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      in Data Management & Statistics

      Does this artificial intelligence think like a human?

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      In machine learning, understanding why a model makes certain decisions is often just as important as whether those decisions are correct. For instance, a machine-learning model might correctly predict that a skin lesion is cancerous, but it could have done so using an unrelated blip on a clinical photo.

      While tools exist to help experts make sense of a model’s reasoning, often these methods only provide insights on one decision at a time, and each must be manually evaluated. Models are commonly trained using millions of data inputs, making it almost impossible for a human to evaluate enough decisions to identify patterns.

      Now, researchers at MIT and IBM Research have created a method that enables a user to aggregate, sort, and rank these individual explanations to rapidly analyze a machine-learning model’s behavior. Their technique, called Shared Interest, incorporates quantifiable metrics that compare how well a model’s reasoning matches that of a human.

      Shared Interest could help a user easily uncover concerning trends in a model’s decision-making — for example, perhaps the model often becomes confused by distracting, irrelevant features, like background objects in photos. Aggregating these insights could help the user quickly and quantitatively determine whether a model is trustworthy and ready to be deployed in a real-world situation.

      “In developing Shared Interest, our goal is to be able to scale up this analysis process so that you could understand on a more global level what your model’s behavior is,” says lead author Angie Boggust, a graduate student in the Visualization Group of the Computer Science and Artificial Intelligence Laboratory (CSAIL).

      Boggust wrote the paper with her advisor, Arvind Satyanarayan, an assistant professor of computer science who leads the Visualization Group, as well as Benjamin Hoover and senior author Hendrik Strobelt, both of IBM Research. The paper will be presented at the Conference on Human Factors in Computing Systems.

      Boggust began working on this project during a summer internship at IBM, under the mentorship of Strobelt. After returning to MIT, Boggust and Satyanarayan expanded on the project and continued the collaboration with Strobelt and Hoover, who helped deploy the case studies that show how the technique could be used in practice.

      Human-AI alignment

      Shared Interest leverages popular techniques that show how a machine-learning model made a specific decision, known as saliency methods. If the model is classifying images, saliency methods highlight areas of an image that are important to the model when it made its decision. These areas are visualized as a type of heatmap, called a saliency map, that is often overlaid on the original image. If the model classified the image as a dog, and the dog’s head is highlighted, that means those pixels were important to the model when it decided the image contains a dog.

      Shared Interest works by comparing saliency methods to ground-truth data. In an image dataset, ground-truth data are typically human-generated annotations that surround the relevant parts of each image. In the previous example, the box would surround the entire dog in the photo. When evaluating an image classification model, Shared Interest compares the model-generated saliency data and the human-generated ground-truth data for the same image to see how well they align.

      The technique uses several metrics to quantify that alignment (or misalignment) and then sorts a particular decision into one of eight categories. The categories run the gamut from perfectly human-aligned (the model makes a correct prediction and the highlighted area in the saliency map is identical to the human-generated box) to completely distracted (the model makes an incorrect prediction and does not use any image features found in the human-generated box).

      “On one end of the spectrum, your model made the decision for the exact same reason a human did, and on the other end of the spectrum, your model and the human are making this decision for totally different reasons. By quantifying that for all the images in your dataset, you can use that quantification to sort through them,” Boggust explains.

      The technique works similarly with text-based data, where key words are highlighted instead of image regions.

      Rapid analysis

      The researchers used three case studies to show how Shared Interest could be useful to both nonexperts and machine-learning researchers.

      In the first case study, they used Shared Interest to help a dermatologist determine if he should trust a machine-learning model designed to help diagnose cancer from photos of skin lesions. Shared Interest enabled the dermatologist to quickly see examples of the model’s correct and incorrect predictions. Ultimately, the dermatologist decided he could not trust the model because it made too many predictions based on image artifacts, rather than actual lesions.

      “The value here is that using Shared Interest, we are able to see these patterns emerge in our model’s behavior. In about half an hour, the dermatologist was able to make a confident decision of whether or not to trust the model and whether or not to deploy it,” Boggust says.

      In the second case study, they worked with a machine-learning researcher to show how Shared Interest can evaluate a particular saliency method by revealing previously unknown pitfalls in the model. Their technique enabled the researcher to analyze thousands of correct and incorrect decisions in a fraction of the time required by typical manual methods.

      In the third case study, they used Shared Interest to dive deeper into a specific image classification example. By manipulating the ground-truth area of the image, they were able to conduct a what-if analysis to see which image features were most important for particular predictions.   

      The researchers were impressed by how well Shared Interest performed in these case studies, but Boggust cautions that the technique is only as good as the saliency methods it is based upon. If those techniques contain bias or are inaccurate, then Shared Interest will inherit those limitations.

      In the future, the researchers want to apply Shared Interest to different types of data, particularly tabular data which is used in medical records. They also want to use Shared Interest to help improve current saliency techniques. Boggust hopes this research inspires more work that seeks to quantify machine-learning model behavior in ways that make sense to humans.

      This work is funded, in part, by the MIT-IBM Watson AI Lab, the United States Air Force Research Laboratory, and the United States Air Force Artificial Intelligence Accelerator. More

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      in Data Management & Statistics

      System helps severely motor-impaired individuals type more quickly and accurately

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      In 1995, French fashion magazine editor Jean-Dominique Bauby suffered a seizure while driving a car, which left him with a condition known as locked-in syndrome, a neurological disease in which the patient is completely paralyzed and can only move muscles that control the eyes.

