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    3 Questions: Catherine D’Ignazio on data science and a quest for justice

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    As long as we apply data science to society, we should remember that our data may have flaws, biases, and absences. That is one motif of MIT Associate Professor Catherine D’Ignazio’s new book, “Counting Feminicide,” published this spring by the MIT Press. In it, D’Ignazio explores the world of Latin American activists who began using media accounts and other sources to tabulate how many women had been killed in their countries as the result of gender-based violence — and found that their own numbers differed greatly from official statistics.Some of these activists have become prominent public figures, and others less so, but all of them have produced work providing lessons about collecting data, sharing it, and applying data to projects supporting human liberty and dignity. Now, their stories are reaching a new audience thanks to D’Ignazio, an associate professor of urban science and planning in MIT’s Department of Urban Studies and Planning, and director of MIT’s Data and Feminism Lab. She is also hosting an ongoing, transnational book club about the work. MIT News spoke with D’Ignazio about the new book and how activists are expanding the traditional practice of data science.Q: What is your book about?A: Three things. It’s a book that documents the rise of data activism as a really interesting form of citizen data science. Increasingly, because of the availability of data and tools, gathering and doing your own data analysis is a growing form of social activism. We characterize it in the book as a citizenship practice. People are using data to make knowledge claims and put political demands out there for their institutions to respond to.Another takeaway is that from observing data activists, there are ways they approach data science that are very different from how it’s usually taught. Among other things, when undertaking work about inequality and violence, there’s a connection with the rows of data. It’s about memorializing people who have been lost. Mainstream data scientists can learn a lot from this.The third thing is about feminicide itself and missing information. The main reason people start collecting data about feminicide is because their institutions aren’t doing it. This includes our institutions here in the United States. We’re talking about violence against women that the state is neglecting to count, classify, or take action on. So, activists step into these gaps and do this to the best of their ability, and they have been quite effective. The media will go to the activists, who end up becoming authorities on feminicide.Q: Can you elaborate on the differences between the practices of these data activists and more standard data science?A: One difference is what I’ll call the intimacy and proximity to the rows of the data set. In conventional data science, when you’re analyzing data, typically you’re not also the data collector. However these activists and groups are involved across the entire pipeline. As a result, there’s a connection and humanization to each line of the data set. For example, there is a school nurse in Texas who runs the site Women Count USA, and she will spend many hours trying to find photographs of victims of feminicide, which represents unusual care paid to each row of a dataset.Another point is the sophistication that the data activists have around what their data represent and what the biases are in the data. In mainstream AI and data science, we’re still having conversations where people seem surprised that there is bias in datasets. But I was impressed with the critical sophistication with which the activists approached their data. They gather information from the media and are familiar with the biases media have, and are aware their data is not comprehensive but is still useful. We can hold those two things together. It’s often more comprehensive data than what the institutions themselves have or will release to the public.Q: You did not just chronicle the work of activists, but engaged with them as well, and report about that in the book. What did you work on with them?A: One big component in the book is the participatory technology development that we engaged in with the activists, and one chapter is a case study of our work with activists to co-design machine learning and AI technology that supports their work. Our team was brainstorming about a system for the activists that would automatically find cases, verify them, and put them right in the database. Interestingly, the activists pushed back on that. They did not want full automation. They felt being, in effect, witnesses is an important part of the work. The emotional burden is an important part of the work and very central to it, too. That’s not something I might always expect to hear from data scientists.Keeping the human in the loop also means the human makes the final decision over whether a specific item constitutes feminicide or not. Handling it like that aligns with the fact that there are multiple definitions of feminicide, which is a complicated thing from a computational perspective. The proliferation of definitions about what counts as feminicide is a reflection of the fact that this is an ongoing global, transnational conversation. Feminicide has been codified in many laws, especially in Latin American countries, but none of those single laws is definitive. And no single activist definition is definitive. People are creating this together, through dialogue and struggle, so any computational system has to be designed with that understanding of the democratic process in mind. More

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      Q&A: Exploring ethnic dynamics and climate change in Africa

