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      Companies use MIT research to identify and respond to supply chain risks

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      In February 2020, MIT professor David Simchi-Levi predicted the future. In an article in Harvard Business Review, he and his colleague warned that the new coronavirus outbreak would throttle supply chains and shutter tens of thousands of businesses across North America and Europe by mid-March.

      For Simchi-Levi, who had developed new models of supply chain resiliency and advised major companies on how to best shield themselves from supply chain woes, the signs of disruption were plain to see. Two years later, the professor of engineering systems at the MIT Schwarzman College of Computing and the Department of Civil and Environmental Engineering, and director of the MIT Data Science Lab has found a “flood of interest” from companies anxious to apply his Risk Exposure Index (REI) research to identify and respond to hidden risks in their own supply chains.

      His work on “stress tests” for critical supply chains and ways to guide global supply chain recovery were included in the 2022 Economic Report of the President presented to the U.S. Congress in April.

      It is rare that data science research can influence policy at the highest levels, Simchi-Levi says, but his models reflect something that business needs now: a new world of continuing global crisis, without relying on historical precedent.

      “What the last two years showed is that you cannot plan just based on what happened last year or the last two years,” Simchi-Levi says.

      He recalled the famous quote, sometimes attributed to hockey great Wayne Gretzsky, that good players don’t skate to where the puck is, but where the puck is going to be. “We are not focusing on the state of the supply chain right now, but what may happen six weeks from now, eight weeks from now, to prepare ourselves today to prevent the problems of the future.”

      Finding hidden risks

      At the heart of REI is a mathematical model of the supply chain that focuses on potential failures at different supply chain nodes — a flood at a supplier’s factory, or a shortage of raw materials at another factory, for instance. By calculating variables such as “time-to-recover” (TTR), which measures how long it will take a particular node to be back at full function, and time-to-survive (TTS), which identifies the maximum duration that the supply chain can match supply with demand after a disruption, the model focuses on the impact of disruption on the supply chain, rather than the cause of disruption.

      Even before the pandemic, catastrophic events such as the 2010 Iceland volcanic eruption and the 2011 Tohoku earthquake and tsunami in Japan were threatening these nodes. “For many years, companies from a variety of industries focused mostly on efficiency, cutting costs as much as possible, using strategies like outsourcing and offshoring,” Simchi-Levi says. “They were very successful doing this, but it has dramatically increased their exposure to risk.”

      Using their model, Simchi-Levi and colleagues began working with Ford Motor Company in 2013 to improve the company’s supply chain resiliency. The partnership uncovered some surprising hidden risks.

      To begin with, the researchers found out that Ford’s “strategic suppliers” — the nodes of the supply chain where the company spent large amount of money each year — had only moderate exposure to risk. Instead, the biggest risk “tended to come from tiny suppliers that provide Ford with components that cost about 10 cents,” says Simchi-Levi.

      The analysis also found that risky suppliers are everywhere across the globe. “There is this idea that if you just move suppliers closer to market, to demand, to North America or to Mexico, you increase the resiliency of your supply chain. That is not supported by our data,” he says.

      Rewards of resiliency

      By creating a virtual representation, or “digital twin,” of the Ford supply chain, the researchers were able to test out strategies at each node to see what would increase supply chain resiliency. Should the company invest in more warehouses to store a key component? Should it shift production of a component to another factory?

      Companies are sometimes reluctant to invest in supply chain resiliency, Simchi-Levi says, but the analysis isn’t just about risk. “It’s also going to help you identify savings opportunities. The company may be building a lot of misplaced, costly inventory, for instance, and our method helps them to identify these inefficiencies and cut costs.”

      Since working with Ford, Simchi-Levi and colleagues have collaborated with many other companies, including a partnership with Accenture, to scale the REI technology to a variety of industries including high-tech, industrial equipment, home improvement retailers, fashion retailers, and consumer packaged goods.

      Annette Clayton, the CEO of Schneider Electric North America and previously its chief supply chain officer, has worked with Simchi-Levi for 17 years. “When I first went to work for Schneider, I asked David and his team to help us look at resiliency and inventory positioning in order to make the best cost, delivery, flexibility, and speed trade-offs for the North American supply chain,” she says. “As the pandemic unfolded, the very learnings in supply chain resiliency we had worked on before became even more important and we partnered with David and his team again,”

      “We have used TTR and TTS to determine places where we need to develop and duplicate supplier capability, from raw materials to assembled parts. We increased inventories where our time-to-recover because of extended logistics times exceeded our time-to-survive,” Clayton adds. “We have used TTR and TTS to prioritize our workload in supplier development, procurement and expanding our own manufacturing capacity.”

      The REI approach can even be applied to an entire country’s economy, as the U.N. Office for Disaster Risk Reduction has done for developing countries such as Thailand in the wake of disastrous flooding in 2011.