      Bauby, who had signed a book contract shortly before his accident, wrote the memoir “The Diving Bell and the Butterfly” using a dictation system in which his speech therapist recited the alphabet and he would blink when she said the correct letter. They wrote the 130-page book one blink at a time.

      Technology has come a long way since Bauby’s accident. Many individuals with severe motor impairments caused by locked-in syndrome, cerebral palsy, amyotrophic lateral sclerosis, or other conditions can communicate using computer interfaces where they select letters or words in an onscreen grid by activating a single switch, often by pressing a button, releasing a puff of air, or blinking.

      But these row-column scanning systems are very rigid, and, similar to the technique used by Bauby’s speech therapist, they highlight each option one at a time, making them frustratingly slow for some users. And they are not suitable for tasks where options can’t be arranged in a grid, like drawing, browsing the web, or gaming.

      A more flexible system being developed by researchers at MIT places individual selection indicators next to each option on a computer screen. The indicators can be placed anywhere — next to anything someone might click with a mouse — so a user does not need to cycle through a grid of choices to make selections. The system, called Nomon, incorporates probabilistic reasoning to learn how users make selections, and then adjusts the interface to improve their speed and accuracy.

      Participants in a user study were able to type faster using Nomon than with a row-column scanning system. The users also performed better on a picture selection task, demonstrating how Nomon could be used for more than typing.

      “It is so cool and exciting to be able to develop software that has the potential to really help people. Being able to find those signals and turn them into communication as we are used to it is a really interesting problem,” says senior author Tamara Broderick, an associate professor in the MIT Department of Electrical Engineering and Computer Science (EECS) and a member of the Laboratory for Information and Decision Systems and the Institute for Data, Systems, and Society.

      Joining Broderick on the paper are lead author Nicholas Bonaker, an EECS graduate student; Emli-Mari Nel, head of innovation and machine learning at Averly and a visiting lecturer at the University of Witwatersrand in South Africa; and Keith Vertanen, an associate professor at Michigan Tech. The research is being presented at the ACM Conference on Human Factors in Computing Systems.

      On the clock

      In the Nomon interface, a small analog clock is placed next to every option the user can select. (A gnomon is the part of a sundial that casts a shadow.) The user looks at one option and then clicks their switch when that clock’s hand passes a red “noon” line. After each click, the system changes the phases of the clocks to separate the most probable next targets. The user clicks repeatedly until their target is selected.

      When used as a keyboard, Nomon’s machine-learning algorithms try to guess the next word based on previous words and each new letter as the user makes selections.

      Broderick developed a simplified version of Nomon several years ago but decided to revisit it to make the system easier for motor-impaired individuals to use. She enlisted the help of then-undergraduate Bonaker to redesign the interface.

      They first consulted nonprofit organizations that work with motor-impaired individuals, as well as a motor-impaired switch user, to gather feedback on the Nomon design.

      Then they designed a user study that would better represent the abilities of motor-impaired individuals. They wanted to make sure to thoroughly vet the system before using much of the valuable time of motor-impaired users, so they first tested on non-switch users, Broderick explains.

      Switching up the switch

      To gather more representative data, Bonaker devised a webcam-based switch that was harder to use than simply clicking a key. The non-switch users had to lean their bodies to one side of the screen and then back to the other side to register a click.

      “And they have to do this at precisely the right time, so it really slows them down. We did some empirical studies which showed that they were much closer to the response times of motor-impaired individuals,” Broderick says.

      They ran a 10-session user study with 13 non-switch participants and one single-switch user with an advanced form of spinal muscular dystrophy. In the first nine sessions, participants used Nomon and a row-column scanning interface for 20 minutes each to perform text entry, and in the 10th session they used the two systems for a picture selection task.

      Non-switch users typed 15 percent faster using Nomon, while the motor-impaired user typed even faster than the non-switch users. When typing unfamiliar words, the users were 20 percent faster overall and made half as many errors. In their final session, they were able to complete the picture selection task 36 percent faster using Nomon.

      “Nomon is much more forgiving than row-column scanning. With row-column scanning, even if you are just slightly off, now you’ve chosen B instead of A and that’s an error,” Broderick says.

      Adapting to noisy clicks

      With its probabilistic reasoning, Nomon incorporates everything it knows about where a user is likely to click to make the process faster, easier, and less error-prone. For instance, if the user selects “Q,” Nomon will make it as easy as possible for the user to select “U” next.

      Nomon also learns how a user clicks. So, if the user always clicks a little after the clock’s hand strikes noon, the system adapts to that in real time. It also adapts to noisiness. If a user’s click is often off the mark, the system requires extra clicks to ensure accuracy.

      This probabilistic reasoning makes Nomon powerful but also requires a higher click-load than row-column scanning systems. Clicking multiple times can be a trying task for severely motor-impaired users.

      Broderick hopes to reduce the click-load by incorporating gaze tracking into Nomon, which would give the system more robust information about what a user might choose next based on which part of the screen they are looking at. The researchers also want to find a better way to automatically adjust the clock speeds to help users be more accurate and efficient.

      They are working on a new series of studies in which they plan to partner with more motor-impaired users.

      “So far, the feedback from motor-impaired users has been invaluable to us; we’re very grateful to the motor-impaired user who commented on our initial interface and the separate motor-impaired user who participated in our study. We’re currently extending our study to work with a bigger and more diverse group of our target population. With their help, we’re already making further improvements to our interface and working to better understand the performance of Nomon,” she says.