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      Evan Lieberman is the Total Professor of Political Science and Contemporary Africa at MIT, and is also director of the Center for International Studies. During a semester-long sabbatical, he’s currently based at the African Climate and Development Initiative at the University of Cape Town.In this Q&A, Lieberman discusses several climate-related research projects he’s pursuing in South Africa and surrounding countries. This is part of an ongoing series exploring how the School of Humanities, Arts, and Social Sciences is addressing the climate crisis.Q: South Africa is a nation whose political and economic development you have long studied and written about. Do you see this visit as an extension of the kind of research you have been pursuing, or a departure from it?A: Much of my previous work has been animated by the question of understanding the causes and consequences of group-based disparities, whether due to AIDS or Covid. These are problems that know no geographic boundaries, and where ethnic and racial minorities are often hardest hit. Climate change is an analogous problem, with these minority populations living in places where they are most vulnerable, in heat islands in cities, and in coastal areas where they are not protected. The reality is they might get hit much harder by longer-term trends and immediate shocks.In one line of research, I seek to understand how people in different African countries, in different ethnic groups, perceive the problems of climate change and their governments’ response to it. There are ethnic divisions of labor in terms of what people do — whether they are farmers or pastoralists, or live in cities. So some ethnic groups are simply more affected by drought or extreme weather than others, and this can be a basis for conflict, especially when competing for often limited government resources.In this area, just like in my previous research, learning what shapes ordinary citizen perspectives is really important, because these views affect people’s everyday practices, and the extent to which they support certain kinds of policies and investments their government makes in response to climate-related challenges. But I will also try to learn more about the perspectives of policymakers and various development partners who seek to balance climate-related challenges against a host of other problems and priorities.Q: You recently published “Until We Have Won Our Liberty,” which examines the difficult transition of South Africa from apartheid to a democratic government, scrutinizing in particular whether the quality of life for citizens has improved in terms of housing, employment, discrimination, and ethnic conflicts. How do climate change-linked issues fit into your scholarship?A: I never saw myself as a climate researcher, but a number of years ago, heavily influenced by what I was learning at MIT, I began to recognize more and more how important the issue of climate change is. And I realized there were lots of ways in which the climate problem resonated with other kinds of problems I had tackled in earlier parts of my work.There was once a time when climate and the environment was the purview primarily of white progressives: the “tree huggers.” And that’s really changed in recent decades as it has become evident that the people who’ve been most affected by the climate emergency are ethnic and racial minorities. We saw with Hurricane Katrina and other places [that] if you are Black, you’re more likely to live in a vulnerable area and to just generally experience more environmental harms, from pollution and emissions, leaving these communities much less resilient than white communities. Government has largely not addressed this inequity. When you look at American survey data in terms of who’s concerned about climate change, Black Americans, Hispanic Americans, and Asian Americans are more unified in their worries than are white Americans.There are analogous problems in Africa, my career research focus. Governments there have long responded in different ways to different ethnic groups. The research I am starting looks at the extent to which there are disparities in how governments try to solve climate-related challenges.Q: It’s difficult enough in the United States taking the measure of different groups’ perceptions of the impact of climate change and government’s effectiveness in contending with it. How do you go about this in Africa?A: Surprisingly, there’s only been a little bit of work done so far on how ordinary African citizens, who are ostensibly being hit the hardest in the world by the climate emergency, are thinking about this problem. Climate change has not been politicized there in a very big way. In fact, only 50 percent of Africans in one poll had heard of the term.In one of my new projects, with political science faculty colleague Devin Caughey and political science doctoral student Preston Johnston, we are analyzing social and climate survey data [generated by the Afrobarometer research network] from over 30 African countries to understand within and across countries the ways in which ethnic identities structure people’s perception of the climate crisis, and their beliefs in what government ought to be doing. In largely agricultural African societies, people routinely experience drought, extreme rain, and heat. They also lack the infrastructure that can shield them from the intense variability of weather patterns. But we’re adding a lens, which is looking at sources of inequality, especially ethnic differences.I will also be investigating specific sectors. Africa is a continent where in most places people cannot take for granted universal, piped access to clean water. In Cape Town, several years ago, the combination of failure to replace infrastructure and lack of rain caused such extreme conditions that one of the world’s most important cities almost ran out of water.While these studies are in progress, it is clear that in many countries, there are substantively large differences in perceptions of the severity of climate change, and attitudes about who should be doing what, and who’s capable of doing what. In several countries, both perceptions and policy preferences are differentiated along ethnic lines, more so than with respect to generational or class differences within societies.This is interesting as a phenomenon, but substantively, I think it’s important in that it may provide the basis for how politicians and government actors decide to move on allocating resources and implementing climate-protection policies. We see this kind of political calculation in the U.S. and we shouldn’t be surprised that it happens in Africa as well.That’s ultimately one of the challenges from the perch of MIT, where we’re really interested in understanding climate change, and creating technological tools and policies for mitigating the problem or adapting to it. The reality is frustrating. The political world — those who make decisions about whether to acknowledge the problem and whether to implement resources in the best technical way — are playing a whole other game. That game is about rewarding key supporters and being reelected.Q: So how do you go from measuring perceptions and beliefs among citizens about climate change and government responsiveness to those problems, to policies and actions that might actually reduce disparities in the way climate-vulnerable African groups receive support?A: Some of the work I have been doing involves understanding what local and national governments across Africa are actually doing to address these problems. We will have to drill down into government budgets to determine the actual resources devoted to addressing a challenge, what sorts of practices the government follows, and the political ramifications for governments that act aggressively versus those that don’t. With the Cape Town water crisis, for example, the government dramatically changed residents’ water usage through naming and shaming, and transformed institutional practices of water collection. They made it through a major drought by using much less water, and doing it with greater energy efficiency. Through the government’s strong policy and implementation, and citizens’ active responses, an entire city, with all its disparate groups, gained resilience. Maybe we can highlight creative solutions to major climate-related problems and use them as prods to push more effective policies and solutions in other places.In the MIT Global Diversity Lab, along with political science faculty colleague Volha Charnysh, political science doctoral student Jared Kalow, and Institute for Data, Systems and Society doctoral student Erin Walk, we are exploring American perspectives on climate-related foreign aid, asking survey respondents whether the U.S. should be giving more to people in the global South who didn’t cause the problems of climate change but have to suffer the externalities. We are particularly interested in whether people’s desire to help vulnerable communities rests on the racial or national identity of those communities.From my new seat as director of the Center for International Studies (CIS), I hope to do more and more to connect social science findings to relevant policymakers, whether in the U.S. or in other places. CIS is making climate one of our thematic priority areas, directing hundreds of thousands of dollars for MIT faculty to spark climate collaborations with researchers worldwide through the Global Seed Fund program. COP 28 (the U.N. Climate Change Conference), which I attended in December in Dubai, really drove home the importance of people coming together from around the world to exchange ideas and form networks. It was unbelievably large, with 85,000 people. But so many of us shared the belief that we are not doing enough. We need enforceable global solutions and innovation. We need ways of financing. We need to provide opportunities for journalists to broadcast the importance of this problem. And we need to understand the incentives that different actors have and what sorts of messages and strategies will resonate with them, and inspire those who have resources to be more generous. More

