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    “They can see themselves shaping the world they live in”

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    During the journey from the suburbs to the city, the tree canopy often dwindles down as skyscrapers rise up. A group of New England Innovation Academy students wondered why that is.“Our friend Victoria noticed that where we live in Marlborough there are lots of trees in our own backyards. But if you drive just 30 minutes to Boston, there are almost no trees,” said high school junior Ileana Fournier. “We were struck by that duality.”This inspired Fournier and her classmates Victoria Leeth and Jessie Magenyi to prototype a mobile app that illustrates Massachusetts deforestation trends for Day of AI, a free, hands-on curriculum developed by the MIT Responsible AI for Social Empowerment and Education (RAISE) initiative, headquartered in the MIT Media Lab and in collaboration with the MIT Schwarzman College of Computing and MIT Open Learning. They were among a group of 20 students from New England Innovation Academy who shared their projects during the 2024 Day of AI global celebration hosted with the Museum of Science.The Day of AI curriculum introduces K-12 students to artificial intelligence. Now in its third year, Day of AI enables students to improve their communities and collaborate on larger global challenges using AI. Fournier, Leeth, and Magenyi’s TreeSavers app falls under the Telling Climate Stories with Data module, one of four new climate-change-focused lessons.“We want you to be able to express yourselves creatively to use AI to solve problems with critical-thinking skills,” Cynthia Breazeal, director of MIT RAISE, dean for digital learning at MIT Open Learning, and professor of media arts and sciences, said during this year’s Day of AI global celebration at the Museum of Science. “We want you to have an ethical and responsible way to think about this really powerful, cool, and exciting technology.”Moving from understanding to actionDay of AI invites students to examine the intersection of AI and various disciplines, such as history, civics, computer science, math, and climate change. With the curriculum available year-round, more than 10,000 educators across 114 countries have brought Day of AI activities to their classrooms and homes.The curriculum gives students the agency to evaluate local issues and invent meaningful solutions. “We’re thinking about how to create tools that will allow kids to have direct access to data and have a personal connection that intersects with their lived experiences,” Robert Parks, curriculum developer at MIT RAISE, said at the Day of AI global celebration.Before this year, first-year Jeremie Kwapong said he knew very little about AI. “I was very intrigued,” he said. “I started to experiment with ChatGPT to see how it reacts. How close can I get this to human emotion? What is AI’s knowledge compared to a human’s knowledge?”In addition to helping students spark an interest in AI literacy, teachers around the world have told MIT RAISE that they want to use data science lessons to engage students in conversations about climate change. Therefore, Day of AI’s new hands-on projects use weather and climate change to show students why it’s important to develop a critical understanding of dataset design and collection when observing the world around them.“There is a lag between cause and effect in everyday lives,” said Parks. “Our goal is to demystify that, and allow kids to access data so they can see a long view of things.”Tools like MIT App Inventor — which allows anyone to create a mobile application — help students make sense of what they can learn from data. Fournier, Leeth, and Magenyi programmed TreeSavers in App Inventor to chart regional deforestation rates across Massachusetts, identify ongoing trends through statistical models, and predict environmental impact. The students put that “long view” of climate change into practice when developing TreeSavers’ interactive maps. Users can toggle between Massachusetts’s current tree cover, historical data, and future high-risk areas.Although AI provides fast answers, it doesn’t necessarily offer equitable solutions, said David Sittenfeld, director of the Center for the Environment at the Museum of Science. The Day of AI curriculum asks students to make decisions on sourcing data, ensuring unbiased data, and thinking responsibly about how findings could be used.“There’s an ethical concern about tracking people’s data,” said Ethan Jorda, a New England Innovation Academy student. His group used open-source data to program an app that helps users track and reduce their carbon footprint.Christine Cunningham, senior vice president of STEM Learning at the Museum of Science, believes students are prepared to use AI responsibly to make the world a better place. “They can see themselves shaping the world they live in,” said Cunningham. “Moving through from understanding to action, kids will never look at a bridge or a piece of plastic lying on the ground in the same way again.”Deepening collaboration on earth and beyondThe 2024 Day of AI speakers emphasized collaborative problem solving at the local, national, and global levels.“Through different ideas and different perspectives, we’re going to get better solutions,” said Cunningham. “How do we start young enough that every child has a chance to both understand the world around them but also to move toward shaping the future?”Presenters from MIT, the Museum of Science, and NASA approached this question with a common goal — expanding STEM education to learners of all ages and backgrounds.“We have been delighted to collaborate with the MIT RAISE team to bring this year’s Day of AI celebration to the Museum of Science,” says Meg Rosenburg, manager of operations at the Museum of Science Centers for Public Science Learning. “This opportunity to highlight the new climate modules for the curriculum not only perfectly aligns with the museum’s goals to focus on climate and active hope throughout our Year of the Earthshot initiative, but it has also allowed us to bring our teams together and grow a relationship that we are very excited to build upon in the future.”Rachel Connolly, systems integration and analysis lead for NASA’s Science Activation Program, showed the power of collaboration with the example of how human comprehension of Saturn’s appearance has evolved. From Galileo’s early telescope to the Cassini space probe, modern imaging of Saturn represents 400 years of science, technology, and math working together to further knowledge.“Technologies, and the engineers who built them, advance the questions we’re able to ask and therefore what we’re able to understand,” said Connolly, research scientist at MIT Media Lab.New England Innovation Academy students saw an opportunity for collaboration a little closer to home. Emmett Buck-Thompson, Jeff Cheng, and Max Hunt envisioned a social media app to connect volunteers with local charities. Their project was inspired by Buck-Thompson’s father’s difficulties finding volunteering opportunities, Hunt’s role as the president of the school’s Community Impact Club, and Cheng’s aspiration to reduce screen time for social media users. Using MIT App Inventor, ​their combined ideas led to a prototype with the potential to make a real-world impact in their community.The Day of AI curriculum teaches the mechanics of AI, ethical considerations and responsible uses, and interdisciplinary applications for different fields. It also empowers students to become creative problem solvers and engaged citizens in their communities and online. From supporting volunteer efforts to encouraging action for the state’s forests to tackling the global challenge of climate change, today’s students are becoming tomorrow’s leaders with Day of AI.“We want to empower you to know that this is a tool you can use to make your community better, to help people around you with this technology,” said Breazeal.Other Day of AI speakers included Tim Ritchie, president of the Museum of Science; Michael Lawrence Evans, program director of the Boston Mayor’s Office of New Urban Mechanics; Dava Newman, director of the MIT Media Lab; and Natalie Lao, executive director of the App Inventor Foundation. More