      “Nonspeaking individuals with motor disabilities are currently not provided with efficient communication solutions for interacting with either speaking partners or computer systems. This ‘communication gap’ is a known unresolved problem in human-computer interaction, and so far there are no good solutions. This paper demonstrates that a highly creative approach underpinned by a statistical model can provide tangible performance gains to the users who need it the most: nonspeaking individuals reliant on a single switch to communicate,” says Per Ola Kristensson, professor of interactive systems engineering at Cambridge University, who was not involved with this research. “The paper also demonstrates the value of complementing insights from computational experiments with the involvement of end-users and other stakeholders in the design process. I find this a highly creative and important paper in an area where it is notoriously difficult to make significant progress.”

      This research was supported, in part, by the Seth Teller Memorial Fund to Advanced Technology for People with Disabilities, a Peter J. Eloranta Summer Undergraduate Research Fellowship, the MIT Quest for Intelligence, and the National Science Foundation. More

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      Generating new molecules with graph grammar

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      Chemical engineers and materials scientists are constantly looking for the next revolutionary material, chemical, and drug. The rise of machine-learning approaches is expediting the discovery process, which could otherwise take years. “Ideally, the goal is to train a machine-learning model on a few existing chemical samples and then allow it to produce as many manufacturable molecules of the same class as possible, with predictable physical properties,” says Wojciech Matusik, professor of electrical engineering and computer science at MIT. “If you have all these components, you can build new molecules with optimal properties, and you also know how to synthesize them. That’s the overall vision that people in that space want to achieve”

      However, current techniques, mainly deep learning, require extensive datasets for training models, and many class-specific chemical datasets contain a handful of example compounds, limiting their ability to generalize and generate physical molecules that could be created in the real world.

      Now, a new paper from researchers at MIT and IBM tackles this problem using a generative graph model to build new synthesizable molecules within the same chemical class as their training data. To do this, they treat the formation of atoms and chemical bonds as a graph and develop a graph grammar — a linguistics analogy of systems and structures for word ordering — that contains a sequence of rules for building molecules, such as monomers and polymers. Using the grammar and production rules that were inferred from the training set, the model can not only reverse engineer its examples, but can create new compounds in a systematic and data-efficient way. “We basically built a language for creating molecules,” says Matusik “This grammar essentially is the generative model.”

      Matusik’s co-authors include MIT graduate students Minghao Guo, who is the lead author, and Beichen Li as well as Veronika Thost, Payal Das, and Jie Chen, research staff members with IBM Research. Matusik, Thost, and Chen are affiliated with the MIT-IBM Watson AI Lab. Their method, which they’ve called data-efficient graph grammar (DEG), will be presented at the International Conference on Learning Representations.

      “We want to use this grammar representation for monomer and polymer generation, because this grammar is explainable and expressive,” says Guo. “With only a few number of the production rules, we can generate many kinds of structures.”

      A molecular structure can be thought of as a symbolic representation in a graph — a string of atoms (nodes) joined together by chemical bonds (edges). In this method, the researchers allow the model to take the chemical structure and collapse a substructure of the molecule down to one node; this may be two atoms connected by a bond, a short sequence of bonded atoms, or a ring of atoms. This is done repeatedly, creating the production rules as it goes, until a single node remains. The rules and grammar then could be applied in the reverse order to recreate the training set from scratch or combined in different combinations to produce new molecules of the same chemical class.

      “Existing graph generation methods would produce one node or one edge sequentially at a time, but we are looking at higher-level structures and, specifically, exploiting chemistry knowledge, so that we don’t treat the individual atoms and bonds as the unit. This simplifies the generation process and also makes it more data-efficient to learn,” says Chen.

      Further, the researchers optimized the technique so that the bottom-up grammar was relatively simple and straightforward, such that it fabricated molecules that could be made.

      “If we switch the order of applying these production rules, we would get another molecule; what’s more, we can enumerate all the possibilities and generate tons of them,” says Chen. “Some of these molecules are valid and some of them not, so the learning of the grammar itself is actually to figure out a minimal collection of production rules, such that the percentage of molecules that can actually be synthesized is maximized.” While the researchers concentrated on three training sets of less than 33 samples each — acrylates, chain extenders, and isocyanates — they note that the process could be applied to any chemical class.

      To see how their method performed, the researchers tested DEG against other state-of-the-art models and techniques, looking at percentages of chemically valid and unique molecules, diversity of those created, success rate of retrosynthesis, and percentage of molecules belonging to the training data’s monomer class.

      “We clearly show that, for the synthesizability and membership, our algorithm outperforms all the existing methods by a very large margin, while it’s comparable for some other widely-used metrics,” says Guo. Further, “what is amazing about our algorithm is that we only need about 0.15 percent of the original dataset to achieve very similar results compared to state-of-the-art approaches that train on tens of thousands of samples. Our algorithm can specifically handle the problem of data sparsity.”

      In the immediate future, the team plans to address scaling up this grammar learning process to be able to generate large graphs, as well as produce and identify chemicals with desired properties.

      Down the road, the researchers see many applications for the DEG method, as it’s adaptable beyond generating new chemical structures, the team points out. A graph is a very flexible representation, and many entities can be symbolized in this form — robots, vehicles, buildings, and electronic circuits, for example. “Essentially, our goal is to build up our grammar, so that our graphic representation can be widely used across many different domains,” says Guo, as “DEG can automate the design of novel entities and structures,” says Chen.

