI, such as ChatGPT, and machine learning alter the national security landscape, the organizers at AI4NSL will continue to update the curriculum to ensure it is preparing leaders to understand the implications for their respective units.

      “The rate of change for AI and national security is so fast right now that it’s challenging to keep up, and that’s part of the reason we’ve designed this program. We’ve brought in some of our world-class faculty from different parts of MIT to really address the changing dynamic of AI,” adds Dietel. More

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      Celebrating the impact of IDSS

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      The “interdisciplinary approach” is something that has been lauded for decades for its ability to break down silos and create new integrated approaches to research.

      For Munther Dahleh, founding director of the MIT Institute for Data, Systems, and Society (IDSS), showing the community that data science and statistics can transcend individual disciplines and form a new holistic approach to addressing complex societal challenges has been crucial to the institute’s success.

      “From the very beginning, it was critical that we recognized the areas of data science, statistics, AI, and, in a way, computing, as transdisciplinary,” says Dahleh, who is the William A. Coolidge Professor in Electrical Engineering and Computer Science. “We made that point over and over — these are areas that embed in your field. It is not ours; this organization is here for everyone.”

      On April 14-15, researchers from across and beyond MIT joined together to celebrate the accomplishments and impact IDSS has had on research and education since its inception in 2015. Taking the place of IDSS’s annual statistics and data science conference SDSCon, the celebration also doubled as a way to recognize Dahleh for his work creating and executing the vision of IDSS as he prepares to step down from his director position this summer.

      In addition to talks and panels on statistics and computation, smart systems, automation and artificial intelligence, conference participants discussed issues ranging from climate change, health care, and misinformation. Nobel Prize winner and IDSS affiliate Professor Esther Duflo spoke on large scale immunization efforts, former MLK Visiting Professor Craig Watkins joined a panel on equity and justice in AI, and IDSS Associate Director Alberto Abadie discussed synthetic controls for policy evaluation. Other policy questions were explored through lightning talks, including those by students from the Technology and Policy Program (TPP) within IDSS.

      A place to call home

      The list of IDSS accomplishments over the last eight years is long and growing. From creating a home for 21st century statistics at MIT after other unsuccessful attempts, to creating a new PhD preparing the trilingual student who is an expert in data science and social science in the context of a domain, to playing a key role in determining an effective process for Covid testing in the early days of the pandemic, IDSS has left its mark on MIT. More recently, IDSS launched an initiative using big data to help effect structural and normative change toward racial equity, and will continue to explore societal challenges through the lenses of statistics, social science, and science and engineering.

      “I’m very proud of what we’ve done and of all the people who have contributed to this. The leadership team has been phenomenal in their commitment and their creativity,” Dahleh says. “I always say it doesn’t take one person, it takes the village to do what we have done, and I am very proud of that.”

      Prior to the institute’s formation, Dahleh and others at MIT were brought together to answer one key question: How would MIT prepare for the future of systems and data?

      “Data science is a complex area because in some ways it’s everywhere and it belongs to everyone, similar to statistics and AI,” Dahleh says “The most important part of creating an organization to support it was making it clear that it was an organization for everyone.” The response the team came back with was to build an Institute: a department that could cross all other departments and schools.

      While Dahleh and others on the committee were creating this blueprint for the future, the events that would lead early IDSS hires like Caroline Uhler to join the team were also beginning to take shape. Uhler, now an MIT professor of computer science and co-director of the Eric and Wendy Schmidt Center at the Broad Institute, was a panelist at the celebration discussing statistics and human health.

      In 2015, Uhler was a faculty member at the Institute of Science and Technology in Austria looking to move back to the U.S. “I was looking for positions in all different types of departments related to statistics, including electrical engineering and computer science, which were areas not related to my degree,” Uhler says. “What really got me to MIT was Munther’s vision for building a modern type of statistics, and the unique opportunity to be part of building what statistics should be moving forward.”

      The breadth of the Statistics and Data Science Center has given it a unique and a robust character that makes for an attractive collaborative environment at MIT. “A lot of IDSS’s impact has been in giving people like me a home,” Uhler adds. “By building an institute for statistics that is across all schools instead of housed within a single department, it has created a home for everyone who is interested in the field.”

      Filling the gap

      For Ali Jadbabaie, former IDSS associate director and another early IDSS hire, being in the right place at the right time landed him in the center of it all. A control theory expert and network scientist by training, Jadbabaie first came to MIT during a sabbatical from his position as a professor at the University of Pennsylvania.

