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      Cracking the case of Arctic sea ice breakup

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      Despite its below-freezing temperatures, the Arctic is warming twice as fast as the rest of the planet. As Arctic sea ice melts, fewer bright surfaces are available to reflect sunlight back into space. When fractures open in the ice cover, the water underneath gets exposed. Dark, ice-free water absorbs the sun’s energy, heating the ocean and driving further melting — a vicious cycle. This warming in turn melts glacial ice, contributing to rising sea levels.

      Warming climate and rising sea levels endanger the nearly 40 percent of the U.S. population living in coastal areas, the billions of people who depend on the ocean for food and their livelihoods, and species such as polar bears and Artic foxes. Reduced ice coverage is also making the once-impassable region more accessible, opening up new shipping lanes and ports. Interest in using these emerging trans-Arctic routes for product transit, extraction of natural resources (e.g., oil and gas), and military activity is turning an area traditionally marked by low tension and cooperation into one of global geopolitical competition.

      As the Arctic opens up, predicting when and where the sea ice will fracture becomes increasingly important in strategic decision-making. However, huge gaps exist in our understanding of the physical processes contributing to ice breakup. Researchers at MIT Lincoln Laboratory seek to help close these gaps by turning a data-sparse environment into a data-rich one. They envision deploying a distributed set of unattended sensors across the Arctic that will persistently detect and geolocate ice fracturing events. Concurrently, the network will measure various environmental conditions, including water temperature and salinity, wind speed and direction, and ocean currents at different depths. By correlating these fracturing events and environmental conditions, they hope to discover meaningful insights about what is causing the sea ice to break up. Such insights could help predict the future state of Arctic sea ice to inform climate modeling, climate change planning, and policy decision-making at the highest levels.

      “We’re trying to study the relationship between ice cracking, climate change, and heat flow in the ocean,” says Andrew March, an assistant leader of Lincoln Laboratory’s Advanced Undersea Systems and Technology Group. “Do cracks in the ice cause warm water to rise and more ice to melt? Do undersea currents and waves cause cracking? Does cracking cause undersea waves? These are the types of questions we aim to investigate.”

      Arctic access

      In March 2022, Ben Evans and Dave Whelihan, both researchers in March’s group, traveled for 16 hours across three flights to Prudhoe Bay, located on the North Slope of Alaska. From there, they boarded a small specialized aircraft and flew another 90 minutes to a three-and-a-half-mile-long sheet of ice floating 160 nautical miles offshore in the Arctic Ocean. In the weeks before their arrival, the U.S. Navy’s Arctic Submarine Laboratory had transformed this inhospitable ice floe into a temporary operating base called Ice Camp Queenfish, named after the first Sturgeon-class submarine to operate under the ice and the fourth to reach the North Pole. The ice camp featured a 2,500-foot-long runway, a command center, sleeping quarters to accommodate up to 60 personnel, a dining tent, and an extremely limited internet connection.

      At Queenfish, for the next four days, Evans and Whelihan joined U.S. Navy, Army, Air Force, Marine Corps, and Coast Guard members, and members of the Royal Canadian Air Force and Navy and United Kingdom Royal Navy, who were participating in Ice Exercise (ICEX) 2022. Over the course of about three weeks, more than 200 personnel stationed at Queenfish, Prudhoe Bay, and aboard two U.S. Navy submarines participated in this biennial exercise. The goals of ICEX 2022 were to assess U.S. operational readiness in the Arctic; increase our country’s experience in the region; advance our understanding of the Arctic environment; and continue building relationships with other services, allies, and partner organizations to ensure a free and peaceful Arctic. The infrastructure provided for ICEX concurrently enables scientists to conduct research in an environment — either in person or by sending their research equipment for exercise organizers to deploy on their behalf — that would be otherwise extremely difficult and expensive to access.

      In the Arctic, windchill temperatures can plummet to as low as 60 degrees Fahrenheit below zero, cold enough to freeze exposed skin within minutes. Winds and ocean currents can drift the entire camp beyond the reach of nearby emergency rescue aircraft, and the ice can crack at any moment. To ensure the safety of participants, a team of Navy meteorological specialists continually monitors the ever-changing conditions. The original camp location for ICEX 2022 had to be evacuated and relocated after a massive crack formed in the ice, delaying Evans’ and Whelihan’s trip. Even the newly selected site had a large crack form behind the camp and another crack that necessitated moving a number of tents.