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      MIT researchers introduce generative AI for databases

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      A new tool makes it easier for database users to perform complicated statistical analyses of tabular data without the need to know what is going on behind the scenes.GenSQL, a generative AI system for databases, could help users make predictions, detect anomalies, guess missing values, fix errors, or generate synthetic data with just a few keystrokes.For instance, if the system were used to analyze medical data from a patient who has always had high blood pressure, it could catch a blood pressure reading that is low for that particular patient but would otherwise be in the normal range.GenSQL automatically integrates a tabular dataset and a generative probabilistic AI model, which can account for uncertainty and adjust their decision-making based on new data.Moreover, GenSQL can be used to produce and analyze synthetic data that mimic the real data in a database. This could be especially useful in situations where sensitive data cannot be shared, such as patient health records, or when real data are sparse.This new tool is built on top of SQL, a programming language for database creation and manipulation that was introduced in the late 1970s and is used by millions of developers worldwide.“Historically, SQL taught the business world what a computer could do. They didn’t have to write custom programs, they just had to ask questions of a database in high-level language. We think that, when we move from just querying data to asking questions of models and data, we are going to need an analogous language that teaches people the coherent questions you can ask a computer that has a probabilistic model of the data,” says Vikash Mansinghka ’05, MEng ’09, PhD ’09, senior author of a paper introducing GenSQL and a principal research scientist and leader of the Probabilistic Computing Project in the MIT Department of Brain and Cognitive Sciences.When the researchers compared GenSQL to popular, AI-based approaches for data analysis, they found that it was not only faster but also produced more accurate results. Importantly, the probabilistic models used by GenSQL are explainable, so users can read and edit them.“Looking at the data and trying to find some meaningful patterns by just using some simple statistical rules might miss important interactions. You really want to capture the correlations and the dependencies of the variables, which can be quite complicated, in a model. With GenSQL, we want to enable a large set of users to query their data and their model without having to know all the details,” adds lead author Mathieu Huot, a research scientist in the Department of Brain and Cognitive Sciences and member of the Probabilistic Computing Project.They are joined on the paper by Matin Ghavami and Alexander Lew, MIT graduate students; Cameron Freer, a research scientist; Ulrich Schaechtel and Zane Shelby of Digital Garage; Martin Rinard, an MIT professor in the Department of Electrical Engineering and Computer Science and member of the Computer Science and Artificial Intelligence Laboratory (CSAIL); and Feras Saad ’15, MEng ’16, PhD ’22, an assistant professor at Carnegie Mellon University. The research was recently presented at the ACM Conference on Programming Language Design and Implementation.Combining models and databasesSQL, which stands for structured query language, is a programming language for storing and manipulating information in a database. In SQL, people can ask questions about data using keywords, such as by summing, filtering, or grouping database records.However, querying a model can provide deeper insights, since models can capture what data imply for an individual. For instance, a female developer who wonders if she is underpaid is likely more interested in what salary data mean for her individually than in trends from database records.The researchers noticed that SQL didn’t provide an effective way to incorporate probabilistic AI models, but at the same time, approaches that use probabilistic models to make inferences didn’t support complex database queries.They built GenSQL to fill this gap, enabling someone to query both a dataset and a probabilistic model using a straightforward yet powerful formal programming language.A GenSQL user uploads their data and probabilistic model, which the system automatically integrates. Then, she can run queries on data that also get input from the probabilistic model running behind the scenes. This not only enables more complex queries but can also provide more accurate answers.For instance, a query in GenSQL might be something like, “How likely is it that a developer from Seattle knows the programming language Rust?” Just looking at a correlation between columns in a database might miss subtle dependencies. Incorporating a probabilistic model can capture more complex interactions.   Plus, the probabilistic models GenSQL utilizes are auditable, so people can see which data the model uses for decision-making. In addition, these models provide measures of calibrated uncertainty along with each answer.For instance, with this calibrated uncertainty, if one queries the model for predicted outcomes of different cancer treatments for a patient from a minority group that is underrepresented in the dataset, GenSQL would tell the user that it is uncertain, and how uncertain it is, rather than overconfidently advocating for the wrong treatment.Faster and more accurate resultsTo evaluate GenSQL, the researchers compared their system to popular baseline methods that use neural networks. GenSQL was between 1.7 and 6.8 times faster than these approaches, executing most queries in a few milliseconds while providing more accurate results.They also applied GenSQL in two case studies: one in which the system identified mislabeled clinical trial data and the other in which it generated accurate synthetic data that captured complex relationships in genomics.Next, the researchers want to apply GenSQL more broadly to conduct largescale modeling of human populations. With GenSQL, they can generate synthetic data to draw inferences about things like health and salary while controlling what information is used in the analysis.They also want to make GenSQL easier to use and more powerful by adding new optimizations and automation to the system. In the long run, the researchers want to enable users to make natural language queries in GenSQL. Their goal is to eventually develop a ChatGPT-like AI expert one could talk to about any database, which grounds its answers using GenSQL queries.   This research is funded, in part, by the Defense Advanced Research Projects Agency (DARPA), Google, and the Siegel Family Foundation. More

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      Arvind, longtime MIT professor and prolific computer scientist, dies at 77