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      Q&A: How refusal can be an act of design

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      This month in the ACM Journal on Responsible Computing, MIT graduate student Jonathan Zong SM ’20 and co-author J. Nathan Matias SM ’13, PhD ’17 of the Cornell Citizens and Technology Lab examine how the notion of refusal can open new avenues in the field of data ethics. In their open-access report, “Data Refusal From Below: A Framework for Understanding, Evaluating, and Envisioning Refusal as Design,” the pair proposes a framework in four dimensions to map how individuals can say “no” to technology misuses. At the same time, the researchers argue that just like design, refusal is generative, and has the potential to create alternate futures.

      Zong, a PhD candidate in electrical engineering and computer science, 2022-23 MIT Morningside Academy for Design Design Fellow, and member of the MIT Visualization Group, describes his latest work in this Q&A.

      Q: How do you define the concept of “refusal,” and where does it come from?

      A: Refusal was developed in feminist and Indigenous studies. It’s this idea of saying “no,” without being given permission to say “no.” Scholars like Ruha Benjamin write about refusal in the context of surveillance, race, and bioethics, and talk about it as a necessary counterpart to consent. Others, like the authors of the “Feminist Data Manifest-No,” think of refusal as something that can help us commit to building better futures.

      Benjamin illustrates cases where the choice to refuse is not equally possible for everyone, citing examples involving genetic data and refugee screenings in the U.K. The imbalance of power in these situations underscores the broader concept of refusal, extending beyond rejecting specific options to challenging the entire set of choices presented.

      Q: What inspired you to work on the notion of refusal as an act of design?

      A: In my work on data ethics, I’ve been thinking about how to incorporate processes into research data collection, particularly around consent and opt-out, with a focus on individual autonomy and the idea of giving people choices about the way that their data is used. But when it comes to data privacy, simply making choices available is not enough. Choices can be unequally available, or create no-win situations where all options are bad. This led me to the concept of refusal: questioning the authority of data collectors and challenging their legitimacy.

      The key idea of my work is that refusal is an act of design. I think of refusal as deliberate actions to redesign our socio-technical landscape by exerting some sort of influence. Like design, refusal is generative. Like design, it’s oriented towards creating alternate possibilities and alternate futures. Design is a process of exploring or traversing a space of possibility. Applying a design framework to cases of refusal drawn from scholarly and journalistic sources allowed me to establish a common language for talking about refusal and to imagine refusals that haven’t been explored yet.

      Q: What are the stakes around data privacy and data collection?