      “Such cracking events are only going to increase as the climate warms, so it’s more critical now than ever to understand the physical processes behind them,” Whelihan says. “Such an understanding will require building technology that can persist in the environment despite these incredibly harsh conditions. So, it’s a challenge not only from a scientific perspective but also an engineering one.”

      “The weather always gets a vote, dictating what you’re able to do out here,” adds Evans. “The Arctic Submarine Laboratory does a lot of work to construct the camp and make it a safe environment where researchers like us can come to do good science. ICEX is really the only opportunity we have to go onto the sea ice in a place this remote to collect data.”

      A legacy of sea ice experiments

      Though this trip was Whelihan’s and Evans’ first to the Arctic region, staff from the laboratory’s Advanced Undersea Systems and Technology Group have been conducting experiments at ICEX since 2018. However, because of the Arctic’s remote location and extreme conditions, data collection has rarely been continuous over long periods of time or widespread across large areas. The team now hopes to change that by building low-cost, expendable sensing platforms consisting of co-located devices that can be left unattended for automated, persistent, near-real-time monitoring. 

      “The laboratory’s extensive expertise in rapid prototyping, seismo-acoustic signal processing, remote sensing, and oceanography make us a natural fit to build this sensor network,” says Evans.

      In the months leading up to the Arctic trip, the team collected seismometer data at Firepond, part of the laboratory’s Haystack Observatory site in Westford, Massachusetts. Through this local data collection, they aimed to gain a sense of what anthropogenic (human-induced) noise would look like so they could begin to anticipate the kinds of signatures they might see in the Arctic. They also collected ice melting/fracturing data during a thaw cycle and correlated these data with the weather conditions (air temperature, humidity, and pressure). Through this analysis, they detected an increase in seismic signals as the temperature rose above 32 F — an indication that air temperature and ice cracking may be related.

      A sensing network

      At ICEX, the team deployed various commercial off-the-shelf sensors and new sensors developed by the laboratory and University of New Hampshire (UNH) to assess their resiliency in the frigid environment and to collect an initial dataset.

      “One aspect that differentiates these experiments from those of the past is that we concurrently collected seismo-acoustic data and environmental parameters,” says Evans.

      The commercial technologies were seismometers to detect the vibrational energy released when sea ice fractures or collides with other ice floes; a hydrophone (underwater microphone) array to record the acoustic energy created by ice-fracturing events; a sound speed profiler to measure the speed of sound through the water column; and a conductivity, temperature, and depth (CTD) profiler to measure the salinity (related to conductivity), temperature, and pressure (related to depth) throughout the water column. The speed of sound in the ocean primarily depends on these three quantities. 

      To precisely measure the temperature across the entire water column at one location, they deployed an array of transistor-based temperature sensors developed by the laboratory’s Advanced Materials and Microsystems Group in collaboration with the Advanced Functional Fabrics of America Manufacturing Innovation Institute. The small temperature sensors run along the length of a thread-like polymer fiber embedded with multiple conductors. This fiber platform, which can support a broad range of sensors, can be unspooled hundreds of feet below the water’s surface to concurrently measure temperature or other water properties — the fiber deployed in the Arctic also contained accelerometers to measure depth — at many points in the water column. Traditionally, temperature profiling has required moving a device up and down through the water column.

      The team also deployed a high-frequency echosounder supplied by Anthony Lyons and Larry Mayer, collaborators at UNH’s Center for Coastal and Ocean Mapping. This active sonar uses acoustic energy to detect internal waves, or waves occurring beneath the ocean’s surface.

      “You may think of the ocean as a homogenous body of water, but it’s not,” Evans explains. “Different currents can exist as you go down in depth, much like how you can get different winds when you go up in altitude. The UNH echosounder allows us to see the different currents in the water column, as well as ice roughness when we turn the sensor to look upward.”

      “The reason we care about currents is that we believe they will tell us something about how warmer water from the Atlantic Ocean is coming into contact with sea ice,” adds Whelihan. “Not only is that water melting ice but it also has lower salt content, resulting in oceanic layers and affecting how long ice lasts and where it lasts.”

      Back home, the team has begun analyzing their data. For the seismic data, this analysis involves distinguishing any ice events from various sources of anthropogenic noise, including generators, snowmobiles, footsteps, and aircraft. Similarly, the researchers know their hydrophone array acoustic data are contaminated by energy from a sound source that another research team participating in ICEX placed in the water. Based on their physics, icequakes — the seismic events that occur when ice cracks — have characteristic signatures that can be used to identify them. One approach is to manually find an icequake and use that signature as a guide for finding other icequakes in the dataset.