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      Arvind Mithal, the Charles W. and Jennifer C. Johnson Professor in Computer Science and Engineering at MIT, head of the faculty of computer science in the Department of Electrical Engineering and Computer Science (EECS), and a pillar of the MIT community, died on June 17. Arvind, who went by the mononym, was 77 years old.A prolific researcher who led the Computation Structures Group in the Computer Science and Artificial Intelligence Laboratory (CSAIL), Arvind served on the MIT faculty for nearly five decades.“He was beloved by countless people across the MIT community and around the world who were inspired by his intellectual brilliance and zest for life,” President Sally Kornbluth wrote in a letter to the MIT community today.As a scientist, Arvind was well known for important contributions to dataflow computing, which seeks to optimize the flow of data to take advantage of parallelism, achieving faster and more efficient computation.In the last 25 years, his research interests broadened to include developing techniques and tools for formal modeling, high-level synthesis, and formal verification of complex digital devices like microprocessors and hardware accelerators, as well as memory models and cache coherence protocols for parallel computing architectures and programming languages.Those who knew Arvind describe him as a rare individual whose interests and expertise ranged from high-level, theoretical formal systems all the way down through languages and compilers to the gates and structures of silicon hardware.The applications of Arvind’s work are far-reaching, from reducing the amount of energy and space required by data centers to streamlining the design of more efficient multicore computer chips.“Arvind was both a tremendous scholar in the fields of computer architecture and programming languages and a dedicated teacher, who brought systems-level thinking to our students. He was also an exceptional academic leader, often leading changes in curriculum and contributing to the Engineering Council in meaningful and impactful ways. I will greatly miss his sage advice and wisdom,” says Anantha Chandrakasan, chief innovation and strategy officer, dean of engineering, and the Vannevar Bush Professor of Electrical Engineering and Computer Science.“Arvind’s positive energy, together with his hearty laugh, brightened so many people’s lives. He was an enduring source of wise counsel for colleagues and for generations of students. With his deep commitment to academic excellence, he not only transformed research in computer architecture and parallel computing but also brought that commitment to his role as head of the computer science faculty in the EECS department. He left a lasting impact on all of us who had the privilege of working with him,” says Dan Huttenlocher, dean of the MIT Schwarzman College of Computing and the Henry Ellis Warren Professor of Electrical Engineering and Computer Science.Arvind developed an interest in parallel computing while he was a student at the Indian Institute of Technology in Kanpur, from which he received his bachelor’s degree in 1969. He earned a master’s degree and PhD in computer science in 1972 and 1973, respectively, from the University of Minnesota, where he studied operating systems and mathematical models of program behavior. He taught at the University of California at Irvine from 1974 to 1978 before joining the faculty at MIT.At MIT, Arvind’s group studied parallel computing and declarative programming languages, and he led the development of two parallel computing languages, Id and pH. He continued his work on these programming languages through the 1990s, publishing the book “Implicit Parallel Programming in pH” with co-author R.S. Nikhil in 2001, the culmination of more than 20 years of research.In addition to his research, Arvind was an important academic leader in EECS. He served as head of computer science faculty in the department and played a critical role in helping with the reorganization of EECS after the establishment of the MIT Schwarzman College of Computing.“Arvind was a force of nature, larger than life in every sense. His relentless positivity, unwavering optimism, boundless generosity, and exceptional strength as a researcher was truly inspiring and left a profound mark on all who had the privilege of knowing him. I feel enormous gratitude for the light he brought into our lives and his fundamental impact on our community,” says Daniela Rus, the Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science and the director of CSAIL.His work on dataflow and parallel computing led to the Monsoon project in the late 1980s and early 1990s. Arvind’s group, in collaboration with Motorola, built 16 dataflow computing machines and developed their associated software. One Monsoon dataflow machine is now in the Computer History Museum in Mountain View, California.Arvind’s focus shifted in the 1990s when, as he explained in a 2012 interview for the Institute of Electrical and Electronics Engineers (IEEE), funding for research into parallel computing began to dry up.“Microprocessors were getting so much faster that people thought they didn’t need it,” he recalled.Instead, he began applying techniques his team had learned and developed for parallel programming to the principled design of digital hardware.In addition to mentoring students and junior colleagues at MIT, Arvind also advised universities and governments in many countries on research in parallel programming and semiconductor design.Based on his work on digital hardware design, Arvind founded Sandburst in 2000, a fabless manufacturing company for semiconductor chips. He served as the company’s president for two years before returning to the MIT faculty, while continuing as an advisor. Sandburst was later acquired by Broadcom.Arvind and his students also developed Bluespec, a programming language designed to automate the design of chips. Building off this work, he co-founded the startup Bluespec, Inc., in 2003, to develop practical tools that help engineers streamline device design.Over the past decade, he was dedicated to advancing undergraduate education at MIT by bringing modern design tools to courses 6.004 (Computation Structures) and 6.191 (Introduction to Deep Learning), and incorporating Minispec, a programming language that is closely related to Bluespec.Arvind was honored for these and other contributions to data flow and multithread computing, and the development of tools for the high-level synthesis of hardware, with membership in the National Academy of Engineering in 2008 and the American Academy of Arts and Sciences in 2012. He was also named a distinguished alumnus of IIT Kanpur, his undergraduate alma mater.“Arvind was more than a pillar of the EECS community and a titan of computer science; he was a beloved colleague and a treasured friend. Those of us with the remarkable good fortune to work and collaborate with Arvind are devastated by his sudden loss. His kindness and joviality were unwavering; his mentorship was thoughtful and well-considered; his guidance was priceless. We will miss Arvind deeply,” says Asu Ozdaglar, deputy dean of the MIT Schwarzman College of Computing and head of EECS.Among numerous other awards, including membership in the Indian National Academy of Sciences and fellowship in the Association for Computing Machinery and IEEE, he received the Harry H. Goode Memorial Award from IEEE in 2012, which honors significant contributions to theory or practice in the information processing field.A humble scientist, Arvind was the first to point out that these achievements were only possible because of his outstanding and brilliant collaborators. Chief among those collaborators were the undergraduate and graduate students he felt fortunate to work with at MIT. He maintained excellent relationships with them both professionally and personally, and valued these relationships more than the work they did together, according to family members.In summing up the key to his scientific success, Arvind put it this way in the 2012 IEEE interview: “Really, one has to do what one believes in. I think the level at which most of us work, it is not sustainable if you don’t enjoy it on a day-to-day basis. You can’t work on it just because of the results. You have to work on it because you say, ‘I have to know the answer to this,’” he said.He is survived by his wife, Gita Singh Mithal, their two sons Divakar ’01 and Prabhakar ’04, their wives Leena and Nisha, and two grandchildren, Maya and Vikram.  More