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

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      Fighting discrimination in mortgage lending

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      Although the U.S. Equal Credit Opportunity Act prohibits discrimination in mortgage lending, biases still impact many borrowers. One 2021 Journal of Financial Economics study found that borrowers from minority groups were charged interest rates that were nearly 8 percent higher and were rejected for loans 14 percent more often than those from privileged groups.

      When these biases bleed into machine-learning models that lenders use to streamline decision-making, they can have far-reaching consequences for housing fairness and even contribute to widening the racial wealth gap.

      If a model is trained on an unfair dataset, such as one in which a higher proportion of Black borrowers were denied loans versus white borrowers with the same income, credit score, etc., those biases will affect the model’s predictions when it is applied to real situations. To stem the spread of mortgage lending discrimination, MIT researchers created a process that removes bias in data that are used to train these machine-learning models.

      While other methods try to tackle this bias, the researchers’ technique is new in the mortgage lending domain because it can remove bias from a dataset that has multiple sensitive attributes, such as race and ethnicity, as well as several “sensitive” options for each attribute, such as Black or white, and Hispanic or Latino or non-Hispanic or Latino. Sensitive attributes and options are features that distinguish a privileged group from an underprivileged group.

      The researchers used their technique, which they call DualFair, to train a machine-learning classifier that makes fair predictions of whether borrowers will receive a mortgage loan. When they applied it to mortgage lending data from several U.S. states, their method significantly reduced the discrimination in the predictions while maintaining high accuracy.

      “As Sikh Americans, we deal with bias on a frequent basis and we think it is unacceptable to see that transform to algorithms in real-world applications. For things like mortgage lending and financial systems, it is very important that bias not infiltrate these systems because it can emphasize the gaps that are already in place against certain groups,” says Jashandeep Singh, a senior at Floyd Buchanan High School and co-lead author of the paper with his twin brother, Arashdeep. The Singh brothers were recently accepted into MIT.

      Joining Arashdeep and Jashandeep Singh on the paper are MIT sophomore Ariba Khan and senior author Amar Gupta, a researcher in the Computer Science and Artificial Intelligence Laboratory at MIT, who studies the use of evolving technology to address inequity and other societal issues. The research was recently published online and will appear in a special issue of Machine Learning and Knowledge Extraction.

      Double take

      DualFair tackles two types of bias in a mortgage lending dataset — label bias and selection bias. Label bias occurs when the balance of favorable or unfavorable outcomes for a particular group is unfair. (Black applicants are denied loans more frequently than they should be.) Selection bias is created when data are not representative of the larger population. (The dataset only includes individuals from one neighborhood where incomes are historically low.)

      The DualFair process eliminates label bias by subdividing a dataset into the largest number of subgroups based on combinations of sensitive attributes and options, such as white men who are not Hispanic or Latino, Black women who are Hispanic or Latino, etc.

      By breaking down the dataset into as many subgroups as possible, DualFair can simultaneously address discrimination based on multiple attributes.

      “Researchers have mostly tried to classify biased cases as binary so far. There are multiple parameters to bias, and these multiple parameters have their own impact in different cases. They are not equally weighed. Our method is able to calibrate it much better,” says Gupta.

      After the subgroups have been generated, DualFair evens out the number of borrowers in each subgroup by duplicating individuals from minority groups and deleting individuals from the majority group. DualFair then balances the proportion of loan acceptances and rejections in each subgroup so they match the median in the original dataset before recombining the subgroups.

      DualFair then eliminates selection bias by iterating on each data point to see if discrimination is present. For instance, if an individual is a non-Hispanic or Latino Black woman who was rejected for a loan, the system will adjust her race, ethnicity, and gender one at a time to see if the outcome changes. If this borrower is granted a loan when her race is changed to white, DualFair considers that data point biased and removes it from the dataset.

      Fairness vs. accuracy

      To test DualFair, the researchers used the publicly available Home Mortgage Disclosure Act dataset, which spans 88 percent of all mortgage loans in the U.S. in 2019, and includes 21 features, including race, sex, and ethnicity. They used DualFair to “de-bias” the entire dataset and smaller datasets for six states, and then trained a machine-learning model to predict loan acceptances and rejections.

      After applying DualFair, the fairness of predictions increased while the accuracy level remained high across all states. They used an existing fairness metric known as average odds difference, but it can only measure fairness in one sensitive attribute at a time.

      So, they created their own fairness metric, called alternate world index, that considers bias from multiple sensitive attributes and options as a whole. Using this metric, they found that DualFair increased fairness in predictions for four of the six states while maintaining high accuracy.

      “It is the common belief that if you want to be accurate, you have to give up on fairness, or if you want to be fair, you have to give up on accuracy. We show that we can make strides toward lessening that gap,” Khan says.

      The researchers now want to apply their method to de-bias different types of datasets, such as those that capture health care outcomes, car insurance rates, or job applications. They also plan to address limitations of DualFair, including its instability when there are small amounts of data with multiple sensitive attributes and options.

      While this is only a first step, the researchers are hopeful their work can someday have an impact on mitigating bias in lending and beyond.

      “Technology, very bluntly, works only for a certain group of people. In the mortgage loan domain in particular, African American women have been historically discriminated against. We feel passionate about making sure that systemic racism does not extend to algorithmic models. There is no point in making an algorithm that can automate a process if it doesn’t work for everyone equally,” says Khan.