      “My time at MIT coincided with the early discussions around forming IDSS and given my experience they asked me to stay and help with its creation,” Jadbabaie says. He is now head of the Department of Civil and Environmental Engineering at MIT, and he spoke at the celebration about a new MIT major in climate system science and engineering.

      A critical early accomplishment of IDSS was the creation of a doctoral program in social and engineering systems (SES), which has the goal of educating and fostering the success of a new type of PhD student, says Jadbabaie.

      “We realized we had this opportunity to educate a new type of PhD student who was conversant in the math of information sciences and statistics in addition to an understanding of a domain — infrastructures, climate, political polarization — in which problems arise,” he says. “This program would provide training in statistics and data science, the math of information sciences and a branch of social science that is relevant to their domain.”

      “SES has been filling a gap,” adds Jadbabaie. “We wanted to bring quantitative reasoning to areas in social sciences, particularly as they interact with complex engineering systems.”

      “My first year at MIT really broadened my horizon in terms of what was available and exciting,” says Manxi Wu, a member of the first cohort of students in the SES program after starting out in the Master of Science in Transportation (MST) program. “My advisor introduced me to a number of interesting topics at the intersection of game theory, economics, and engineering systems, and in my second year I realized my interest was really about the societal scale systems, with transportation as my go-to application area when I think about how to make an impact in the real world.”

      Wu, now an assistant professor in the School of Operations Research and Information Engineering at Cornell, was a panelist at the Celebration’s session on smart infrastructure systems. She says that the beauty of the SES program lies in its ability to create a common ground between groups of students and researchers who all have different applications interests but share an eagerness to sharpen their technical skills.

      “While we may be working on very different application areas, the core methodologies, such as mathematical tools for data science and probability optimization, create a common language,” Wu says. “We are all capable of speaking the technical language, and our diversified interests give us even more to talk about.”

      In addition to the PhD program, IDSS has helped bring quality MIT programming to people around the globe with its MicroMasters Program in Statistics and Data Science (SDS), which recently celebrated the certification of over 1,000 learners. The MicroMasters is just one offering in the newly-minted IDSSx, a collection of online learning opportunities for learners at different skill levels and interests.

      “The impact of branding what MIT-IDSS does across the globe has been great,” Dahleh says. “In addition, we’ve created smaller online programs for continued education in data science and machine learning, which I think is also critical in educating the community at large.”

      Hopes for the future

      Through all of its accomplishments, the core mission of IDSS has never changed.

      “The belief was always to create an institute focused on how data science can be used to solve pressing societal problems,” Dahleh says. “The organizational structure of IDSS as an MIT Institute has enabled it to promote data and systems as a transdiciplinary area that embeds in every domain to support its mission. This reverse ownership structure will continue to strengthen the presence of IDSS in MIT and will make it an essential unit within the Schwarzman College of Computing.”

      As Dahleh prepares to step down from his role, and Professor Martin Wainwright gets ready to fill his (very big) shoes as director, Dahleh’s colleagues say the real key to the success of IDSS all started with his passion and vision.

      “Creating a new academic unit within MIT is actually next to impossible,” Jadbabaie says. “It requires structural changes, as well as someone who has a strong understanding of multiple areas, who knows how to get people to work together collectively, and who has a mission.”

      “The most important thing is that he was inclusive,” he adds. “He didn’t try to create a gate around it and say these people are in and these people are not. I don’t think this would have ever happened without Munther at the helm.” More

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      3 Questions: Why cybersecurity is on the agenda for corporate boards of directors

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      Organizations of every size and in every industry are vulnerable to cybersecurity risks — a dynamic landscape of threats and vulnerabilities and a corresponding overload of possible mitigating controls. MIT Senior Lecturer Keri Pearlson, who is also the executive director of the research consortium Cybersecurity at MIT Sloan (CAMS) and an instructor for the new MIT Sloan Executive Education course Cybersecurity Governance for the Board of Directors, knows how business can get ahead of this risk. Here, she describes the current threat and explores how boards can mitigate their risk against cybercrime.

      Q: What does the current state of cyberattacks mean for businesses in 2023?

      A: Last year we were discussing how the pandemic heightened fear, uncertainty, doubt and chaos, opening new doors for malicious actors to do their cyber mischief in our organizations and our families. We saw an increase in ransomware and other cyber attacks, and we saw an increase in concern from operating executives and board of directors wondering how to keep the organization secure. Since then, we have seen a continued escalation of cyber incidents, many of which no longer make the headlines unless they are wildly unique, damaging, or different than previous incidents. For every new technology that cybersecurity professionals invent, it’s only a matter of time until malicious actors find a way around it. New leadership approaches are needed for 2023 as we move into the next phase of securing our organizations.