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      Fostering research, careers, and community in materials science

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      Gabrielle Wood, a junior at Howard University majoring in chemical engineering, is on a mission to improve the sustainability and life cycles of natural resources and materials. Her work in the Materials Initiative for Comprehensive Research Opportunity (MICRO) program has given her hands-on experience with many different aspects of research, including MATLAB programming, experimental design, data analysis, figure-making, and scientific writing.Wood is also one of 10 undergraduates from 10 universities around the United States to participate in the first MICRO Summit earlier this year. The internship program, developed by the MIT Department of Materials Science and Engineering (DMSE), first launched in fall 2021. Now in its third year, the program continues to grow, providing even more opportunities for non-MIT undergraduate students — including the MICRO Summit and the program’s expansion to include Northwestern University.“I think one of the most valuable aspects of the MICRO program is the ability to do research long term with an experienced professor in materials science and engineering,” says Wood. “My school has limited opportunities for undergraduate research in sustainable polymers, so the MICRO program allowed me to gain valuable experience in this field, which I would not otherwise have.”Like Wood, Griheydi Garcia, a senior chemistry major at Manhattan College, values the exposure to materials science, especially since she is not able to learn as much about it at her home institution.“I learned a lot about crystallography and defects in materials through the MICRO curriculum, especially through videos,” says Garcia. “The research itself is very valuable, as well, because we get to apply what we’ve learned through the videos in the research we do remotely.”Expanding research opportunitiesFrom the beginning, the MICRO program was designed as a fully remote, rigorous education and mentoring program targeted toward students from underserved backgrounds interested in pursuing graduate school in materials science or related fields. Interns are matched with faculty to work on their specific research interests.Jessica Sandland ’99, PhD ’05, principal lecturer in DMSE and co-founder of MICRO, says that research projects for the interns are designed to be work that they can do remotely, such as developing a machine-learning algorithm or a data analysis approach.“It’s important to note that it’s not just about what the program and faculty are bringing to the student interns,” says Sandland, a member of the MIT Digital Learning Lab, a joint program between MIT Open Learning and the Institute’s academic departments. “The students are doing real research and work, and creating things of real value. It’s very much an exchange.”Cécile Chazot PhD ’22, now an assistant professor of materials science and engineering at Northwestern University, had helped to establish MICRO at MIT from the very beginning. Once at Northwestern, she quickly realized that expanding MICRO to Northwestern would offer even more research opportunities to interns than by relying on MIT alone — leveraging the university’s strong materials science and engineering department, as well as offering resources for biomaterials research through Northwestern’s medical school. The program received funding from 3M and officially launched at Northwestern in fall 2023. Approximately half of the MICRO interns are now in the program with MIT and half are with Northwestern. Wood and Garcia both participate in the program via Northwestern.“By expanding to another school, we’ve been able to have interns work with a much broader range of research projects,” says Chazot. “It has become easier for us to place students with faculty and research that match their interests.”Building communityThe MICRO program received a Higher Education Innovation grant from the Abdul Latif Jameel World Education Lab, part of MIT Open Learning, to develop an in-person summit. In January 2024, interns visited MIT for three days of presentations, workshops, and campus tours — including a tour of the MIT.nano building — as well as various community-building activities.“A big part of MICRO is the community,” says Chazot. “A highlight of the summit was just seeing the students come together.”The summit also included panel discussions that allowed interns to gain insights and advice from graduate students and professionals. The graduate panel discussion included MIT graduate students Sam Figueroa (mechanical engineering), Isabella Caruso (DMSE), and Eliana Feygin (DMSE). The career panel was led by Chazot and included Jatin Patil PhD ’23, head of product at SiTration; Maureen Reitman ’90, ScD ’93, group vice president and principal engineer at Exponent; Lucas Caretta PhD ’19, assistant professor of engineering at Brown University; Raquel D’Oyen ’90, who holds a PhD from Northwestern University and is a senior engineer at Raytheon; and Ashley Kaiser MS ’19, PhD ’21, senior process engineer at 6K.Students also had an opportunity to share their work with each other through research presentations. Their presentations covered a wide range of topics, including: developing a computer program to calculate solubility parameters for polymers used in textile manufacturing; performing a life-cycle analysis of a photonic chip and evaluating its environmental impact in comparison to a standard silicon microchip; and applying machine learning algorithms to scanning transmission electron microscopy images of CrSBr, a two-dimensional magnetic material. “The summit was wonderful and the best academic experience I have had as a first-year college student,” says MICRO intern Gabriella La Cour, who is pursuing a major in chemistry and dual degree biomedical engineering at Spelman College and participates in MICRO through MIT. “I got to meet so many students who were all in grades above me … and I learned a little about how to navigate college as an upperclassman.” “I actually have an extremely close friendship with one of the students, and we keep in touch regularly,” adds La Cour. “Professor Chazot gave valuable advice about applications and recommendation letters that will be useful when I apply to REUs [Research Experiences for Undergraduates] and graduate schools.”Looking to the future, MICRO organizers hope to continue to grow the program’s reach.“We would love to see other schools taking on this model,” says Sandland. “There are a lot of opportunities out there. The more departments, research groups, and mentors that get involved with this program, the more impact it can have.” More

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      “We offer another place for knowledge”

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      In the Dzaleka Refugee Camp in Malawi, Jospin Hassan didn’t have access to the education opportunities he sought. So, he decided to create his own. 