      A: The use of data for facial recognition surveillance in the U.S. is a big example we use in the paper. When people do everyday things like post on social media or walk past cameras in public spaces, they might be contributing their data to training facial recognition systems. For instance, a tech company may take photos from a social media site and build facial recognition that they then sell to the government. In the U.S., these systems are disproportionately used by police to surveil communities of color. It is difficult to apply concepts like consent and opt out of these processes, because they happen over time and involve multiple kinds of institutions. It’s also not clear that individual opt-out would do anything to change the overall situation. Refusal then becomes a crucial avenue, at both individual and community levels, to think more broadly of how affected people still exert some kind of voice or agency, without necessarily having an official channel to do so.

      Q: Why do you think these issues are more particularly affecting disempowered communities?

      A: People who are affected by technologies are not always included in the design process for those technologies. Refusal then becomes a meaningful expression of values and priorities for those who were not part of the early design conversations. Actions taken against technologies like face surveillance — be it legal battles against companies, advocacy for stricter regulations, or even direct action like disabling security cameras — may not fit the conventional notion of participating in a design process. And yet, these are the actions available to refusers who may be excluded from other forms of participation.

      I’m particularly inspired by the movement around Indigenous data sovereignty. Organizations like the First Nations Information Governance Centre work towards prioritizing Indigenous communities’ perspectives in data collection, and refuse inadequate representation in official health data from the Canadian government. I think this is a movement that exemplifies the potential of refusal, not only as a way to reject what’s being offered, but also as a means to propose a constructive alternative, very much like design. Refusal is not merely a negation, but a pathway to different futures.

      Q: Can you elaborate on the design framework you propose?

      A: Refusals vary widely across contexts and scales. Developing a framework for refusal is about helping people see actions that are seemingly very different as instances of the same broader idea. Our framework consists of four facets: autonomy, time, power, and cost.

      Consider the case of IBM creating a facial recognition dataset using people’s photos without consent. We saw multiple forms of refusal emerge in response. IBM allowed individuals to opt out by withdrawing their photos. People collectively refused by creating a class-action lawsuit against IBM. Around the same time, many U.S. cities started passing local legislation banning the government use of facial recognition. Evaluating these cases through the framework highlights commonalities and differences. The framework highlights varied approaches to autonomy, like individual opt-out and collective action. Regarding time, opt-outs and lawsuits react to past harm, while legislation might proactively prevent future harm. Power dynamics differ; withdrawing individual photos minimally influences IBM, while legislation could potentially cause longer-term change. And as for cost, individual opt-out seems less demanding, while other approaches require more time and effort, balanced against potential benefits.

      The framework facilitates case description and comparison across these dimensions. I think its generative nature encourages exploration of novel forms of refusal as well. By identifying the characteristics we want to see in future refusal strategies — collective, proactive, powerful, low-cost… — we can aspire to shape future approaches and change the behavior of data collectors. We may not always be able to combine all these criteria, but the framework provides a means to articulate our aspirational goals in this context.

      Q: What impact do you hope this research will have?

      A: I hope to expand the notion of who can participate in design, and whose actions are seen as legitimate expressions of design input. I think a lot of work so far in the conversation around data ethics prioritizes the perspective of computer scientists who are trying to design better systems, at the expense of the perspective of people for whom the systems are not currently working. So, I hope designers and computer scientists can embrace the concept of refusal as a legitimate form of design, and a source of inspiration. There’s a vital conversation happening, one that should influence the design of future systems, even if expressed through unconventional means.

      One of the things I want to underscore in the paper is that design extends beyond software. Taking a socio-technical perspective, the act of designing encompasses software, institutions, relationships, and governance structures surrounding data use. I want people who aren’t software engineers, like policymakers or activists, to view themselves as integral to the technology design process. More

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      3 Questions: Renaud Fournier on transforming MIT’s digital landscape

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      Renaud Fournier SM ’95 joined the Institute in September 2023 in the newly established role of chief officer for business and digital transformation and is leading a team focused on simplifying business operations and systems for the MIT community. Fournier has extensive experience implementing systems and solving data challenges, both in higher education and the private sector — most recently, leading the digital transformation effort at New York University. Here, Fournier speaks about how he and his team will work closely with members of the MIT community to chart a course for MIT’s digital evolution.

      Q: What are MIT’s enterprise systems and how are they challenging for our community?

      A: The MIT community relies on our enterprise systems for a range of activities — everything from hiring and evaluating employees to managing research grants and facilities projects to maintaining student information. SAP is our current enterprise resource planning system for human resources, finance, and facilities management, and it’s integrated with other systems that provide additional business functionality. Some of these systems are purchased, like Coupa, while others are partially or fully homegrown, like Kuali Coeus and NIMBUS. Along with SAP, our other core systems — for example, Advance and MITSIS — feed data into a central data warehouse to support reporting.