      This research is supported, in part, by the FinTech@CSAIL initiative. More

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      Security tool guarantees privacy in surveillance footage

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      Surveillance cameras have an identity problem, fueled by an inherent tension between utility and privacy. As these powerful little devices have cropped up seemingly everywhere, the use of machine learning tools has automated video content analysis at a massive scale — but with increasing mass surveillance, there are currently no legally enforceable rules to limit privacy invasions. 

      Security cameras can do a lot — they’ve become smarter and supremely more competent than their ghosts of grainy pictures past, the ofttimes “hero tool” in crime media. (“See that little blurry blue blob in the right hand corner of that densely populated corner — we got him!”) Now, video surveillance can help health officials measure the fraction of people wearing masks, enable transportation departments to monitor the density and flow of vehicles, bikes, and pedestrians, and provide businesses with a better understanding of shopping behaviors. But why has privacy remained a weak afterthought? 

      The status quo is to retrofit video with blurred faces or black boxes. Not only does this prevent analysts from asking some genuine queries (e.g., Are people wearing masks?), it also doesn’t always work; the system may miss some faces and leave them unblurred for the world to see. Dissatisfied with this status quo, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), in collaboration with other institutions, came up with a system to better guarantee privacy in video footage from surveillance cameras. Called “Privid,” the system lets analysts submit video data queries, and adds a little bit of noise (extra data) to the end result to ensure that an individual can’t be identified. The system builds on a formal definition of privacy — “differential privacy” — which allows access to aggregate statistics about private data without revealing personally identifiable information.

      Typically, analysts would just have access to the entire video to do whatever they want with it, but Privid makes sure the video isn’t a free buffet. Honest analysts can get access to the information they need, but that access is restrictive enough that malicious analysts can’t do too much with it. To enable this, rather than running the code over the entire video in one shot, Privid breaks the video into small pieces and runs processing code over each chunk. Instead of getting results back from each piece, the segments are aggregated, and that additional noise is added. (There’s also information on the error bound you’re going to get on your result — maybe a 2 percent error margin, given the extra noisy data added). 

      For example, the code might output the number of people observed in each video chunk, and the aggregation might be the “sum,” to count the total number of people wearing face coverings, or the “average” to estimate the density of crowds. 

      Privid allows analysts to use their own deep neural networks that are commonplace for video analytics today. This gives analysts the flexibility to ask questions that the designers of Privid did not anticipate. Across a variety of videos and queries, Privid was accurate within 79 to 99 percent of a non-private system.

      “We’re at a stage right now where cameras are practically ubiquitous. If there’s a camera on every street corner, every place you go, and if someone could actually process all of those videos in aggregate, you can imagine that entity building a very precise timeline of when and where a person has gone,” says MIT CSAIL PhD student ​​Frank Cangialosi, the lead author on a paper about Privid. “People are already worried about location privacy with GPS — video data in aggregate could capture not only your location history, but also moods, behaviors, and more at each location.” 

      Privid introduces a new notion of “duration-based privacy,” which decouples the definition of privacy from its enforcement — with obfuscation, if your privacy goal is to protect all people, the enforcement mechanism needs to do some work to find the people to protect, which it may or may not do perfectly. With this mechanism, you don’t need to fully specify everything, and you’re not hiding more information than you need to. 

      Let’s say we have a video overlooking a street. Two analysts, Alice and Bob, both claim they want to count the number of people that pass by each hour, so they submit a video processing module and ask for a sum aggregation.

      The first analyst is the city planning department, which hopes to use this information to understand footfall patterns and plan sidewalks for the city. Their model counts people and outputs this count for each video chunk.

      The other analyst is malicious. They hope to identify every time “Charlie” passes by the camera. Their model only looks for Charlie’s face, and outputs a large number if Charlie is present (i.e., the “signal” they’re trying to extract), or zero otherwise. Their hope is that the sum will be non-zero if Charlie was present. 

      From Privid’s perspective, these two queries look identical. It’s hard to reliably determine what their models might be doing internally, or what the analyst hopes to use the data for. This is where the noise comes in. Privid executes both of the queries, and adds the same amount of noise for each. In the first case, because Alice was counting all people, this noise will only have a small impact on the result, but likely won’t impact the usefulness. 

      In the second case, since Bob was looking for a specific signal (Charlie was only visible for a few chunks), the noise is enough to prevent them from knowing if Charlie was there or not. If they see a non-zero result, it might be because Charlie was actually there, or because the model outputs “zero,” but the noise made it non-zero. Privid didn’t need to know anything about when or where Charlie appeared, the system just needed to know a rough upper bound on how long Charlie might appear for, which is easier to specify than figuring out the exact locations, which prior methods rely on. 

      The challenge is determining how much noise to add — Privid wants to add just enough to hide everyone, but not so much that it would be useless for analysts. Adding noise to the data and insisting on queries over time windows means that your result isn’t going to be as accurate as it could be, but the results are still useful while providing better privacy. 

      Cangialosi wrote the paper with Princeton PhD student Neil Agarwal, MIT CSAIL PhD student Venkat Arun, assistant professor at the University of Chicago Junchen Jiang, assistant professor at Rutgers University and former MIT CSAIL postdoc Srinivas Narayana, associate professor at Rutgers University Anand Sarwate, and assistant professor at Princeton University and Ravi Netravali SM ’15, PhD ’18. Cangialosi will present the paper at the USENIX Symposium on Networked Systems Design and Implementation Conference in April in Renton, Washington. 