      Simchi-Levi and colleagues have been motivated by the pandemic to enhance the REI model with new features. “Because we have started collaborating with more companies, we have realized some interesting, company-specific business constraints,” he says, which are leading to more efficient ways of calculating hidden risk. More

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      Hallucinating to better text translation

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      As babies, we babble and imitate our way to learning languages. We don’t start off reading raw text, which requires fundamental knowledge and understanding about the world, as well as the advanced ability to interpret and infer descriptions and relationships. Rather, humans begin our language journey slowly, by pointing and interacting with our environment, basing our words and perceiving their meaning through the context of the physical and social world. Eventually, we can craft full sentences to communicate complex ideas.

      Similarly, when humans begin learning and translating into another language, the incorporation of other sensory information, like multimedia, paired with the new and unfamiliar words, like flashcards with images, improves language acquisition and retention. Then, with enough practice, humans can accurately translate new, unseen sentences in context without the accompanying media; however, imagining a picture based on the original text helps.

      This is the basis of a new machine learning model, called VALHALLA, by researchers from MIT, IBM, and the University of California at San Diego, in which a trained neural network sees a source sentence in one language, hallucinates an image of what it looks like, and then uses both to translate into a target language. The team found that their method demonstrates improved accuracy of machine translation over text-only translation. Further, it provided an additional boost for cases with long sentences, under-resourced languages, and instances where part of the source sentence is inaccessible to the machine translator.

      As a core task within the AI field of natural language processing (NLP), machine translation is an “eminently practical technology that’s being used by millions of people every day,” says study co-author Yoon Kim, assistant professor in MIT’s Department of Electrical Engineering and Computer Science with affiliations in the Computer Science and Artificial Intelligence Laboratory (CSAIL) and the MIT-IBM Watson AI Lab. With recent, significant advances in deep learning, “there’s been an interesting development in how one might use non-text information — for example, images, audio, or other grounding information — to tackle practical tasks involving language” says Kim, because “when humans are performing language processing tasks, we’re doing so within a grounded, situated world.” The pairing of hallucinated images and text during inference, the team postulated, imitates that process, providing context for improved performance over current state-of-the-art techniques, which utilize text-only data.

      This research will be presented at the IEEE / CVF Computer Vision and Pattern Recognition Conference this month. Kim’s co-authors are UC San Diego graduate student Yi Li and Professor Nuno Vasconcelos, along with research staff members Rameswar Panda, Chun-fu “Richard” Chen, Rogerio Feris, and IBM Director David Cox of IBM Research and the MIT-IBM Watson AI Lab.

      Learning to hallucinate from images

      When we learn new languages and to translate, we’re often provided with examples and practice before venturing out on our own. The same is true for machine-translation systems; however, if images are used during training, these AI methods also require visual aids for testing, limiting their applicability, says Panda.

      “In real-world scenarios, you might not have an image with respect to the source sentence. So, our motivation was basically: Instead of using an external image during inference as input, can we use visual hallucination — the ability to imagine visual scenes — to improve machine translation systems?” says Panda.

      To do this, the team used an encoder-decoder architecture with two transformers, a type of neural network model that’s suited for sequence-dependent data, like language, that can pay attention key words and semantics of a sentence. One transformer generates a visual hallucination, and the other performs multimodal translation using outputs from the first transformer.

      During training, there are two streams of translation: a source sentence and a ground-truth image that is paired with it, and the same source sentence that is visually hallucinated to make a text-image pair. First the ground-truth image and sentence are tokenized into representations that can be handled by transformers; for the case of the sentence, each word is a token. The source sentence is tokenized again, but this time passed through the visual hallucination transformer, outputting a hallucination, a discrete image representation of the sentence. The researchers incorporated an autoregression that compares the ground-truth and hallucinated representations for congruency — e.g., homonyms: a reference to an animal “bat” isn’t hallucinated as a baseball bat. The hallucination transformer then uses the difference between them to optimize its predictions and visual output, making sure the context is consistent.

      The two sets of tokens are then simultaneously passed through the multimodal translation transformer, each containing the sentence representation and either the hallucinated or ground-truth image. The tokenized text translation outputs are compared with the goal of being similar to each other and to the target sentence in another language. Any differences are then relayed back to the translation transformer for further optimization.

      For testing, the ground-truth image stream drops off, since images likely wouldn’t be available in everyday scenarios.

      “To the best of our knowledge, we haven’t seen any work which actually uses a hallucination transformer jointly with a multimodal translation system to improve machine translation performance,” says Panda.

      Visualizing the target text

      To test their method, the team put VALHALLA up against other state-of-the-art multimodal and text-only translation methods. They used public benchmark datasets containing ground-truth images with source sentences, and a dataset for translating text-only news articles. The researchers measured its performance over 13 tasks, ranging from translation on well-resourced languages (like English, German, and French), under-resourced languages (like English to Romanian) and non-English (like Spanish to French). The group also tested varying transformer model sizes, how accuracy changes with the sentence length, and translation under limited textual context, where portions of the text were hidden from the machine translators.