      MIT’s enterprise systems and data landscape has evolved organically over 30 years. The Institute has become considerably more complicated since then, and they no longer represent the best practices or technology in the IT market.

      Q: What digital transformation projects are you most focused on?

      A: Our primary goal is to free up our community’s time so that they can achieve their greatest impact. The vision is to create easy-to-use and well-integrated systems, along with comprehensible and accessible data for reporting and analysis. To accomplish this, we will be taking a series of actions. These include modernizing our enterprise systems and data architecture to take advantage of better technology and functionality, within a cohesive and well-integrated landscape, and simplifying our business processes. To make our data accessible and actionable, we will implement more robust data governance, assigning clear ownership and accountability. And we will offer IT support that enables our community to accomplish its objectives. We need to address systems, processes, data, and support holistically, while engaging and assisting our community every step of the way.    

      Q: What are your next steps?

      A: Over the next few months, I will be building a team to guide the community on this journey, in partnership with IS&T [Information Systems and Technology], other central units, and our academic areas. Together, we will be developing a thoughtful and actionable multi-year roadmap of digital transformation projects, which will help us to produce a steady stream of improvements for our community. We have not selected any systems yet or determined the order in which they will be implemented. Engagement with stakeholders from central, academic, and research areas will inform how we prioritize projects over the next few years. Once we have created the roadmap to guide us, we look forward to the next phase — getting started on the work itself. More

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      3 Questions: A new PhD program from the Center for Computational Science and Engineering

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      This fall, the Center for Computational Science and Engineering (CCSE), an academic unit in the MIT Schwarzman College of Computing, is introducing a new standalone PhD degree program that will enable students to pursue research in cross-cutting methodological aspects of computational science and engineering. The launch follows approval of the center’s degree program proposal at the May 2023 Institute faculty meeting.

      Doctoral-level graduate study in computational science and engineering (CSE) at MIT has, for the past decade, been offered through an interdisciplinary program in which CSE students are admitted to one of eight participating academic departments in the School of Engineering or School of Science. While this model adds a strong disciplinary component to students’ education, the rapid growth of the CSE field and the establishment of the MIT Schwarzman College of Computing have prompted an exciting expansion of MIT’s graduate-level offerings in computation.

      The new degree, offered by the college, will run alongside MIT’s existing interdisciplinary offerings in CSE, complementing these doctoral training programs and preparing students to contribute to the leading edge of the field. Here, CCSE co-directors Youssef Marzouk and Nicolas Hadjiconstantinou discuss the standalone program and how they expect it to elevate the visibility and impact of CSE research and education at MIT.

      Q: What is computational science and engineering?

      Marzouk: Computational science and engineering focuses on the development and analysis of state-of-the-art methods for computation and their innovative application to problems of science and engineering interest. It has intellectual foundations in applied mathematics, statistics, and computer science, and touches the full range of science and engineering disciplines. Yet, it synthesizes these foundations into a discipline of its own — one that links the digital and physical worlds. It’s an exciting and evolving multidisciplinary field.

      Hadjiconstantinou: Examples of CSE research happening at MIT include modeling and simulation techniques, the underlying computational mathematics, and data-driven modeling of physical systems. Computational statistics and scientific machine learning have become prominent threads within CSE, joining high-performance computing, mathematically-oriented programming languages, and their broader links to algorithms and software. Application domains include energy, environment and climate, materials, health, transportation, autonomy, and aerospace, among others. Some of our researchers focus on general and widely applicable methodology, while others choose to focus on methods and algorithms motivated by a specific domain of application.

      Q: What was the motivation behind creating a standalone PhD program?

      Marzouk: The new degree focuses on a particular class of students whose background and interests are primarily in CSE methodology, in a manner that cuts across the disciplinary research structure represented by our current “with-departments” degree program. There is a strong research demand for such methodologically-focused students among CCSE faculty and MIT faculty in general. Our objective is to create a targeted, coherent degree program in this field that, alongside our other thriving CSE offerings, will create the leading environment for top CSE students worldwide.

      Hadjiconstantinou: One of CCSE’s most important functions is to recruit exceptional students who are trained in and want to work in computational science and engineering. Experience with our CSE master’s program suggests that students with a strong background and interests in the discipline prefer to apply to a pure CSE program for their graduate studies. The standalone degree aims to bring these students to MIT and make them available to faculty across the Institute.

      Q: How will this impact computing education and research at MIT? 