      Hassan knew the booming fields of data science and artificial intelligence could bring job opportunities to his community and help solve local challenges. After earning a spot in the 2020-21 cohort of the Certificate Program in Computer and Data Science from MIT Refugee Action Hub (ReACT), Hassan started sharing MIT knowledge and skills with other motivated learners in Dzaleka.

      MIT ReACT is now Emerging Talent, part of the Jameel World Education Lab (J-WEL) at MIT Open Learning. Currently serving its fifth cohort of global learners, Emerging Talent’s year-long certificate program incorporates high-quality computer science and data analysis coursework from MITx, professional skill building, experiential learning, apprenticeship work, and opportunities for networking with MIT’s global community of innovators. Hassan’s cohort honed their leadership skills through interactive online workshops with J-WEL and the 10-week online MIT Innovation Leadership Bootcamp. 

      “My biggest takeaway was networking, collaboration, and learning from each other,” Hassan says.

      Today, Hassan’s organization ADAI Circle offers mentorship and education programs for youth and other job seekers in the Dzaleka Refugee Camp. The curriculum encourages hands-on learning and collaboration.

      Launched in 2020, ADAI Circle aims to foster job creation and reduce poverty in Malawi through technology and innovation. In addition to their classes in data science, AI, software development, and hardware design, their Innovation Hub offers internet access to anyone in need. 

      Doing something different in the community

      Hassan first had the idea for his organization in 2018 when he reached a barrier in his own education journey. There were several programs in the Dzaleka Refugee Camp teaching learners how to code websites and mobile apps, but Hassan felt that they were limited in scope. 

      “We had good devices and internet access,” he says, “but I wanted to learn something new.” 

      Teaming up with co-founder Patrick Byamasu, Hassan and Byamasu set their sights on the longevity of AI and how that might create more jobs for people in their community. “The world is changing every day, and data scientists are in a higher demand today in various companies,” Hassan says. “For this reason, I decided to expand and share the knowledge that I acquired with my fellow refugees and the surrounding villages.”

      ADAI Circle draws inspiration from Hassan’s own experience with MIT Emerging Talent coursework, community, and training opportunities. For example, the MIT Bootcamps model is now standard practice for ADAI Circle’s annual hackathon. Hassan first introduced the hackathon to ADAI Circle students as part of his final experiential learning project of the Emerging Talent certificate program. 

      ADAI Circle’s annual hackathon is now an interactive — and effective — way to select students who will most benefit from its programs. The local schools’ curricula, Hassan says, might not provide enough of an academic challenge. “We can’t teach everyone and accommodate everyone because there are a lot of schools,” Hassan says, “but we offer another place for knowledge.” 

      The hackathon helps students develop data science and robotics skills. Before they start coding, students have to convince ADAI Circle teachers that their designs are viable, answering questions like, “What problem are you solving?” and “How will this help the community?” A community-oriented mindset is just as important to the curriculum.

      In addition to the practical skills Hassan gained from Emerging Talent, he leveraged the program’s network to help his community. Thanks to a social media connection Hassan made with the nongovernmental organization Give Internet after one of Emerging Talent’s virtual events, Give Internet brought internet access to ADAI Circle.

      Bridging the AI gap to unmet communities

      In 2023, ADAI Circle connected with another MIT Open Learning program, Responsible AI for Social Empowerment and Education (RAISE), which led to a pilot test of a project-based AI curriculum for middle school students. The Responsible AI for Computational Action (RAICA) curriculum equipped ADAI Circle students with AI skills for chatbots and natural language processing. 

      “I liked that program because it was based on what we’re teaching at the center,” Hassan says, speaking of his organization’s mission of bridging the AI gap to reach unmet communities.

      The RAICA curriculum was designed by education experts at MIT Scheller Teacher Education Program (STEP Lab) and AI experts from MIT Personal Robots group and MIT App Inventor. ADAI Circle teachers gave detailed feedback about the pilot to the RAICA team. During weekly meetings with Glenda Stump, education research scientist for RAICA and J-WEL, and Angela Daniel, teacher development specialist for RAICA, the teachers discussed their experiences, prepared for upcoming lessons, and translated the learning materials in real time. 

      “We are trying to create a curriculum that’s accessible worldwide and to students who typically have little or no access to technology,” says Mary Cate Gustafson-Quiett, curriculum design manager at STEP Lab and project manager for RAICA. “Working with ADAI and students in a refugee camp challenged us to design in more culturally and technologically inclusive ways.”

      Gustafson-Quiett says the curriculum feedback from ADAI Circle helped inform how RAICA delivers teacher development resources to accommodate learning environments with limited internet access. “They also exposed places where our team’s western ideals, specifically around individualism, crept into activities in the lesson and contrasted with their more communal cultural beliefs,” she says.

      Eager to introduce more MIT-developed AI resources, Hassan also shared MIT RAISE’s Day of AI curricula with ADAI Circle teachers. The new ChatGPT module gave students the chance to level up their chatbot programming skills that they gained from the RAICA module. Some of the advanced students are taking initiative to use ChatGPT API to create their own projects in education.

      “We don’t want to tell them what to do, we want them to come up with their own ideas,” Hassan says.

      Although ADAI Circle faces many challenges, Hassan says his team is addressing them one by one. Last year, they didn’t have electricity in their Innovation Hub, but they solved that. This year, they achieved a stable internet connection that’s one of the fastest in Malawi. Next up, they are hoping to secure more devices for their students, create more jobs, and add additional hubs throughout the community. The work is never done, but Hassan is starting to see the impact that ADAI Circle is making. 

      “For those who want to learn data science, let’s let them learn,” Hassan says. More

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      Generating opportunities with generative AI

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      Talking with retail executives back in 2010, Rama Ramakrishnan came to two realizations. First, although retail systems that offered customers personalized recommendations were getting a great deal of attention, these systems often provided little payoff for retailers. Second, for many of the firms, most customers shopped only once or twice a year, so companies didn’t really know much about them.