      The team observed significant improvements over text-only translation methods, improving data efficiency, and that smaller models performed better than the larger base model. As sentences became longer, VALHALLA’s performance over other methods grew, which the researchers attributed to the addition of more ambiguous words. In cases where part of the sentence was masked, VALHALLA could recover and translate the original text, which the team found surprising.

      Further unexpected findings arose: “Where there weren’t as many training [image and] text pairs, [like for under-resourced languages], improvements were more significant, which indicates that grounding in images helps in low-data regimes,” says Kim. “Another thing that was quite surprising to me was this improved performance, even on types of text that aren’t necessarily easily connectable to images. For example, maybe it’s not so surprising if this helps in translating visually salient sentences, like the ‘there is a red car in front of the house.’ [However], even in text-only [news article] domains, the approach was able to improve upon text-only systems.”

      While VALHALLA performs well, the researchers note that it does have limitations, requiring pairs of sentences to be annotated with an image, which could make it more expensive to obtain. It also performs better in its ground domain and not the text-only news articles. Moreover, Kim and Panda note, a technique like VALHALLA is still a black box, with the assumption that hallucinated images are providing helpful information, and the team plans to investigate what and how the model is learning in order to validate their methods.

      In the future, the team plans to explore other means of improving translation. “Here, we only focus on images, but there are other types of a multimodal information — for example, speech, video or touch, or other sensory modalities,” says Panda. “We believe such multimodal grounding can lead to even more efficient machine translation models, potentially benefiting translation across many low-resource languages spoken in the world.”

      This research was supported, in part, by the MIT-IBM Watson AI Lab and the National Science Foundation. More

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      Making data visualization more accessible for blind and low-vision individuals

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      Data visualizations on the web are largely inaccessible for blind and low-vision individuals who use screen readers, an assistive technology that reads on-screen elements as text-to-speech. This excludes millions of people from the opportunity to probe and interpret insights that are often presented through charts, such as election results, health statistics, and economic indicators. 

      When a designer attempts to make a visualization accessible, best practices call for including a few sentences of text that describe the chart and a link to the underlying data table — a far cry from the rich reading experience available to sighted users.

      An interdisciplinary team of researchers from MIT and elsewhere is striving to create screen-reader-friendly data visualizations that offer a similarly rich experience. They prototyped several visualization structures that provide text descriptions at varying levels of detail, enabling a screen-reader user to drill down from high-level data to more detailed information using just a few keystrokes.

      The MIT team embarked on an iterative co-design process with collaborator Daniel Hajas, a researcher at University College London who works with the Global Disability Innovation Hub and lost his sight at age 16. They collaborated to develop prototypes and ran a detailed user study with blind and low-vision individuals to gather feedback.

      “Researchers might see some connections between problems and be aware of potential solutions, but very often they miss it by a little bit. Insights from people who have the lived experience of a certain specific, measurable problem are really important for a lot of disability-related solutions. I think we found a really nice fit,” says Hajas.

      They created a framework to help designers think systematically about how to develop accessible visualizations. In the future, they plan to use their prototypes and design framework to build a user-friendly tool that could convert visualizations into accessible formats.

      MIT collaborators include co-lead authors and Computer Science and Artificial Intelligence Laboratory (CSAIL) graduate students Jonathan Zong, Crystal Lee, and Alan Lundgard, as well as JiWoong Jang, an undergraduate at Carnegie Mellon University who worked on this project during MIT’s Summer Research Program (MSRP), and senior author Arvind Satyanarayan, assistant professor of computer science who leads the Visualization Group in CSAIL. The research paper, which will be presented at the Eurographics Conference on Visualization, won a best paper honorable mention award.

      “Push what is possible”

      The researchers defined three design dimensions as key to making accessible visualizations: structure, navigation, and description. Structure involves arranging the information into a hierarchy. Navigation refers to how the user moves through different levels of detail. Description is how the information is spoken, including how much information is conveyed.

      Using these design dimensions, they developed several visualization prototypes that emphasized ease-of-navigation for screen-reader users. One prototype, known as multiview, enabled individuals to use the up and down arrows to navigate between different levels of information (like the chart title as the top level, the legend as the second level, etc.), and the right and left arrow keys to cycle through information on the same level (such as adjacent scatterplots). Another prototype, known as target, included the same arrow key navigation but also a drop-down menu of key chart locations so the user could quickly jump to an area of interest.

      “Our goal is not just to work within existing standards to make them serviceable. We really set out to do grounded speculation and imagine where we can push what is possible with these existing standards. We didn’t want to limit ourselves to refitting tools that were designed for images,” says Zong.

      They tested these prototypes and an accessible data table, the existing best practice for accessible visualizations, with 13 blind and visually impaired screen-reader users. They asked users to rate each tool on several criteria, including how easy it was to learn and how easy it was to locate data or answer questions.