      Hadjiconstantinou: We believe that offering a standalone PhD program in CSE alongside the existing “with-departments” programs will significantly strengthen MIT’s graduate programs in computing. In particular, it will strengthen the methodological core of CSE research and education at MIT, while continuing to support the disciplinary-flavored CSE work taking place in our participating departments, which include Aeronautics and Astronautics; Chemical Engineering; Civil and Environmental Engineering; Materials Science and Engineering; Mechanical Engineering; Nuclear Science and Engineering; Earth, Atmospheric and Planetary Sciences; and Mathematics. Together, these programs will create a stronger CSE student cohort and facilitate deeper exchanges between the college and other units at MIT.

      Marzouk: In a broader sense, the new program is designed to help realize one of the key opportunities presented by the college, which is to create a richer variety of graduate degrees in computation and to involve as many faculty and units in these educational endeavors as possible. The standalone CSE PhD will join other distinguished doctoral programs of the college — such as the Department of Electrical Engineering and Computer Science PhD; the Operations Research Center PhD; and the Interdisciplinary Doctoral Program in Statistics and the Social and Engineering Systems PhD within the Institute for Data, Systems, and Society — and grow in a way that is informed by them. The confluence of these academic programs, and natural synergies among them, will make MIT quite unique. More

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      3 Questions: Honing robot perception and mapping

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      Walking to a friend’s house or browsing the aisles of a grocery store might feel like simple tasks, but they in fact require sophisticated capabilities. That’s because humans are able to effortlessly understand their surroundings and detect complex information about patterns, objects, and their own location in the environment.

      What if robots could perceive their environment in a similar way? That question is on the minds of MIT Laboratory for Information and Decision Systems (LIDS) researchers Luca Carlone and Jonathan How. In 2020, a team led by Carlone released the first iteration of Kimera, an open-source library that enables a single robot to construct a three-dimensional map of its environment in real time, while labeling different objects in view. Last year, Carlone’s and How’s research groups (SPARK Lab and Aerospace Controls Lab) introduced Kimera-Multi, an updated system in which multiple robots communicate among themselves in order to create a unified map. A 2022 paper associated with the project recently received this year’s IEEE Transactions on Robotics King-Sun Fu Memorial Best Paper Award, given to the best paper published in the journal in 2022.

      Carlone, who is the Leonardo Career Development Associate Professor of Aeronautics and Astronautics, and How, the Richard Cockburn Maclaurin Professor in Aeronautics and Astronautics, spoke to LIDS about Kimera-Multi and the future of how robots might perceive and interact with their environment.

      Q: Currently your labs are focused on increasing the number of robots that can work together in order to generate 3D maps of the environment. What are some potential advantages to scaling this system?

      How: The key benefit hinges on consistency, in the sense that a robot can create an independent map, and that map is self-consistent but not globally consistent. We’re aiming for the team to have a consistent map of the world; that’s the key difference in trying to form a consensus between robots as opposed to mapping independently.

      Carlone: In many scenarios it’s also good to have a bit of redundancy. For example, if we deploy a single robot in a search-and-rescue mission, and something happens to that robot, it would fail to find the survivors. If multiple robots are doing the exploring, there’s a much better chance of success. Scaling up the team of robots also means that any given task may be completed in a shorter amount of time.

      Q: What are some of the lessons you’ve learned from recent experiments, and challenges you’ve had to overcome while designing these systems?

      Carlone: Recently we did a big mapping experiment on the MIT campus, in which eight robots traversed up to 8 kilometers in total. The robots have no prior knowledge of the campus, and no GPS. Their main tasks are to estimate their own trajectory and build a map around it. You want the robots to understand the environment as humans do; humans not only understand the shape of obstacles, to get around them without hitting them, but also understand that an object is a chair, a desk, and so on. There’s the semantics part.

      The interesting thing is that when the robots meet each other, they exchange information to improve their map of the environment. For instance, if robots connect, they can leverage information to correct their own trajectory. The challenge is that if you want to reach a consensus between robots, you don’t have the bandwidth to exchange too much data. One of the key contributions of our 2022 paper is to deploy a distributed protocol, in which robots exchange limited information but can still agree on how the map looks. They don’t send camera images back and forth but only exchange specific 3D coordinates and clues extracted from the sensor data. As they continue to exchange such data, they can form a consensus.

      Right now we are building color-coded 3D meshes or maps, in which the color contains some semantic information, like “green” corresponds to grass, and “magenta” to a building. But as humans, we have a much more sophisticated understanding of reality, and we have a lot of prior knowledge about relationships between objects. For instance, if I was looking for a bed, I would go to the bedroom instead of exploring the entire house. If you start to understand the complex relationships between things, you can be much smarter about what the robot can do in the environment. We’re trying to move from capturing just one layer of semantics, to a more hierarchical representation in which the robots understand rooms, buildings, and other concepts.