      From their water column profiling sensors, they identified an interesting evolution in the sound speed profile 30 to 40 meters below the ocean surface, related to a mass of colder water moving in later in the day. The group’s physical oceanographer believes this change in the profile is due to water coming up from the Bering Sea, water that initially comes from the Atlantic Ocean. The UNH-supplied echosounder also generated an interesting signal at a similar depth.

      “Our supposition is that this result has something to do with the large sound speed variation we detected, either directly because of reflections off that layer or because of plankton, which tend to rise on top of that layer,” explains Evans.  

      A future predictive capability

      Going forward, the team will continue mining their collected data and use these data to begin building algorithms capable of automatically detecting and localizing — and ultimately predicting — ice events correlated with changes in environmental conditions. To complement their experimental data, they have initiated conversations with organizations that model the physical behavior of sea ice, including the National Oceanic and Atmospheric Administration and the National Ice Center. Merging the laboratory’s expertise in sensor design and signal processing with their expertise in ice physics would provide a more complete understanding of how the Arctic is changing.

      The laboratory team will also start exploring cost-effective engineering approaches for integrating the sensors into packages hardened for deployment in the harsh environment of the Arctic.

      “Until these sensors are truly unattended, the human factor of usability is front and center,” says Whelihan. “Because it’s so cold, equipment can break accidentally. For example, at ICEX 2022, our waterproof enclosure for the seismometers survived, but the enclosure for its power supply, which was made out of a cheaper plastic, shattered in my hand when I went to pick it up.”

      The sensor packages will not only need to withstand the frigid environment but also be able to “phone home” over some sort of satellite data link and sustain their power. The team plans to investigate whether waste heat from processing can keep the instruments warm and how energy could be harvested from the Arctic environment.

      Before the next ICEX scheduled for 2024, they hope to perform preliminary testing of their sensor packages and concepts in Arctic-like environments. While attending ICEX 2022, they engaged with several other attendees — including the U.S. Navy, Arctic Submarine Laboratory, National Ice Center, and University of Alaska Fairbanks (UAF) — and identified cold room experimentation as one area of potential collaboration. Testing can also be performed at outdoor locations a bit closer to home and more easily accessible, such as the Great Lakes in Michigan and a UAF-maintained site in Barrow, Alaska. In the future, the laboratory team may have an opportunity to accompany U.S. Coast Guard personnel on ice-breaking vessels traveling from Alaska to Greenland. The team is also thinking about possible venues for collecting data far removed from human noise sources.

      “Since I’ve told colleagues, friends, and family I was going to the Arctic, I’ve had a lot of interesting conversations about climate change and what we’re doing there and why we’re doing it,” Whelihan says. “People don’t have an intrinsic, automatic understanding of this environment and its impact because it’s so far removed from us. But the Arctic plays a crucial role in helping to keep the global climate in balance, so it’s imperative we understand the processes leading to sea ice fractures.”

      This work is funded through Lincoln Laboratory’s internally administered R&D portfolio on climate. More

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      in Data Management & Statistics

      In bias we trust?

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      When the stakes are high, machine-learning models are sometimes used to aid human decision-makers. For instance, a model could predict which law school applicants are most likely to pass the bar exam to help an admissions officer determine which students should be accepted.

      These models often have millions of parameters, so how they make predictions is nearly impossible for researchers to fully understand, let alone an admissions officer with no machine-learning experience. Researchers sometimes employ explanation methods that mimic a larger model by creating simple approximations of its predictions. These approximations, which are far easier to understand, help users determine whether to trust the model’s predictions.

      But are these explanation methods fair? If an explanation method provides better approximations for men than for women, or for white people than for Black people, it may encourage users to trust the model’s predictions for some people but not for others.

      MIT researchers took a hard look at the fairness of some widely used explanation methods. They found that the approximation quality of these explanations can vary dramatically between subgroups and that the quality is often significantly lower for minoritized subgroups.

      In practice, this means that if the approximation quality is lower for female applicants, there is a mismatch between the explanations and the model’s predictions that could lead the admissions officer to wrongly reject more women than men.