      This work was partially supported by a Sloan Research Fellowship and National Science Foundation grants. More

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      A tool for predicting the future

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      Whether someone is trying to predict tomorrow’s weather, forecast future stock prices, identify missed opportunities for sales in retail, or estimate a patient’s risk of developing a disease, they will likely need to interpret time-series data, which are a collection of observations recorded over time.

      Making predictions using time-series data typically requires several data-processing steps and the use of complex machine-learning algorithms, which have such a steep learning curve they aren’t readily accessible to nonexperts.

      To make these powerful tools more user-friendly, MIT researchers developed a system that directly integrates prediction functionality on top of an existing time-series database. Their simplified interface, which they call tspDB (time series predict database), does all the complex modeling behind the scenes so a nonexpert can easily generate a prediction in only a few seconds.

      The new system is more accurate and more efficient than state-of-the-art deep learning methods when performing two tasks: predicting future values and filling in missing data points.

      One reason tspDB is so successful is that it incorporates a novel time-series-prediction algorithm, explains electrical engineering and computer science (EECS) graduate student Abdullah Alomar, an author of a recent research paper in which he and his co-authors describe the algorithm. This algorithm is especially effective at making predictions on multivariate time-series data, which are data that have more than one time-dependent variable. In a weather database, for instance, temperature, dew point, and cloud cover each depend on their past values.

      The algorithm also estimates the volatility of a multivariate time series to provide the user with a confidence level for its predictions.

      “Even as the time-series data becomes more and more complex, this algorithm can effectively capture any time-series structure out there. It feels like we have found the right lens to look at the model complexity of time-series data,” says senior author Devavrat Shah, the Andrew and Erna Viterbi Professor in EECS and a member of the Institute for Data, Systems, and Society and of the Laboratory for Information and Decision Systems.

      Joining Alomar and Shah on the paper is lead author Anish Agrawal, a former EECS graduate student who is currently a postdoc at the Simons Institute at the University of California at Berkeley. The research will be presented at the ACM SIGMETRICS conference.

      Adapting a new algorithm

      Shah and his collaborators have been working on the problem of interpreting time-series data for years, adapting different algorithms and integrating them into tspDB as they built the interface.

      About four years ago, they learned about a particularly powerful classical algorithm, called singular spectrum analysis (SSA), that imputes and forecasts single time series. Imputation is the process of replacing missing values or correcting past values. While this algorithm required manual parameter selection, the researchers suspected it could enable their interface to make effective predictions using time series data. In earlier work, they removed this need to manually intervene for algorithmic implementation.  

      The algorithm for single time series transformed it into a matrix and utilized matrix estimation procedures. The key intellectual challenge was how to adapt it to utilize multiple time series.  After a few years of struggle, they realized the answer was something very simple: “Stack” the matrices for each individual time series, treat it as a one big matrix, and then apply the single time-series algorithm on it.

      This utilizes information across multiple time series naturally — both across the time series and across time, which they describe in their new paper.

      This recent publication also discusses interesting alternatives, where instead of transforming the multivariate time series into a big matrix, it is viewed as a three-dimensional tensor. A tensor is a multi-dimensional array, or grid, of numbers. This established a promising connection between the classical field of time series analysis and the growing field of tensor estimation, Alomar says.

      “The variant of mSSA that we introduced actually captures all of that beautifully. So, not only does it provide the most likely estimation, but a time-varying confidence interval, as well,” Shah says.

      The simpler, the better

      They tested the adapted mSSA against other state-of-the-art algorithms, including deep-learning methods, on real-world time-series datasets with inputs drawn from the electricity grid, traffic patterns, and financial markets.

      Their algorithm outperformed all the others on imputation and it outperformed all but one of the other algorithms when it came to forecasting future values. The researchers also demonstrated that their tweaked version of mSSA can be applied to any kind of time-series data.

      “One reason I think this works so well is that the model captures a lot of time series dynamics, but at the end of the day, it is still a simple model. When you are working with something simple like this, instead of a neural network that can easily overfit the data, you can actually perform better,” Alomar says.

      The impressive performance of mSSA is what makes tspDB so effective, Shah explains. Now, their goal is to make this algorithm accessible to everyone.

      One a user installs tspDB on top of an existing database, they can run a prediction query with just a few keystrokes in about 0.9 milliseconds, as compared to 0.5 milliseconds for a standard search query. The confidence intervals are also designed to help nonexperts to make a more informed decision by incorporating the degree of uncertainty of the predictions into their decision making.

      For instance, the system could enable a nonexpert to predict future stock prices with high accuracy in just a few minutes, even if the time-series dataset contains missing values.

      Now that the researchers have shown why mSSA works so well, they are targeting new algorithms that can be incorporated into tspDB. One of these algorithms utilizes the same model to automatically enable change point detection, so if the user believes their time series will change its behavior at some point, the system will automatically detect that change and incorporate that into its predictions.

      They also want to continue gathering feedback from current tspDB users to see how they can improve the system’s functionality and user-friendliness, Shah says.

      “Our interest at the highest level is to make tspDB a success in the form of a broadly utilizable, open-source system. Time-series data are very important, and this is a beautiful concept of actually building prediction functionalities directly into the database. It has never been done before, and so we want to make sure the world uses it,” he says.