      “One thing I thought was really interesting was how much people were constantly testing their own hypotheses or trying to make specific patterns as they moved through the visualization. The implication for navigation is that you want to be able to orient yourself within the visualization so you know where the limits are,” says Lee. “Can you accurately and easily know where the walls are in the room you are exploring?”

      Improved insights

      Users said both prototypes enabled them to more rapidly identify patterns in the data. Scrolling from a high level to deeper levels of information helped them gain insights more easily than when browsing the data table, they said. They also enjoyed faster navigation using the menu in the target prototype.

      But the data table got top marks for ease of use.

      “I expected people to be disappointed with the everyday tools when compared to the new prototypes, but they still clung to the data table a bit, likely because of their familiarity with it. That shows that principles like familiarity, learnability, and usability still matter. No matter how ‘good’ our new invention is, if it is not easy enough to learn, people might stick with an older version,” Hajas says.

      Drawing on these insights, the researchers are refining the prototypes and using them to build a software package that can be used with existing design tools to give visualizations an accessible, navigable structure.

      They also want to explore multimodal solutions. Some study participants used different devices together, like screen readers and braille displays, or data sonification tools that convey information using non-speech audio. How these tools can complement each other when applied to a visualization is still an open question, Zong says.

      In the long-run, they hope their work might lead to careful rethinking of web accessibility standards.

      “There is no one-size-fits-all solution for accessibility. While existing standards don’t presume that, they only offer simple approaches, like data tables and alt text. One of the key benefits of our research contribution is that we are proposing a framework — different preferences and data representations are situated at different points in this design space,” says Lundgard.

      “We have been working hard toward reducing the inequities that screen-reader users face when extracting information from online data visualizations for the past few years. So, we are really appreciative of this work and the knowledge that it adds to the existing literature,” says Ather Sharif, a graduate student who researches accessibility and visualization in the labs of professors Jacob Wobbrock and Katharina Reinecke at the Paul G. Allen School of Computer Science and Engineering of the University of Washington at Seattle, and who was not involved with this work.

      “I like to think of it as a movement where we’re all finally coming together and improving the experiences of a demographic that has been largely ignored, especially when presenting data through visualizations. Kudos to Jonathan, Arvind, and their team for this insightful and timely work! I am looking forward to what’s next,” adds Sharif, who is lead author of several recent papers related to accessible data visualizations.

      Amy Bower, a senior scientist in the Department of Physical Oceanography at the Woods Hole Oceanographic Institution who suffers from a degenerative retinal disease and uses a screen reader extensively in her work as a researcher and also for basic living tasks, found the researchers’ explanations of the importance of co-design to be powerful and compelling.  

      “As a blind scientist, I’m constantly searching for effective tools that will allow me to access the information conveyed in data visualizations. The layered approach taken by these researchers, which provides the option to get the ‘big picture’ from the data as well as drill down into the data points themselves, allows the user to choose how they want to explore the data,” says Bower, who also was not involved with this work. “I think the ability to freely explore the data is necessary not just to learn the ‘story’ that the data are telling, but to allow a blind researcher such as myself to formulate the next questions that need to be tackled to advance understanding in any field of study.”

      This work was supported, in part, by the National Science Foundation.   More

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      Emery Brown wins a share of 2022 Gruber Neuroscience Prize

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      The Gruber Foundation announced on May 17 that Emery N. Brown, the Edward Hood Taplin Professor of Medical Engineering and Computational Neuroscience at MIT, has won the 2022 Gruber Neuroscience Prize along with neurophysicists Laurence Abbott of Columbia University, Terrence Sejnowski of the Salk Institute for Biological Studies, and Haim Sompolinsky of the Hebrew University of Jerusalem.

      The foundation says it honored the four recipients for their influential contributions to the fields of computational and theoretical neuroscience. As datasets have grown ever larger and more complex, these fields have increasingly helped scientists unravel the mysteries of how the brain functions in both health and disease. The prize, which includes a total $500,000 award, will be presented in San Diego, California, on Nov. 13 at the annual meeting of the Society for Neuroscience.

      “These four remarkable scientists have applied their expertise in mathematical and statistical analysis, physics, and machine learning to create theories, mathematical models, and tools that have greatly advanced how we study and understand the brain,” says Joshua Sanes, professor of molecular and cellular biology and founding director of the Center for Brain Science at Harvard University and member of the selection advisory board to the prize. “Their insights and research have not only transformed how experimental neuroscientists do their research, but also are leading to promising new ways of providing clinical care.”

      Brown, who is an investigator in The Picower Institute for Learning and Memory and the Institute for Medical Engineering and Science at MIT, an anesthesiologist at Massachusetts General Hospital, and a professor at Harvard Medical School, says: “It is a pleasant surprise and tremendous honor to be named a co-recipient of the 2022 Gruber Prize in Neuroscience. I am especially honored to share this award with three luminaries in computational and theoretical neuroscience.”