      Once the MIT researchers saw how pervasive these fairness gaps are, they tried several techniques to level the playing field. They were able to shrink some gaps, but couldn’t eradicate them.

      “What this means in the real-world is that people might incorrectly trust predictions more for some subgroups than for others. So, improving explanation models is important, but communicating the details of these models to end users is equally important. These gaps exist, so users may want to adjust their expectations as to what they are getting when they use these explanations,” says lead author Aparna Balagopalan, a graduate student in the Healthy ML group of the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL).

      Balagopalan wrote the paper with CSAIL graduate students Haoran Zhang and Kimia Hamidieh; CSAIL postdoc Thomas Hartvigsen; Frank Rudzicz, associate professor of computer science at the University of Toronto; and senior author Marzyeh Ghassemi, an assistant professor and head of the Healthy ML Group. The research will be presented at the ACM Conference on Fairness, Accountability, and Transparency.

      High fidelity

      Simplified explanation models can approximate predictions of a more complex machine-learning model in a way that humans can grasp. An effective explanation model maximizes a property known as fidelity, which measures how well it matches the larger model’s predictions.

      Rather than focusing on average fidelity for the overall explanation model, the MIT researchers studied fidelity for subgroups of people in the model’s dataset. In a dataset with men and women, the fidelity should be very similar for each group, and both groups should have fidelity close to that of the overall explanation model.

      “When you are just looking at the average fidelity across all instances, you might be missing out on artifacts that could exist in the explanation model,” Balagopalan says.

      They developed two metrics to measure fidelity gaps, or disparities in fidelity between subgroups. One is the difference between the average fidelity across the entire explanation model and the fidelity for the worst-performing subgroup. The second calculates the absolute difference in fidelity between all possible pairs of subgroups and then computes the average.

      With these metrics, they searched for fidelity gaps using two types of explanation models that were trained on four real-world datasets for high-stakes situations, such as predicting whether a patient dies in the ICU, whether a defendant reoffends, or whether a law school applicant will pass the bar exam. Each dataset contained protected attributes, like the sex and race of individual people. Protected attributes are features that may not be used for decisions, often due to laws or organizational policies. The definition for these can vary based on the task specific to each decision setting.

      The researchers found clear fidelity gaps for all datasets and explanation models. The fidelity for disadvantaged groups was often much lower, up to 21 percent in some instances. The law school dataset had a fidelity gap of 7 percent between race subgroups, meaning the approximations for some subgroups were wrong 7 percent more often on average. If there are 10,000 applicants from these subgroups in the dataset, for example, a significant portion could be wrongly rejected, Balagopalan explains.

      “I was surprised by how pervasive these fidelity gaps are in all the datasets we evaluated. It is hard to overemphasize how commonly explanations are used as a ‘fix’ for black-box machine-learning models. In this paper, we are showing that the explanation methods themselves are imperfect approximations that may be worse for some subgroups,” says Ghassemi.

      Narrowing the gaps

      After identifying fidelity gaps, the researchers tried some machine-learning approaches to fix them. They trained the explanation models to identify regions of a dataset that could be prone to low fidelity and then focus more on those samples. They also tried using balanced datasets with an equal number of samples from all subgroups.

      These robust training strategies did reduce some fidelity gaps, but they didn’t eliminate them.

      The researchers then modified the explanation models to explore why fidelity gaps occur in the first place. Their analysis revealed that an explanation model might indirectly use protected group information, like sex or race, that it could learn from the dataset, even if group labels are hidden.

      They want to explore this conundrum more in future work. They also plan to further study the implications of fidelity gaps in the context of real-world decision making.

      Balagopalan is excited to see that concurrent work on explanation fairness from an independent lab has arrived at similar conclusions, highlighting the importance of understanding this problem well.

      As she looks to the next phase in this research, she has some words of warning for machine-learning users.

      “Choose the explanation model carefully. But even more importantly, think carefully about the goals of using an explanation model and who it eventually affects,” she says.

      This work was funded, in part, by the MIT-IBM Watson AI Lab, the Quanta Research Institute, a Canadian Institute for Advanced Research AI Chair, and Microsoft Research. 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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      Is it topological? A new materials database has the answer

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      What will it take to make our electronics smarter, faster, and more resilient? One idea is to build them from materials that are topological.