      “But by being very diligent about noting down the interactions a customer has with a retailer or an e-commerce site, we can create a very nice and detailed composite picture of what that person does and what they care about,” says Ramakrishnan, professor of the practice at the MIT Sloan School of Management. “Once you have that, then you can apply proven algorithms from machine learning.”

      These realizations led Ramakrishnan to found CQuotient, a startup whose software has now become the foundation for Salesforce’s widely adopted AI e-commerce platform. “On Black Friday alone, CQuotient technology probably sees and interacts with over a billion shoppers on a single day,” he says.

      After a highly successful entrepreneurial career, in 2019 Ramakrishnan returned to MIT Sloan, where he had earned master’s and PhD degrees in operations research in the 1990s. He teaches students “not just how these amazing technologies work, but also how do you take these technologies and actually put them to use pragmatically in the real world,” he says.

      Additionally, Ramakrishnan enjoys participating in MIT executive education. “This is a great opportunity for me to convey the things that I have learned, but also as importantly, to learn what’s on the minds of these senior executives, and to guide them and nudge them in the right direction,” he says.

      For example, executives are understandably concerned about the need for massive amounts of data to train machine learning systems. He can now guide them to a wealth of models that are pre-trained for specific tasks. “The ability to use these pre-trained AI models, and very quickly adapt them to your particular business problem, is an incredible advance,” says Ramakrishnan.

      Rama Ramakrishnan – Utilizing AI in Real World Applications for Intelligent WorkVideo: MIT Industrial Liaison Program

      Understanding AI categories

      “AI is the quest to imbue computers with the ability to do cognitive tasks that typically only humans can do,” he says. Understanding the history of this complex, supercharged landscape aids in exploiting the technologies.

      The traditional approach to AI, which basically solved problems by applying if/then rules learned from humans, proved useful for relatively few tasks. “One reason is that we can do lots of things effortlessly, but if asked to explain how we do them, we can’t actually articulate how we do them,” Ramakrishnan comments. Also, those systems may be baffled by new situations that don’t match up to the rules enshrined in the software.

      Machine learning takes a dramatically different approach, with the software fundamentally learning by example. “You give it lots of examples of inputs and outputs, questions and answers, tasks and responses, and get the computer to automatically learn how to go from the input to the output,” he says. Credit scoring, loan decision-making, disease prediction, and demand forecasting are among the many tasks conquered by machine learning.

      But machine learning only worked well when the input data was structured, for instance in a spreadsheet. “If the input data was unstructured, such as images, video, audio, ECGs, or X-rays, it wasn’t very good at going from that to a predicted output,” Ramakrishnan says. That means humans had to manually structure the unstructured data to train the system.

      Around 2010 deep learning began to overcome that limitation, delivering the ability to directly work with unstructured input data, he says. Based on a longstanding AI strategy known as neural networks, deep learning became practical due to the global flood tide of data, the availability of extraordinarily powerful parallel processing hardware called graphics processing units (originally invented for video games) and advances in algorithms and math.

      Finally, within deep learning, the generative AI software packages appearing last year can create unstructured outputs, such as human-sounding text, images of dogs, and three-dimensional models. Large language models (LLMs) such as OpenAI’s ChatGPT go from text inputs to text outputs, while text-to-image models such as OpenAI’s DALL-E can churn out realistic-appearing images.

      Rama Ramakrishnan – Making Note of Little Data to Improve Customer ServiceVideo: MIT Industrial Liaison Program

      What generative AI can (and can’t) do

      Trained on the unimaginably vast text resources of the internet, a LLM’s “fundamental capability is to predict the next most likely, most plausible word,” Ramakrishnan says. “Then it attaches the word to the original sentence, predicts the next word again, and keeps on doing it.”

      “To the surprise of many, including a lot of researchers, an LLM can do some very complicated things,” he says. “It can compose beautifully coherent poetry, write Seinfeld episodes, and solve some kinds of reasoning problems. It’s really quite remarkable how next-word prediction can lead to these amazing capabilities.”

      “But you have to always keep in mind that what it is doing is not so much finding the correct answer to your question as finding a plausible answer your question,” Ramakrishnan emphasizes. Its content may be factually inaccurate, irrelevant, toxic, biased, or offensive.

      That puts the burden on users to make sure that the output is correct, relevant, and useful for the task at hand. “You have to make sure there is some way for you to check its output for errors and fix them before it goes out,” he says.

      Intense research is underway to find techniques to address these shortcomings, adds Ramakrishnan, who expects many innovative tools to do so.

      Finding the right corporate roles for LLMs

      Given the astonishing progress in LLMs, how should industry think about applying the software to tasks such as generating content?

      First, Ramakrishnan advises, consider costs: “Is it a much less expensive effort to have a draft that you correct, versus you creating the whole thing?” Second, if the LLM makes a mistake that slips by, and the mistaken content is released to the outside world, can you live with the consequences?

      “If you have an application which satisfies both considerations, then it’s good to do a pilot project to see whether these technologies can actually help you with that particular task,” says Ramakrishnan. He stresses the need to treat the pilot as an experiment rather than as a normal IT project.

      Right now, software development is the most mature corporate LLM application. “ChatGPT and other LLMs are text-in, text-out, and a software program is just text-out,” he says. “Programmers can go from English text-in to Python text-out, as well as you can go from English-to-English or English-to-German. There are lots of tools which help you write code using these technologies.”

      Of course, programmers must make sure the result does the job properly. Fortunately, software development already offers infrastructure for testing and verifying code. “This is a beautiful sweet spot,” he says, “where it’s much cheaper to have the technology write code for you, because you can very quickly check and verify it.”

      Another major LLM use is content generation, such as writing marketing copy or e-commerce product descriptions. “Again, it may be much cheaper to fix ChatGPT’s draft than for you to write the whole thing,” Ramakrishnan says. “However, companies must be very careful to make sure there is a human in the loop.”