      In great part, this means ensuring deep cybersecurity competencies on our boards of directors. Cyber risk is so significant that a responsible board can no longer ignore it or just delegate it to risk management experts. In fact, an organization’s board of directors holds a uniquely vital role in safeguarding data and systems for the future because of their fiduciary responsibility to shareholders and their responsibility to oversee and mitigate business risk.

      As these cyber threats increase, and as companies bolster their cybersecurity budgets accordingly, the regulatory community is also advancing new requirements of companies. In March of this year, the SEC issued a proposed rule titled Cybersecurity Risk Management, Strategy, Governance, and Incident Disclosure. In it, the SEC describes its intention to require public companies to disclose whether their boards have members with cybersecurity expertise. Specifically, registrants will be required to disclose whether the entire board, a specific board member, or a board committee is responsible for the oversight of cyber risks; the processes by which the board is informed about cyber risks, and the frequency of its discussions on this topic; and whether and how the board or specified board committee considers cyber risks as part of its business strategy, risk management, and financial oversight.

      Q: How can boards help their organizations mitigate cyber risk?

      A: According to the studies I’ve conducted with my CAMS colleagues, most organizations focus on cyber protection rather than cyber resilience, and we believe that is a mistake. A company that invests only in protection is not managing the risk associated with getting up and running again in the event of a cyber incident, and they are not going to be able to respond appropriately to new regulations, either. Resiliency means having a practical plan for recovery and business continuation.

      Certainly, protection is part of the resilience equation, but if the pandemic taught us anything, it taught us that resilience is the ability to weather an attack and recover quickly with minimal impact to our operations. The ultimate goal of a cyber-resilient organization would be zero disruption from a cyber breach — no impact on operations, finances, technologies, supply chain or reputation. Board members should ask, What would it take for this to be the case? And they should ensure that executives and managers have made proper and appropriate preparations to respond and recover.

      Being a knowledgeable board member does not mean becoming a cybersecurity expert, but it does mean understanding basic concepts, risks, frameworks, and approaches. And it means having the ability to assess whether management appropriately comprehends related threats, has an appropriate cyber strategy, and can measure its effectiveness. Board members today require focused training on these critical areas to carry out their mission. Unfortunately, many enterprises fail to leverage their boards of directors in this capacity or prepare board members to actively contribute to strategy, protocols, and emergency action plans.

      Alongside my CAMS colleagues Stuart Madnick and Kevin Powers, I’m teaching a new  MIT Sloan Executive Education course, Cybersecurity Governance for the Board of Directors, designed to help organizations and their boards get up to speed. Participants will explore the board’s role in cybersecurity, as well as breach planning, response, and mitigation. And we will discuss the impact and requirements of the many new regulations coming forward, not just from the SEC, but also White House, Congress, and most states and countries around the world, which are imposing more high-level responsibilities on companies.

      Q: What are some examples of how companies, and specifically boards of directors, have successfully upped their cybersecurity game?

      A: To ensure boardroom skills reflect the patterns of the marketplace, companies such as FedEx, Hasbro, PNC, and UPS have transformed their approach to governing cyber risk, starting with board cyber expertise. In companies like these, building resiliency started with a clear plan — from the boardroom — built on business and economic analysis.

      In one company we looked at, the CEO realized his board was not well versed in the business context or financial exposure risk from a cyber attack, so he hired a third-party consulting firm to conduct a cybersecurity maturity assessment. The company CISO presented the results of the report to the enterprise risk management subcommittee, creating a productive dialogue around the business and financial impact of different investments in cybersecurity.  

      Another organization focused their board on the alignment of their cybersecurity program and operational risk. The CISO, chief risk officer, and board collaborated to understand the exposure of the organization from a risk perspective, resulting in optimizing their cyber insurance policy to mitigate the newly understood risk.

      One important takeaway from these examples is the importance of using the language of risk, resiliency, and reputation to bridge the gaps between technical cybersecurity needs and the oversight responsibilities executed by boards. Boards need to understand the financial exposure resulting from cyber risk, not just the technical components typically found in cyber presentations.

      Cyber risk is not going away. It’s escalating and becoming more sophisticated every day. Getting your board “on board” is key to meeting new guidelines, providing sufficient oversight to cybersecurity plans, and making organizations more resilient. More