      Topology stems from a branch of mathematics that studies shapes that can be manipulated or deformed without losing certain core properties. A donut is a common example: If it were made of rubber, a donut could be twisted and squeezed into a completely new shape, such as a coffee mug, while retaining a key trait — namely, its center hole, which takes the form of the cup’s handle. The hole, in this case, is a topological trait, robust against certain deformations.

      In recent years, scientists have applied concepts of topology to the discovery of materials with similarly robust electronic properties. In 2007, researchers predicted the first electronic topological insulators — materials in which electrons that behave in ways that are “topologically protected,” or persistent in the face of certain disruptions.

      Since then, scientists have searched for more topological materials with the aim of building better, more robust electronic devices. Until recently, only a handful of such materials were identified, and were therefore assumed to be a rarity.

      Now researchers at MIT and elsewhere have discovered that, in fact, topological materials are everywhere, if you know how to look for them.

      In a paper published today in Science, the team, led by Nicolas Regnault of Princeton University and the École Normale Supérieure Paris, reports harnessing the power of multiple supercomputers to map the electronic structure of more than 96,000 natural and synthetic crystalline materials. They applied sophisticated filters to determine whether and what kind of topological traits exist in each structure.

      Overall, they found that 90 percent of all known crystalline structures contain at least one topological property, and more than 50 percent of all naturally occurring materials exhibit some sort of topological behavior.

      “We found there’s a ubiquity — topology is everywhere,” says Benjamin Wieder, the study’s co-lead, and a postdoc in MIT’s Department of Physics.

      The team has compiled the newly identified materials into a new, freely accessible Topological Materials Database resembling a periodic table of topology. With this new library, scientists can quickly search materials of interest for any topological properties they might hold, and harness them to build ultra-low-power transistors, new magnetic memory storage, and other devices with robust electronic properties.

      The paper includes co-lead author Maia Vergniory of the Donostia International Physics Center, Luis Elcoro of the University of Basque Country, Stuart Parkin and Claudia Felser of the Max Planck Institute, and Andrei Bernevig of Princeton University.

      Beyond intuition

      The new study was motivated by a desire to speed up the traditional search for topological materials.

      “The way the original materials were found was through chemical intuition,” Wieder says. “That approach had a lot of early successes. But as we theoretically predicted more kinds of topological phases, it seemed intuition wasn’t getting us very far.”

      Wieder and his colleagues instead utilized an efficient and systematic method to root out signs of topology, or robust electronic behavior, in all known crystalline structures, also known as inorganic solid-state materials.

      For their study, the researchers looked to the Inorganic Crystal Structure Database, or ICSD, a repository into which researchers enter the atomic and chemical structures of crystalline materials that they have studied. The database includes materials found in nature, as well as those that have been synthesized and manipulated in the lab. The ICSD is currently the largest materials database in the world, containing over 193,000 crystals whose structures have been mapped and characterized.

      The team downloaded the entire ICSD, and after performing some data cleaning to weed out structures with corrupted files or incomplete data, the researchers were left with just over 96,000 processable structures. For each of these structures, they performed a set of calculations based on fundamental knowledge of the relation between chemical constituents, to produce a map of the material’s electronic structure, also known as the electron band structure.

      The team was able to efficiently carry out the complicated calculations for each structure using multiple supercomputers, which they then employed to perform a second set of operations, this time to screen for various known topological phases, or persistent electrical behavior in each crystal material.

      “We’re looking for signatures in the electronic structure in which certain robust phenomena should occur in this material,” explains Wieder, whose previous work involved refining and expanding the screening technique, known as topological quantum chemistry.

      From their high-throughput analysis, the team quickly discovered a surprisingly large number of materials that are naturally topological, without any experimental manipulation, as well as materials that can be manipulated, for instance with light or chemical doping, to exhibit some sort of robust electronic behavior. They also discovered a handful of materials that contained more than one topological state when exposed to certain conditions.

      “Topological phases of matter in 3D solid-state materials have been proposed as venues for observing and manipulating exotic effects, including the interconversion of electrical current and electron spin, the tabletop simulation of exotic theories from high-energy physics, and even, under the right conditions, the storage and manipulation of quantum information,” Wieder notes. 

      For experimentalists who are studying such effects, Wieder says the team’s new database now reveals a menagerie of new materials to explore.