      Q: What kinds of applications might Kimera and similar technologies lead to in the future?

      How: Autonomous vehicle companies are doing a lot of mapping of the world and learning from the environments they’re in. The holy grail would be if these vehicles could communicate with each other and share information, then they could improve models and maps that much quicker. The current solutions out there are individualized. If a truck pulls up next to you, you can’t see in a certain direction. Could another vehicle provide a field of view that your vehicle otherwise doesn’t have? This is a futuristic idea because it requires vehicles to communicate in new ways, and there are privacy issues to overcome. But if we could resolve those issues, you could imagine a significantly improved safety situation, where you have access to data from multiple perspectives, not only your field of view.

      Carlone: These technologies will have a lot of applications. Earlier I mentioned search and rescue. Imagine that you want to explore a forest and look for survivors, or map buildings after an earthquake in a way that can help first responders access people who are trapped. Another setting where these technologies could be applied is in factories. Currently, robots that are deployed in factories are very rigid. They follow patterns on the floor, and are not really able to understand their surroundings. But if you’re thinking about much more flexible factories in the future, robots will have to cooperate with humans and exist in a much less structured environment. More

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      Q&A: Are far-reaching fires the new normal?

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      Where there’s smoke, there is fire. But with climate change, larger and longer-burning wildfires are sending smoke farther from their source, often to places that are unaccustomed to the exposure. That’s been the case this week, as smoke continues to drift south from massive wildfires in Canada, prompting warnings of hazardous air quality, and poor visibility in states across New England, the mid-Atlantic, and the Midwest.

      As wildfire season is just getting going, many may be wondering: Are the air-polluting effects of wildfires a new normal?

      MIT News spoke with Professor Colette Heald of the Department of Civil and Environmental Engineering and the Department of Earth, Atmospheric and Planetary Sciences, and Professor Noelle Selin of the Institute for Data, Systems and Society and the Department of Earth, Atmospheric and Planetary Sciences. Heald specializes in atmospheric chemistry and has studied the climate and health effects associated with recent wildfires, while Selin works with atmospheric models to track air pollutants around the world, which she uses to inform policy decisions on mitigating  pollution and climate change. The researchers shared some of their insights on the immediate impacts of Canada’s current wildfires and what downwind regions may expect in the coming months, as the wildfire season stretches into summer.  

      Q: What role has climate change and human activity played in the wildfires we’ve seen so far this year?

      Heald: Unusually warm and dry conditions have dramatically increased fire susceptibility in Canada this year. Human-induced climate change makes such dry and warm conditions more likely. Smoke from fires in Alberta and Nova Scotia in May, and Quebec in early June, has led to some of the worst air quality conditions measured locally in Canada. This same smoke has been transported into the United States and degraded air quality here as well. Local officials have determined that ignitions have been associated with lightning strikes, but human activity has also played a role igniting some of the fires in Alberta.

      Q: What can we expect for the coming months in terms of the pattern of wildfires and their associated air pollution across the United States?

      Heald: The Government of Canada is projecting higher-than-normal fire activity throughout the 2023 fire season. Fire susceptibility will continue to respond to changing weather conditions, and whether the U.S. is impacted will depend on the winds and how air is transported across those regions. So far, the fire season in the United States has been below average, but fire risk is expected to increase modestly through the summer, so we may see local smoke influences as well.

      Q: How has air pollution from wildfires affected human health in the U.S. this year so far?

      Selin: The pollutant of most concern in wildfire smoke is fine particulate matter (PM2.5) – fine particles in the atmosphere that can be inhaled deep into the lungs, causing health damages. Exposure to PM2.5 causes respiratory and cardiovascular damage, including heart attacks and premature deaths. It can also cause symptoms like coughing and difficulty breathing. In New England this week, people have been breathing much higher concentrations of PM2.5 than usual. People who are particularly vulnerable to the effects are likely experiencing more severe impacts, such as older people and people with underlying conditions. But PM2.5 affects everyone. While the number and impact of wildfires varies from year to year, the associated air pollution from them generally lead to tens of thousands of premature deaths in the U.S. overall annually. There is also some evidence that PM2.5 from fires could be particularly damaging to health.

      While we in New England usually have relatively lower levels of pollution, it’s important also to note that some cities around the globe experience very high PM2.5 on a regular basis, not only from wildfires, but other sources such as power plants and industry. So, while we’re feeling the effects over the past few days, we should remember the broader importance of reducing PM2.5 levels overall for human health everywhere.

      Q: While firefighters battle fires directly this wildfire season, what can we do to reduce the effects of associated air pollution? And what can we do in the long-term, to prevent or reduce wildfire impacts?