      Brown’s early groundbreaking findings in neuroscience included a novel algorithm that decodes the position of an animal by observing the activity of a small group of place cells in the animal’s brain, a discovery he made while working with fellow Picower Institute investigator Matt Wilson in the 1990s. The resulting state-space algorithm for point processes not only offered much better decoding with fewer neurons than previous approaches, but it also established a new framework for specifying dynamically the relationship between the spike trains (the timing sequence of firing neurons) in the brain and factors from the outside world.

      “One of the basic questions at the time was whether an animal holds a representation of where it is in its mind — in the hippocampus,” Brown says. “We were able to show that it did, and we could show that with only 30 neurons.”

      After introducing this state-space paradigm to neuroscience, Brown went on to refine the original idea and apply it to other dynamic situations — to simultaneously track neural activity and learning, for example, and to define with precision anesthesia-induced loss of consciousness, as well as its subsequent recovery. In the early 2000s, Brown put together a team to specifically study anesthesia’s effects on the brain.

      Through experimental research and mathematical modeling, Brown and his team showed that the altered arousal states produced by the main classes of anesthesia medications can be characterized by analyzing the oscillatory patterns observed in the EEG along with the locations of their molecular targets, and the anatomy and physiology of the neural circuits that connect those locations. He has established, including in recent papers with Picower Professor Earl K. Miller, that a principal way in which anesthetics produce unconsciousness is by producing oscillations that impair how different brain regions communicate with each other.

      The result of Brown’s research has been a new paradigm for brain monitoring during general anesthesia for surgery, one that allows an anesthesiologist to dose the patient based on EEG readouts (neural oscillations) of the patient’s anesthetic state rather than purely on vital sign responses. This pioneering approach promises to revolutionize how anesthesia medications are delivered to patients, and also shed light on other altered states of arousal such as sleep and coma.

      To advance that vision, Brown recently discussed how he is working to develop a new research center at MIT and MGH to further integrate anesthesiology with neuroscience research. The Brain Arousal State Control Innovation Center, he said, would not only advance anesthesiology care but also harness insights gained from anesthesiology research to improve other aspects of clinical neuroscience.

      “By demonstrating that physics and mathematics can make an enormous contribution to neuroscience, doctors Abbott, Brown, Sejnowski, and Sompolinsky have inspired an entire new generation of physicists and other quantitative scientists to follow in their footsteps,” says Frances Jensen, professor and chair of the Department of Neurology and co-director of the Penn Medicine Translational Neuroscience Center within the Perelman School of Medicine at the University of Pennsylvania, and chair of the Selection Advisory Board to the prize. “The ramifications for neuroscience have been broad and profound. It is a great pleasure to be honoring each of them with this prestigious award.”

      This report was adapted from materials provided by the Gruber Foundation. More

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      Artificial intelligence predicts patients’ race from their medical images

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      The miseducation of algorithms is a critical problem; when artificial intelligence mirrors unconscious thoughts, racism, and biases of the humans who generated these algorithms, it can lead to serious harm. Computer programs, for example, have wrongly flagged Black defendants as twice as likely to reoffend as someone who’s white. When an AI used cost as a proxy for health needs, it falsely named Black patients as healthier than equally sick white ones, as less money was spent on them. Even AI used to write a play relied on using harmful stereotypes for casting. 

      Removing sensitive features from the data seems like a viable tweak. But what happens when it’s not enough? 

      Examples of bias in natural language processing are boundless — but MIT scientists have investigated another important, largely underexplored modality: medical images. Using both private and public datasets, the team found that AI can accurately predict self-reported race of patients from medical images alone. Using imaging data of chest X-rays, limb X-rays, chest CT scans, and mammograms, the team trained a deep learning model to identify race as white, Black, or Asian — even though the images themselves contained no explicit mention of the patient’s race. This is a feat even the most seasoned physicians cannot do, and it’s not clear how the model was able to do this. 

      In an attempt to tease out and make sense of the enigmatic “how” of it all, the researchers ran a slew of experiments. To investigate possible mechanisms of race detection, they looked at variables like differences in anatomy, bone density, resolution of images — and many more, and the models still prevailed with high ability to detect race from chest X-rays. “These results were initially confusing, because the members of our research team could not come anywhere close to identifying a good proxy for this task,” says paper co-author Marzyeh Ghassemi, an assistant professor in the MIT Department of Electrical Engineering and Computer Science and the Institute for Medical Engineering and Science (IMES), who is an affiliate of the Computer Science and Artificial Intelligence Laboratory (CSAIL) and of the MIT Jameel Clinic. “Even when you filter medical images past where the images are recognizable as medical images at all, deep models maintain a very high performance. That is concerning because superhuman capacities are generally much more difficult to control, regulate, and prevent from harming people.”