      This research was funded, in part, by the U.S. Department of Energy, the National Science Foundation, and the Office of Naval Research. 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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      Zero-trust architecture may hold the answer to cybersecurity insider threats

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      For years, organizations have taken a defensive “castle-and-moat” approach to cybersecurity, seeking to secure the perimeters of their networks to block out any malicious actors. Individuals with the right credentials were assumed to be trustworthy and allowed access to a network’s systems and data without having to reauthorize themselves at each access attempt. However, organizations today increasingly store data in the cloud and allow employees to connect to the network remotely, both of which create vulnerabilities to this traditional approach. A more secure future may require a “zero-trust architecture,” in which users must prove their authenticity each time they access a network application or data.

      In May 2021, President Joe Biden’s Executive Order on Improving the Nation’s Cybersecurity outlined a goal for federal agencies to implement zero-trust security. Since then, MIT Lincoln Laboratory has been performing a study on zero-trust architectures, with the goals of reviewing their implementation in government and industry, identifying technical gaps and opportunities, and developing a set of recommendations for the United States’ approach to a zero-trust system.

      The study team’s first step was to define the term “zero trust” and understand the misperceptions in the field surrounding the concept. Some of these misperceptions suggest that a zero-trust architecture requires entirely new equipment to implement, or that it makes systems so “locked down” they’re not usable. 

      “Part of the reason why there is a lot of confusion about what zero trust is, is because it takes what the cybersecurity world has known about for many years and applies it in a different way,” says Jeffrey Gottschalk, the assistant head of Lincoln Laboratory’s Cyber Security and Information Sciences Division and study’s co-lead. “It is a paradigm shift in terms of how to think about security, but holistically it takes a lot of things that we already know how to do — such as multi-factor authentication, encryption, and software-defined networking­ — and combines them in different ways.”

      Play video

      Presentation: Overview of Zero Trust Architectures

      Recent high-profile cybersecurity incidents — such as those involving the National Security Agency, the U.S. Office of Personnel Management, Colonial Pipeline, SolarWinds, and Sony Pictures — highlight the vulnerability of systems and the need to rethink cybersecurity approaches.

      The study team reviewed recent, impactful cybersecurity incidents to identify which security principles were most responsible for the scale and impact of the attack. “We noticed that while a number of these attacks exploited previously unknown implementation vulnerabilities (also known as ‘zero-days’), the vast majority actually were due to the exploitation of operational security principles,” says Christopher Roeser, study co-lead and the assistant head of the Homeland Protection and Air Traffic Control Division, “that is, the gaining of individuals’ credentials, and the movement within a well-connected network that allows users to gather a significant amount of information or have very widespread effects.”

      In other words, the malicious actor had “breached the moat” and effectively became an insider.

      Zero-trust security principles could protect against this type of insider threat by treating every component, service, and user of a system as continuously exposed to and potentially compromised by a malicious actor. A user’s identity is verified each time that they request to access a new resource, and every access is mediated, logged, and analyzed. It’s like putting trip wires all over the inside of a network system, says Gottschalk. “So, when an adversary trips over that trip wire, you’ll get a signal and can validate that signal and see what’s going on.”

      In practice, a zero-trust approach could look like replacing a single-sign-on system, which lets users sign in just once for access to multiple applications, with a cloud-based identity that is known and verified. “Today, a lot of organizations have different ways that people authenticate and log onto systems, and many of those have been aggregated for expediency into single-sign-on capabilities, just to make it easier for people to log onto their systems. But we envision a future state that embraces zero trust, where identity verification is enabled by cloud-based identity that’s portable and ubiquitous, and very secure itself.”

      While conducting their study, the team spoke to approximately 10 companies and government organizations that have adopted zero-trust implementations — either through cloud services, in-house management, or a combination of both. They found the hybrid approach to be a good model for government organizations to adopt. They also found that the implementation could take from three to five years. “We talked to organizations that have actually done implementations of zero trust, and all of them have indicated that significant organizational commitment and change was required to be able to implement them,” Gottschalk says.

      But a key takeaway from the study is that there isn’t a one-size-fits-all approach to zero trust. “It’s why we think that having test-bed and pilot efforts are going to be very important to balance out zero-trust security with the mission needs of those systems,” Gottschalk says. The team also recognizes the importance of conducting ongoing research and development beyond initial zero-trust implementations, to continue to address evolving threats.

      Lincoln Laboratory will present further findings from the study at its upcoming Cyber Technology for National Security conference, which will be held June 28-29. The conference will also offer a short course for attendees to learn more about the benefits and implementations of zero-trust architectures.  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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      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