      “This work is very interesting for a number of reasons. It provides a practical variant of mSSA which requires no hand tuning, they provide the first known analysis of mSSA, and the authors demonstrate the real-world value of their algorithm by being competitive with or out-performing several known algorithms for imputations and predictions in (multivariate) time series for several real-world data sets,” says Vishal Misra, a professor of computer science at Columbia University who was not involved with this research. “At the heart of it all is the beautiful modeling work where they cleverly exploit correlations across time (within a time series) and space (across time series) to create a low-rank spatiotemporal factor representation of a multivariate time series. Importantly this model connects the field of time series analysis to that of the rapidly evolving topic of tensor completion, and I expect a lot of follow-on research spurred by this paper.” More

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      Study: With masking and distancing in place, NFL stadium openings in 2020 had no impact on local Covid-19 infections

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      As with most everything in the world, football looked very different in 2020. As the Covid-19 pandemic unfolded, many National Football League (NFL) games were played in empty stadiums, while other stadiums opened to fans at significantly reduced capacity, with strict safety protocols in place.

      At the time it was unclear what impact such large sporting events would have on Covid-19 case counts, particularly at a time when vaccination against the virus was not widely available.

      Now, MIT engineers have taken a look back at the NFL’s 2020 regular season and found that for this specific period during the pandemic, opening stadiums to fans while requiring face coverings, social distancing, and other measures had no impact on the number of Covid-19 infections in those stadiums’ local counties.

      As they write in a new paper appearing this week in the Proceedings of the National Academy of Sciences, “the benefits of providing a tightly controlled outdoor spectating environment — including masking and distancing requirements — counterbalanced the risks associated with opening.”

      The study concentrates on the NFL’s 2020 regular season (September 2020 to early January 2021), at a time when earlier strains of the virus dominated, before the rise of more transmissible Delta and Omicron variants. Nevertheless, the results may inform decisions on whether and how to hold large outdoor gatherings in the face of future public health crises.

      “These results show that the measures adopted by the NFL were effective in safely opening stadiums,” says study author Anette “Peko” Hosoi, the Neil and Jane Pappalardo Professor of Mechanical Engineering at MIT. “If case counts start to rise again, we know what to do: mask people, put them outside, and distance them from each other.”

      The study’s co-authors are members of MIT’s Institue for Data, Systems, and Society (IDSS), and include Bernardo García Bulle, Dennis Shen, and Devavrat Shah, the Andrew and Erna Viterbi Professor in the Department of Electrical Engineering and Computer Science (EECS).

      Preseason patterns

      Last year a group led by the University of Southern Mississippi compared Covid-19 case counts in the counties of NFL stadiums that allowed fans in, versus those that did not. Their analysis showed that stadiums that opened to large numbers of fans led to “tangible increases” in the local county’s number of Covid-19 cases.

      But there are a number of factors in addition to a stadium’s opening that can affect case counts, including local policies, mandates, and attitudes. As the MIT team writes, “it is not at all obvious that one can attribute the differences in case spikes to the stadiums given the enormous number of confounding factors.”

      To truly isolate the effects of a stadium’s opening, one could imagine tracking Covid cases in a county with an open stadium through the 2020 season, then turning back the clock, closing the stadium, then tracking that same county’s Covid cases through the same season, all things being equal.

      “That’s the perfect experiment, with the exception that you would need a time machine,” Hosoi says.

      As it turns out, the next best thing is synthetic control — a statistical method that is used to determine the effect of an “intervention” (such as the opening of a stadium) compared with the exact same scenario without that intervention.

      In synthetic control, researchers use a weighted combination of groups to construct a “synthetic” version of an actual  scenario. In this case, the actual scenario is a county such as Dallas that hosts an open stadium. A synthetic version would be a county that looks similar to Dallas, only without a stadium. In the context of this study, a county that “looks” like Dallas has a similar preseason pattern of Covid-19 cases.

      To construct a synthetic Dallas, the researchers looked for surrounding counties without stadiums, that had similar Covid-19 trajectories leading up to the 2020 football season. They combined these counties in a way that best fit Dallas’ actual case trajectory. They then used data from the combined counties to calculate the number of Covid cases for this synthetic Dallas through the season, and compared these counts to the real Dallas.

      The team carried out this analysis for every “stadium county.” They determined a county to be a stadium county if more than 10 percent of a stadium’s fans came from that county, which the researchers estimated based on attendance data provided by the NFL.

      “Go outside”

      Of the stadiums included in the study, 13 were closed through the regular season, while 16 opened with reduced capacity and multiple pandemic requirements in place, such as required masking, distanced seating, mobile ticketing, and enhanced cleaning protocols.

      The researchers found the trajectory of infections in all stadium counties mirrored that of synthetic counties, showing that the number of infections would have been the same if the stadiums had remained closed. In other words, they found no evidence that NFL stadium openings led to any increase in local Covid case counts.

      To check that their method wasn’t missing any case spikes, they tested it on a known superspreader: the Sturgis Motorcycle Rally, which was held in August of 2020. The analysis successfully picked up an increase in cases in Meade, the host county, compared to a synthetic counterpart, in the two weeks following the rally.