      Selin: In the short term, protecting yourself from the impacts of PM2.5 is important. Limiting time outdoors, avoiding outdoor exercise, and wearing a high-quality mask are some strategies that can minimize exposure. Air filters can help reduce the concentrations of particles in indoor air. Taking measures to avoid exposure is particularly important for vulnerable groups. It’s also important to note that these strategies aren’t equally possible for everyone (for example, people who work outside) — stressing the importance of developing new strategies to address the underlying causes of increasing wildfires.

      Over the long term, mitigating climate change is important — because warm and dry conditions lead to wildfires, warming increases fire risk. Preventing the fires that are ignited by people or human activities can help.  Another way that damages can be mitigated in the longer term is by exploring land management strategies that could help manage fire intensity. More

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      3 Questions: Leo Anthony Celi on ChatGPT and medicine

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      Launched in November 2022, ChatGPT is a chatbot that can not only engage in human-like conversation, but also provide accurate answers to questions in a wide range of knowledge domains. The chatbot, created by the firm OpenAI, is based on a family of “large language models” — algorithms that can recognize, predict, and generate text based on patterns they identify in datasets containing hundreds of millions of words.

      In a study appearing in PLOS Digital Health this week, researchers report that ChatGPT performed at or near the passing threshold of the U.S. Medical Licensing Exam (USMLE) — a comprehensive, three-part exam that doctors must pass before practicing medicine in the United States. In an editorial accompanying the paper, Leo Anthony Celi, a principal research scientist at MIT’s Institute for Medical Engineering and Science, a practicing physician at Beth Israel Deaconess Medical Center, and an associate professor at Harvard Medical School, and his co-authors argue that ChatGPT’s success on this exam should be a wake-up call for the medical community.

      Q: What do you think the success of ChatGPT on the USMLE reveals about the nature of the medical education and evaluation of students? 

      A: The framing of medical knowledge as something that can be encapsulated into multiple choice questions creates a cognitive framing of false certainty. Medical knowledge is often taught as fixed model representations of health and disease. Treatment effects are presented as stable over time despite constantly changing practice patterns. Mechanistic models are passed on from teachers to students with little emphasis on how robustly those models were derived, the uncertainties that persist around them, and how they must be recalibrated to reflect advances worthy of incorporation into practice. 

      ChatGPT passed an examination that rewards memorizing the components of a system rather than analyzing how it works, how it fails, how it was created, how it is maintained. Its success demonstrates some of the shortcomings in how we train and evaluate medical students. Critical thinking requires appreciation that ground truths in medicine continually shift, and more importantly, an understanding how and why they shift.

      Q: What steps do you think the medical community should take to modify how students are taught and evaluated?  

      A: Learning is about leveraging the current body of knowledge, understanding its gaps, and seeking to fill those gaps. It requires being comfortable with and being able to probe the uncertainties. We fail as teachers by not teaching students how to understand the gaps in the current body of knowledge. We fail them when we preach certainty over curiosity, and hubris over humility.  

      Medical education also requires being aware of the biases in the way medical knowledge is created and validated. These biases are best addressed by optimizing the cognitive diversity within the community. More than ever, there is a need to inspire cross-disciplinary collaborative learning and problem-solving. Medical students need data science skills that will allow every clinician to contribute to, continually assess, and recalibrate medical knowledge.

      Q: Do you see any upside to ChatGPT’s success in this exam? Are there beneficial ways that ChatGPT and other forms of AI can contribute to the practice of medicine? 

      A: There is no question that large language models (LLMs) such as ChatGPT are very powerful tools in sifting through content beyond the capabilities of experts, or even groups of experts, and extracting knowledge. However, we will need to address the problem of data bias before we can leverage LLMs and other artificial intelligence technologies. The body of knowledge that LLMs train on, both medical and beyond, is dominated by content and research from well-funded institutions in high-income countries. It is not representative of most of the world.

      We have also learned that even mechanistic models of health and disease may be biased. These inputs are fed to encoders and transformers that are oblivious to these biases. Ground truths in medicine are continuously shifting, and currently, there is no way to determine when ground truths have drifted. LLMs do not evaluate the quality and the bias of the content they are being trained on. Neither do they provide the level of uncertainty around their output. But the perfect should not be the enemy of the good. There is tremendous opportunity to improve the way health care providers currently make clinical decisions, which we know are tainted with unconscious bias. I have no doubt AI will deliver its promise once we have optimized the data input. More

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      Q&A: A fresh look at data science

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

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

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

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

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

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

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

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

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

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

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

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

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

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

      Q: How did you keep focus throughout the process?

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

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

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

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

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