      In a clinical setting, algorithms can help tell us whether a patient is a candidate for chemotherapy, dictate the triage of patients, or decide if a movement to the ICU is necessary. “We think that the algorithms are only looking at vital signs or laboratory tests, but it’s possible they’re also looking at your race, ethnicity, sex, whether you’re incarcerated or not — even if all of that information is hidden,” says paper co-author Leo Anthony Celi, principal research scientist in IMES at MIT and associate professor of medicine at Harvard Medical School. “Just because you have representation of different groups in your algorithms, that doesn’t guarantee it won’t perpetuate or magnify existing disparities and inequities. Feeding the algorithms with more data with representation is not a panacea. This paper should make us pause and truly reconsider whether we are ready to bring AI to the bedside.” 

      The study, “AI recognition of patient race in medical imaging: a modeling study,” was published in Lancet Digital Health on May 11. Celi and Ghassemi wrote the paper alongside 20 other authors in four countries.

      To set up the tests, the scientists first showed that the models were able to predict race across multiple imaging modalities, various datasets, and diverse clinical tasks, as well as across a range of academic centers and patient populations in the United States. They used three large chest X-ray datasets, and tested the model on an unseen subset of the dataset used to train the model and a completely different one. Next, they trained the racial identity detection models for non-chest X-ray images from multiple body locations, including digital radiography, mammography, lateral cervical spine radiographs, and chest CTs to see whether the model’s performance was limited to chest X-rays. 

      The team covered many bases in an attempt to explain the model’s behavior: differences in physical characteristics between different racial groups (body habitus, breast density), disease distribution (previous studies have shown that Black patients have a higher incidence for health issues like cardiac disease), location-specific or tissue specific differences, effects of societal bias and environmental stress, the ability of deep learning systems to detect race when multiple demographic and patient factors were combined, and if specific image regions contributed to recognizing race. 

      What emerged was truly staggering: The ability of the models to predict race from diagnostic labels alone was much lower than the chest X-ray image-based models. 

      For example, the bone density test used images where the thicker part of the bone appeared white, and the thinner part appeared more gray or translucent. Scientists assumed that since Black people generally have higher bone mineral density, the color differences helped the AI models to detect race. To cut that off, they clipped the images with a filter, so the model couldn’t color differences. It turned out that cutting off the color supply didn’t faze the model — it still could accurately predict races. (The “Area Under the Curve” value, meaning the measure of the accuracy of a quantitative diagnostic test, was 0.94–0.96). As such, the learned features of the model appeared to rely on all regions of the image, meaning that controlling this type of algorithmic behavior presents a messy, challenging problem. 

      The scientists acknowledge limited availability of racial identity labels, which caused them to focus on Asian, Black, and white populations, and that their ground truth was a self-reported detail. Other forthcoming work will include potentially looking at isolating different signals before image reconstruction, because, as with bone density experiments, they couldn’t account for residual bone tissue that was on the images. 

      Notably, other work by Ghassemi and Celi led by MIT student Hammaad Adam has found that models can also identify patient self-reported race from clinical notes even when those notes are stripped of explicit indicators of race. Just as in this work, human experts are not able to accurately predict patient race from the same redacted clinical notes.

      “We need to bring social scientists into the picture. Domain experts, which are usually the clinicians, public health practitioners, computer scientists, and engineers are not enough. Health care is a social-cultural problem just as much as it’s a medical problem. We need another group of experts to weigh in and to provide input and feedback on how we design, develop, deploy, and evaluate these algorithms,” says Celi. “We need to also ask the data scientists, before any exploration of the data, are there disparities? Which patient groups are marginalized? What are the drivers of those disparities? Is it access to care? Is it from the subjectivity of the care providers? If we don’t understand that, we won’t have a chance of being able to identify the unintended consequences of the algorithms, and there’s no way we’ll be able to safeguard the algorithms from perpetuating biases.”

      “The fact that algorithms ‘see’ race, as the authors convincingly document, can be dangerous. But an important and related fact is that, when used carefully, algorithms can also work to counter bias,” says Ziad Obermeyer, associate professor at the University of California at Berkeley, whose research focuses on AI applied to health. “In our own work, led by computer scientist Emma Pierson at Cornell, we show that algorithms that learn from patients’ pain experiences can find new sources of knee pain in X-rays that disproportionately affect Black patients — and are disproportionately missed by radiologists. So just like any tool, algorithms can be a force for evil or a force for good — which one depends on us, and the choices we make when we build algorithms.”

      The work is supported, in part, by the National Institutes of Health. More

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      Living better with algorithms

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

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

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

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

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

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

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

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

      Auditing social media algorithms

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

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

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

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

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

      Who gets good outcomes and who gets bad ones?

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

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

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

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

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

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

      What is the effect of X on Y?

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

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

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

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

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

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

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

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

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      On the road to cleaner, greener, and faster driving

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      No one likes sitting at a red light. But signalized intersections aren’t just a minor nuisance for drivers; vehicles consume fuel and emit greenhouse gases while waiting for the light to change.