      Surprisingly, the researchers found that several stadium counties’ case counts dipped slightly compared to their synthetic counterparts. In these counties — including Hamilton, Ohio, home of the Cincinnati Bengals — it appeared that opening the stadium to fans was tied to a dip in Covid-19 infections. Hosoi has a guess as to why:

      “These are football communities with dedicated fans. Rather than stay home alone, those fans may have gone to a sports bar or hosted indoor football gatherings if the stadium had not opened,” Hosoi proposes. “Opening the stadium under those circumstances would have been beneficial to the community because it makes people go outside.”

      The team’s analysis also revealed another connection: Counties with similar Covid trajectories also shared similar politics. To illustrate this point, the team mapped the county-wide temporal trajectories of Covid case counts in Ohio in 2020 and found them to be a strong predictor of the state’s 2020 electoral map.

      “That is not a coincidence,” Hosoi notes. “It tells us that local political leanings determined the temporal trajectory of the pandemic.”

      The team plans to apply their analysis to see how other factors may have influenced the pandemic.

      “Covid is a different beast [today],” she says. “Omicron is more transmissive, and more of the population is vaccinated. It’s possible we’d find something different if we ran this analysis on the upcoming season, and I think we probably should try.” More

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      How artificial intelligence can help combat systemic racism

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      In 2020, Detroit police arrested a Black man for shoplifting almost $4,000 worth of watches from an upscale boutique. He was handcuffed in front of his family and spent a night in lockup. After some questioning, however, it became clear that they had the wrong man. So why did they arrest him in the first place?

      The reason: a facial recognition algorithm had matched the photo on his driver’s license to grainy security camera footage.

      Facial recognition algorithms — which have repeatedly been demonstrated to be less accurate for people with darker skin — are just one example of how racial bias gets replicated within and perpetuated by emerging technologies.

      “There’s an urgency as AI is used to make really high-stakes decisions,” says MLK Visiting Professor S. Craig Watkins, whose academic home for his time at MIT is the Institute for Data, Systems, and Society (IDSS). “The stakes are higher because new systems can replicate historical biases at scale.”

      Watkins, a professor at the University of Texas at Austin and the founding director of the Institute for Media Innovation​, researches the impacts of media and data-based systems on human behavior, with a specific concentration on issues related to systemic racism. “One of the fundamental questions of the work is: how do we build AI models that deal with systemic inequality more effectively?”

      Play video

      Artificial Intelligence and the Future of Racial Justice | S. Craig Watkins | TEDxMIT

      Ethical AI

      Inequality is perpetuated by technology in many ways across many sectors. One broad domain is health care, where Watkins says inequity shows up in both quality of and access to care. The demand for mental health care, for example, far outstrips the capacity for services in the United States. That demand has been exacerbated by the pandemic, and access to care is harder for communities of color.

      For Watkins, taking the bias out of the algorithm is just one component of building more ethical AI. He works also to develop tools and platforms that can address inequality outside of tech head-on. In the case of mental health access, this entails developing a tool to help mental health providers deliver care more efficiently.

      “We are building a real-time data collection platform that looks at activities and behaviors and tries to identify patterns and contexts in which certain mental states emerge,” says Watkins. “The goal is to provide data-informed insights to care providers in order to deliver higher-impact services.”

      Watkins is no stranger to the privacy concerns such an app would raise. He takes a user-centered approach to the development that is grounded in data ethics. “Data rights are a significant component,” he argues. “You have to give the user complete control over how their data is shared and used and what data a care provider sees. No one else has access.”

      Combating systemic racism

      Here at MIT, Watkins has joined the newly launched Initiative on Combatting Systemic Racism (ICSR), an IDSS research collaboration that brings together faculty and researchers from the MIT Stephen A. Schwarzman College of Computing and beyond. The aim of the ICSR is to develop and harness computational tools that can help effect structural and normative change toward racial equity.

      The ICSR collaboration has separate project teams researching systemic racism in different sectors of society, including health care. Each of these “verticals” addresses different but interconnected issues, from sustainability to employment to gaming. Watkins is a part of two ICSR groups, policing and housing, that aim to better understand the processes that lead to discriminatory practices in both sectors. “Discrimination in housing contributes significantly to the racial wealth gap in the U.S.,” says Watkins.

      The policing team examines patterns in how different populations get policed. “There is obviously a significant and charged history to policing and race in America,” says Watkins. “This is an attempt to understand, to identify patterns, and note regional differences.”

      Watkins and the policing team are building models using data that details police interventions, responses, and race, among other variables. The ICSR is a good fit for this kind of research, says Watkins, who notes the interdisciplinary focus of both IDSS and the SCC. 

      “Systemic change requires a collaborative model and different expertise,” says Watkins. “We are trying to maximize influence and potential on the computational side, but we won’t get there with computation alone.”

      Opportunities for change

      Models can also predict outcomes, but Watkins is careful to point out that no algorithm alone will solve racial challenges.

      “Models in my view can inform policy and strategy that we as humans have to create. Computational models can inform and generate knowledge, but that doesn’t equate with change.” It takes additional work — and additional expertise in policy and advocacy — to use knowledge and insights to strive toward progress.

      One important lever of change, he argues, will be building a more AI-literate society through access to information and opportunities to understand AI and its impact in a more dynamic way. He hopes to see greater data rights and greater understanding of how societal systems impact our lives.

      “I was inspired by the response of younger people to the murders of George Floyd and Breonna Taylor,” he says. “Their tragic deaths shine a bright light on the real-world implications of structural racism and has forced the broader society to pay more attention to this issue, which creates more opportunities for change.” More