      LLMs also are spreading quickly as in-house tools to search enterprise documents. Unlike conventional search algorithms, an LLM chatbot can offer a conversational search experience, because it remembers each question you ask. “But again, it will occasionally make things up,” he says. “In terms of chatbots for external customers, these are very early days, because of the risk of saying something wrong to the customer.”

      Overall, Ramakrishnan notes, we’re living in a remarkable time to grapple with AI’s rapidly evolving potentials and pitfalls. “I help companies figure out how to take these very transformative technologies and put them to work, to make products and services much more intelligent, employees much more productive, and processes much more efficient,” he says. More

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      Learner in Afghanistan reaches beyond barriers to pursue career in data science

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      Tahmina S. was a junior studying computer engineering at a top university in Afghanistan when a new government policy banned women from pursuing education. In August 2021, the Taliban prohibited girls from attending school beyond the sixth grade. While women were initially allowed to continue to attend universities, by October 2021, an order from the Ministry of Higher Education declared that all women in Afghanistan were suspended from attending public and private centers of higher education.

      Determined to continue her studies and pursue her ambitions, Tahmina found the MIT Refugee Action Hub (ReACT) and was accepted to its Certificate in Computer Science and Data Science program in 2022.

      “ReACT helped me realize that I can do big things and be a part of big things,” she says.

      MIT ReACT provides education and professional opportunities to learners from refugee and forcibly displaced communities worldwide. ReACT’s core pillars include academic development, human skills development, employment pathways, and network building. Since 2017, ReACT has offered its Certificate in Computer and Data Science (CDS) program free-of-cost to learners wherever they live. In 2022, ReACT welcomed its largest and most diverse cohort to date — 136 learners from 29 countries — including 25 learners from Afghanistan, more than half of whom are women.

      Tahmina was able to select her classes in the program, and especially valued learning Python — which has led to her studying other programming languages and gaining more skills in data science. She’s continuing to take online courses in hopes of completing her undergraduate degree, and someday pursuing a masters degree in computer science and becoming a data scientist.

      “It’s an important and fun career. I really love data,” she says. “If this is my only time for this experience, I will bring to the table what I have, and do my best.”

      In addition to the education ban, Tahmina also faced the challenge of accessing an internet connection, which is expensive where she lives. But she regularly studies between 12 and 14 hours a day to achieve her dreams.

      The ReACT program offers a blend of asynchronous and synchronous learning. Learners complete a curated series of online, rigorous MIT coursework through MITx with the support of teaching assistants and collaborators, and also participate in a series of interactive online workshops in interpersonal skills that are critical to success in education and careers.

      ReACT learners engage with MIT’s global network of experts including MIT staff, faculty, and alumni — as well as collaborators across technology, humanitarian, and government sectors.

      “I loved that experience a lot, it was a huge achievement. I’m grateful ReACT gave me a chance to be a part of that team of amazing people. I’m amazed I completed that program, because it was really challenging.”

      Theory into practice

      Tahmina was one of 10 students from the ReACT cohort accepted to the highly competitive MIT Innovation Leadership Bootcamp program. She worked on a team of five people who initiated a business proposal and took the project through each phase of the development process. Her team’s project was creating an app for finance management for users aged 23-51 — including all the graphic elements and a final presentation. One valuable aspect of the boot camp, Tahmina says, was presenting their project to real investors who then provided business insights and actionable feedback.

      As part of this ReACT cohort, Tahmina also participated in the Global Apprenticeship Program (GAP) pilot, an initiative led by Talanta and with the participation of MIT Open Learning as curriculum provider. The GAP initiative focuses on improving diverse emerging talent job preparedness and exploring how companies can successfully recruit, onboard, and retain this talent through remote, paid internships. Through the GAP pilot, Tahmina received training in professional skills, resume and interview preparation, and was matched with a financial sector firm for a four-month remote internship in data science.

      To prepare Tahmina and other learners for these professional experiences, ReACT trains its cohorts to work with people who have diverse backgrounds, experiences, and challenges. The nonprofit Na’amal offered workshops covering areas such as problem-solving, innovation and ideation, goal-setting, communication, teamwork, and infrastructure and info security. Tahmina was able to access English classes and learn valuable career skills, such as writing a resume.“This was an amazing part for me. There’s a huge difference going from theoretical to practical,” she says. “Not only do you have to have the theoretical experience, you have to have soft skills. You have to communicate everything you learn to other people, because other people in the business might not have that knowledge, so you have to tell the story in a way that they can understand.”

      ReACT wanted the women in the program to be mentored by women who were not only leaders in the tech field, but working in the same geographic region as learners. At the start of the internship, Na’amal connected Tahmina with a mentor, Maha Gad, who is head of talent development at Talabat and lives in Dubai. Tahmina met with Gad at the beginning and end of each month, giving her the opportunity to ask expansive questions. Tahmina says Gad encouraged her to research and plan first, and then worked with her to explore new tools, like Trello.

      Wanting to put her skills to use locally, Tahmina volunteered at the nonprofit Rumie, a community for Afghan women and girls, working as a learning designer, translator, team leader, and social media manager. She currently volunteers at Correspondents of the World as a story ambassador, helping Afghan people share stories, community, and culture — especially telling the stories of Afghan women and the changes they’ve made in the world.

      “It’s been the most beautiful journey of my life that I will never forget,” says Tahmina. “I found ReACT at a time when I had nothing, and I found the most valuable thing.” More

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      Research, education, and connection in the face of war

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      When Russian forces invaded Ukraine in February 2022, Tetiana Herasymova had several decisions to make: What should she do, where should she live, and should she take her MITx MicroMasters capstone exams? She had registered for the Statistics and Data Science Program’s final exams just days prior to moving out of her apartment and into a bomb shelter. Although it was difficult to focus on studying and preparations with air horns sounding overhead and uncertainty lingering around her, she was determined to try. “I wouldn’t let the aggressor in the war squash my dreams,” she says.