      What if motorists could time their trips so they arrive at the intersection when the light is green? While that might be just a lucky break for a human driver, it could be achieved more consistently by an autonomous vehicle that uses artificial intelligence to control its speed.

      In a new study, MIT researchers demonstrate a machine-learning approach that can learn to control a fleet of autonomous vehicles as they approach and travel through a signalized intersection in a way that keeps traffic flowing smoothly.

      Using simulations, they found that their approach reduces fuel consumption and emissions while improving average vehicle speed. The technique gets the best results if all cars on the road are autonomous, but even if only 25 percent use their control algorithm, it still leads to substantial fuel and emissions benefits.

      “This is a really interesting place to intervene. No one’s life is better because they were stuck at an intersection. With a lot of other climate change interventions, there is a quality-of-life difference that is expected, so there is a barrier to entry there. Here, the barrier is much lower,” says senior author Cathy Wu, the Gilbert W. Winslow Career Development Assistant Professor in the Department of Civil and Environmental Engineering and a member of the Institute for Data, Systems, and Society (IDSS) and the Laboratory for Information and Decision Systems (LIDS).

      The lead author of the study is Vindula Jayawardana, a graduate student in LIDS and the Department of Electrical Engineering and Computer Science. The research will be presented at the European Control Conference.

      Intersection intricacies

      While humans may drive past a green light without giving it much thought, intersections can present billions of different scenarios depending on the number of lanes, how the signals operate, the number of vehicles and their speeds, the presence of pedestrians and cyclists, etc.

      Typical approaches for tackling intersection control problems use mathematical models to solve one simple, ideal intersection. That looks good on paper, but likely won’t hold up in the real world, where traffic patterns are often about as messy as they come.

      Wu and Jayawardana shifted gears and approached the problem using a model-free technique known as deep reinforcement learning. Reinforcement learning is a trial-and-error method where the control algorithm learns to make a sequence of decisions. It is rewarded when it finds a good sequence. With deep reinforcement learning, the algorithm leverages assumptions learned by a neural network to find shortcuts to good sequences, even if there are billions of possibilities.

      This is useful for solving a long-horizon problem like this; the control algorithm must issue upwards of 500 acceleration instructions to a vehicle over an extended time period, Wu explains.

      “And we have to get the sequence right before we know that we have done a good job of mitigating emissions and getting to the intersection at a good speed,” she adds.

      But there’s an additional wrinkle. The researchers want the system to learn a strategy that reduces fuel consumption and limits the impact on travel time. These goals can be conflicting.

      “To reduce travel time, we want the car to go fast, but to reduce emissions, we want the car to slow down or not move at all. Those competing rewards can be very confusing to the learning agent,” Wu says.

      While it is challenging to solve this problem in its full generality, the researchers employed a workaround using a technique known as reward shaping. With reward shaping, they give the system some domain knowledge it is unable to learn on its own. In this case, they penalized the system whenever the vehicle came to a complete stop, so it would learn to avoid that action.

      Traffic tests

      Once they developed an effective control algorithm, they evaluated it using a traffic simulation platform with a single intersection. The control algorithm is applied to a fleet of connected autonomous vehicles, which can communicate with upcoming traffic lights to receive signal phase and timing information and observe their immediate surroundings. The control algorithm tells each vehicle how to accelerate and decelerate.

      Their system didn’t create any stop-and-go traffic as vehicles approached the intersection. (Stop-and-go traffic occurs when cars are forced to come to a complete stop due to stopped traffic ahead). In simulations, more cars made it through in a single green phase, which outperformed a model that simulates human drivers. When compared to other optimization methods also designed to avoid stop-and-go traffic, their technique resulted in larger fuel consumption and emissions reductions. If every vehicle on the road is autonomous, their control system can reduce fuel consumption by 18 percent and carbon dioxide emissions by 25 percent, while boosting travel speeds by 20 percent.

      “A single intervention having 20 to 25 percent reduction in fuel or emissions is really incredible. But what I find interesting, and was really hoping to see, is this non-linear scaling. If we only control 25 percent of vehicles, that gives us 50 percent of the benefits in terms of fuel and emissions reduction. That means we don’t have to wait until we get to 100 percent autonomous vehicles to get benefits from this approach,” she says.

      Down the road, the researchers want to study interaction effects between multiple intersections. They also plan to explore how different intersection set-ups (number of lanes, signals, timings, etc.) can influence travel time, emissions, and fuel consumption. In addition, they intend to study how their control system could impact safety when autonomous vehicles and human drivers share the road. For instance, even though autonomous vehicles may drive differently than human drivers, slower roadways and roadways with more consistent speeds could improve safety, Wu says.

      While this work is still in its early stages, Wu sees this approach as one that could be more feasibly implemented in the near-term.

      “The aim in this work is to move the needle in sustainable mobility. We want to dream, as well, but these systems are big monsters of inertia. Identifying points of intervention that are small changes to the system but have significant impact is something that gets me up in the morning,” she says.  

      This work was supported, in part, by the MIT-IBM Watson AI Lab. More

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      in Data Management & Statistics

      Technique protects privacy when making online recommendations

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      Algorithms recommend products while we shop online or suggest songs we might like as we listen to music on streaming apps.

      These algorithms work by using personal information like our past purchases and browsing history to generate tailored recommendations. The sensitive nature of such data makes preserving privacy extremely important, but existing methods for solving this problem rely on heavy cryptographic tools requiring enormous amounts of computation and bandwidth.

      MIT researchers may have a better solution. They developed a privacy-preserving protocol that is so efficient it can run on a smartphone over a very slow network. Their technique safeguards personal data while ensuring recommendation results are accurate.

      In addition to user privacy, their protocol minimizes the unauthorized transfer of information from the database, known as leakage, even if a malicious agent tries to trick a database into revealing secret information.

      The new protocol could be especially useful in situations where data leaks could violate user privacy laws, like when a health care provider uses a patient’s medical history to search a database for other patients who had similar symptoms or when a company serves targeted advertisements to users under European privacy regulations.

      “This is a really hard problem. We relied on a whole string of cryptographic and algorithmic tricks to arrive at our protocol,” says Sacha Servan-Schreiber, a graduate student in the Computer Science and Artificial Intelligence Laboratory (CSAIL) and lead author of the paper that presents this new protocol.

      Servan-Schreiber wrote the paper with fellow CSAIL graduate student Simon Langowski and their advisor and senior author Srinivas Devadas, the Edwin Sibley Webster Professor of Electrical Engineering. The research will be presented at the IEEE Symposium on Security and Privacy.

      The data next door

      The technique at the heart of algorithmic recommendation engines is known as a nearest neighbor search, which involves finding the data point in a database that is closest to a query point. Data points that are mapped nearby share similar attributes and are called neighbors.

      These searches involve a server that is linked with an online database which contains concise representations of data point attributes. In the case of a music streaming service, those attributes, known as feature vectors, could be the genre or popularity of different songs.

      To find a song recommendation, the client (user) sends a query to the server that contains a certain feature vector, like a genre of music the user likes or a compressed history of their listening habits. The server then provides the ID of a feature vector in the database that is closest to the client’s query, without revealing the actual vector. In the case of music streaming, that ID would likely be a song title. The client learns the recommended song title without learning the feature vector associated with it.

      “The server has to be able to do this computation without seeing the numbers it is doing the computation on. It can’t actually see the features, but still needs to give you the closest thing in the database,” says Langowski.

      To achieve this, the researchers created a protocol that relies on two separate servers that access the same database. Using two servers makes the process more efficient and enables the use of a cryptographic technique known as private information retrieval. This technique allows a client to query a database without revealing what it is searching for, Servan-Schreiber explains.

      Overcoming security challenges

      But while private information retrieval is secure on the client side, it doesn’t provide database privacy on its own. The database offers a set of candidate vectors — possible nearest neighbors — for the client, which are typically winnowed down later by the client using brute force. However, doing so can reveal a lot about the database to the client. The additional privacy challenge is to prevent the client from learning those extra vectors. 

      The researchers employed a tuning technique that eliminates many of the extra vectors in the first place, and then used a different trick, which they call oblivious masking, to hide any additional data points except for the actual nearest neighbor. This efficiently preserves database privacy, so the client won’t learn anything about the feature vectors in the database.  

      Once they designed this protocol, they tested it with a nonprivate implementation on four real-world datasets to determine how to tune the algorithm to maximize accuracy. Then, they used their protocol to conduct private nearest neighbor search queries on those datasets.

      Their technique requires a few seconds of server processing time per query and less than 10 megabytes of communication between the client and servers, even with databases that contained more than 10 million items. By contrast, other secure methods can require gigabytes of communication or hours of computation time. With each query, their method achieved greater than 95 percent accuracy (meaning that nearly every time it found the actual approximate nearest neighbor to the query point). 

      The techniques they used to enable database privacy will thwart a malicious client even if it sends false queries to try and trick the server into leaking information.

      “A malicious client won’t learn much more information than an honest client following protocol. And it protects against malicious servers, too. If one deviates from protocol, you might not get the right result, but they will never learn what the client’s query was,” Langowski says.

      In the future, the researchers plan to adjust the protocol so it can preserve privacy using only one server. This could enable it to be applied in more real-world situations, since it would not require the use of two noncolluding entities (which don’t share information with each other) to manage the database.  

      “Nearest neighbor search undergirds many critical machine-learning driven applications, from providing users with content recommendations to classifying medical conditions. However, it typically requires sharing a lot of data with a central system to aggregate and enable the search,” says Bayan Bruss, head of applied machine-learning research at Capital One, who was not involved with this work. “This research provides a key step towards ensuring that the user receives the benefits from nearest neighbor search while having confidence that the central system will not use their data for other purposes.” More