      A love of research and the desire to improve teaching 

      An early love of solving puzzles and problems for fun piqued Herasymova’s initial interest in mathematics. When she later pursued her PhD in mathematics at Kiev National Taras Shevchenko University, Herasymova’s love of math evolved into a love of research. Throughout Herasymova’s career, she’s worked to close the gap between scientific researchers and educators. Starting as a math tutor at MBA Strategy, a company that prepares Ukrainian leaders for qualifying standardized tests for MBA programs, she was later promoted as the head of their test preparation department. Afterward, she moved on to an equivalent position at ZNOUA, a new project that prepared high school students for Ukraine’s standardized test, and she eventually became ZNOUA’s CEO.

      In 2018, she founded Prosteer, a “self-learning community” of educators who share research, pedagogy, and experience to learn from one another. “It’s really interesting to have a community of teachers from different domains,” she says, speaking of educators and researchers whose specialties range across language, mathematics, physics, music, and more.

      Implementing new pedagogical research in the classroom is often up to educators who seek out studies on an individual basis, Herasymova has found. “Lots of scientists are not practitioners,” she says, and the reverse is also true. She only became more determined to build these connections once she was promoted to head of test preparation at MBA Strategy because she wanted to share more effective pedagogy with the tutors she was mentoring.

      First, Herasymova knew she needed a way to measure the teachers’ effectiveness. She was able to determine whether students who received the company’s tutoring services improved their scores. Moreover, Ukraine keeps an open-access database of national standardized test scores, so anyone could analyze the data in hopes of improving the level of education in the country. She says, “I could do some analytics because I am a mathematician, but I knew I could do much more with this data if I knew data science and machine learning knowledge.”

      That’s why Herasymova sought out the MITx MicroMasters Program in Statistics and Data Science offered by the MIT Institute for Data, Systems, and Society (IDSS). “I wanted to learn the fundamentals so I could join the Learning Analytics domain,” she says. She was looking for a comprehensive program that covered the foundations without being overly basic. “I had some knowledge from the ground, so I could see the deepness of that course,” she says. Because of her background as an instructional designer, she thought the MicroMasters curriculum was well-constructed, calling the variety of videos, practice problems, and homework assignments that encouraged learners to approach the course material in different ways, “a perfect experience.”

      Another benefit of the MicroMasters program was its online format. “I had my usual work, so it was impossible to study in a stationary way,” she says. She found the structure to be more flexible than other programs. “It’s really great that you can construct your course schedule your own way, especially with your own adult life,” she says.

      Determination and support in the midst of war

      When the war first forced Herasymova to flee her apartment, she had already registered to take the exams for her four courses. “It was quite hard to prepare for exams when you could hear explosions outside of the bomb shelter,” she says. She and other Ukranians were invited to postpone their exams until the following session, but the next available testing period wouldn’t be held until October. “It was a hard decision, but I had to allow myself to try,” she says. “For all people in Ukraine, when you don’t know if you’re going to live or die, you try to live in the now. You have to appreciate every moment and what life brings to you. You don’t say, ‘Someday’ — you do it today or tomorrow.”

      In addition to emotional support from her boyfriend, Herasymova had a group of friends who had also enrolled in the program, and they supported each other through study sessions and an ongoing chat. Herasymova’s personal support network helped her accomplish what she set out to do with her MicroMasters program, and in turn, she was able to support her professional network. While Prosteer halted its regular work during the early stages of the war, Herasymova was determined to support the community of educators and scientists that she had built. They continued meeting weekly to exchange ideas as usual. “It’s intrinsic motivation,” she says. They managed to restore all of their activities by October.

      Despite the factors stacked against her, Herasymova’s determination paid off — she passed all of her exams in May, the final step to earning her MicroMasters certificate in statistics and data science. “I just couldn’t believe it,” she says. “It was definitely a bifurcation point. The moment when you realize that you have something to rely on, and that life is just beginning to show all its diversity despite the fact that you live in war.” With her newly minted certificate in hand, Herasymova has continued her research on the effectiveness of educational models — analyzing the data herself — with a summer research program at New York University. 

      The student becomes the master

      After moving seven times between February and October, heading west from Kyiv until most recently settling near the border of Poland, Herasymova hopes she’s moved for the last time. Ukrainian Catholic University offered her a position teaching both mathematics and programming. Before enrolling in the MicroMasters Program in Statistics and Data Science, she had some prior knowledge of programming languages and mathematical algorithms, but she didn’t know Python. She took MITx’s Introduction to Computer Science and Programming Using Python to prepare. “It gave me a huge step forward,” she says. “I learned a lot. Now, not only can I work with Python machine learning models in programming language R, I also have knowledge of the big picture of the purpose and the point to do so.”

      In addition to the skills the MicroMasters Program trained her in, she gained firsthand experience in learning new subjects and exploring topics more deeply. She will be sharing that practice with the community of students and teachers she’s built, plus, she plans on guiding them through this course during the next year. As a continuation of her own educational growth, says she’s looking forward to her next MITx course this year, Data Analysis.

      Herasymova advises that the best way to keep progressing is investing a lot of time. “Adults don’t want to hear this, but you need one or two years,” she says. “Allow yourself to be stupid. If you’re an expert in one domain and want to switch to another, or if you want to understand something new, a lot of people don’t ask questions or don’t ask for help. But from this point, if I don’t know something, I know I should ask for help because that’s the start of learning. With a fixed mindset, you won’t grow.”

      July 2022 MicroMasters Program Joint Completion Celebration. Ukrainian student Tetiana Herasymova, who completed her program amid war in her home country, speaks at 43:55. More

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      Empowering Cambridge youth through data activism

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

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

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

      Cross-Cambridge collaboration

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

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

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

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

      A different side of STEAM

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

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

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

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

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

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

      Photo: Katherine Ouellette

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

      From MSYEP to the mayor’s office

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

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

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

      Photo: Katherine Ouellette

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

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

      Photo: Katherine Ouellette

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