Nature Climate Change, Published online: 04 November 2025; doi:10.1038/s41558-025-02460-5

This study explores pathways of emissions and mitigation compatible with four climate boundaries—planetary boundaries for the climate system. The results highlight the importance of peak emission timing, limitation of carbon budgets as a sole indicator and trade-offs between mitigation options.

Amazon Ring’s upcoming face recognition tool has the potential to violate the privacy rights of millions of people and could result in Amazon breaking state biometric privacy laws.

Ring plans to introduce a feature to its home surveillance cameras called “Familiar Faces,” to identify specific people who come into view of the camera. When turned on, the feature will scan the faces of all people who approach the camera to try and find a match with a list of pre-saved faces. This will include many people who have not consented to a face scan, including friends and family, political canvassers, postal workers, delivery drivers, children selling cookies, or maybe even some people passing on the sidewalk.

When turned on, the feature will scan the faces of all people who approach the camera.

Many biometric privacy laws across the country are clear: Companies need your affirmative consent before running face recognition on you. In at least one state, ordinary people with the help of attorneys can challenge Amazon’s data collection. Where not possible, state privacy regulators should step in.

Sen. Ed Markey (D-Mass.) has already called on Amazon to abandon its plans and sent the company a list of questions. Ring spokesperson Emma Daniels answered written questions posed by EFF, which can be viewed here.

What is Ring’s “Familiar Faces”?

Amazon describes “Familiar Faces” as a tool that “intelligently recognizes familiar people.” It says this tool will provide camera owners with “personalized context of who is detected, eliminating guesswork and making it effortless to find and review important moments involving specific familiar people.” Amazon plans to release the feature in December.

The feature will allow camera owners to tag particular people so Ring cameras can automatically recognize them in the future. In order for Amazon to recognize particular people, it will need to perform face recognition on every person that steps in front of the camera. Even if a camera owner does not tag a particular face, Amazon says it may retain that biometric information for up to six months. Amazon said it does not currently use the biometric data for “model training or algorithmic purposes.”

In order to biometrically identify you, a company typically will take your image and extract a faceprint by taking tiny measurements of your face and converting that into a series of numbers that is saved for later. When you step in front of a camera again, the company takes a new faceprint and compares it to a list of previous prints to find a match. Other forms of biometric tracking can be done with a scan of your fingertip, eyeball, or even your particular gait.

Amazon has told reporters that the feature will be off by default and that it would be unavailable in certain jurisdictions with the most active biometric privacy enforcement—including the states of Illinois and Texas, and the city of Portland, Oregon. The company would not promise that this feature will remain off by default in the future.

Why is This a Privacy Problem?

Your biometric data, such as your faceprint, are some of the most sensitive pieces of data that a company can collect. Associated risks include mass surveillance, data breach, and discrimination.

Today’s feature to recognize your friend at your front door can easily be repurposed tomorrow for mass surveillance. Ring’s close partnership with police amplifies that threat. For example, in a city dense with face recognition cameras, the entirety of a person’s movements could be tracked with the click of a button, or all people could be identified at a particular location. A recent and unrelated private-public partnership in New Orleans unfortunately shows that mass surveillance through face recognition is not some far flung concern.

Amazon has already announced a related tool called “search party” that can identify and track lost dogs using neighbors’ cameras. A tool like this could be repurposed for law enforcement to track people. At least for now, Amazon says it does not have the technical capability to comply with law enforcement demanding a list of all cameras in which a person has been identified. Though, it complies with other law enforcement demands.

In addition, data breaches are a perpetual concern with any data collection. Biometrics magnify that risk because your face cannot be reset, unlike a password or credit card number. Amazon says it processes and stores biometrics collected by Ring cameras on its own servers, and that it uses comprehensive security measure to protect the data.

Face recognition has also been shown to have higher error rates with certain groups—most prominently with dark-skinned women. Similar technology has also been used to make questionable guesses about a person’s emotions, age, and gender.

Will Ring’s “Familiar Faces” Violate State Biometric Laws?

Any Ring collection of biometric information in states that require opt-in consent poses huge legal risk for the company. Amazon already told reporters that the feature will not be available in Illinois and Texas—strongly suggesting its feature could not survive legal scrutiny there. The company said it is also avoiding Portland, Oregon, which has a biometric privacy law that similar companies have avoided.

Its “familiar faces” feature will necessarily require its cameras to collect a faceprint from of every person who comes into view of an enabled camera, to try and find a match. It is impossible for Amazon to obtain consent from everyone—especially people who do not own Ring cameras. It appears that Amazon will try to unload some consent requirements onto individual camera owners themselves. Amazon says it will provide in-app messages to customers, reminding them to comply with applicable laws. But Amazon—as a company itself collecting, processing, and storing this biometric data—could have its own consent obligations under numerous laws.

Lawsuits against similar features highlight Amazon’s legal risks. In Texas, Google paid $1.375 billion to settle a lawsuit that alleged, among other things, that Google’s Nest cameras "indiscriminately capture the face geometry of any Texan who happens to come into view, including non-users." In Illinois, Facebook paid $650 million and shut down its face recognition tools that automatically scanned Facebook photos—even the faces of non-Facebook users—in order to identify people to recommend tagging. Later, Meta paid another $1.4 billion to settle a similar suit in Texas.

Many states aside from Illinois and Texas now protect biometric data. While the state has never enforced its law, Washington in 2017 passed a biometric privacy law. In 2023, the state passed an ever stronger law that protects biometric privacy, which allows individuals to sue on their own behalf. And at least 16 states have recently passed comprehensive privacy laws that often require companies to obtain opt-in consent for the collection of sensitive data, which typically includes biometric data. For example, in Colorado, a company that jointly with others determines the purpose and means of processing biometric data must obtain consent. Maryland goes farther, and such companies are essentially prohibited from collecting or processing biometric data from bystanders.

Many of these comprehensive laws have numerous loopholes and can only be enforced by state regulators—a glaring weakness facilitated in part by Amazon lobbyists.

Nonetheless, Ring’s new feature provides regulators a clear opportunity to step up to investigate, protect people’s privacy, and test the strength of their laws.

With the rapid advancement of generative artificial intelligence, teachers and school leaders are looking for answers to complicated questions about successfully integrating technology into lessons, while also ensuring students actually learn what they’re trying to teach. 

Justin Reich, an associate professor in MIT’s Comparative Media Studies/Writing program, hopes a new guidebook published by the MIT Teaching Systems Lab can support K-12 educators as they determine what AI policies or guidelines to craft.

“Throughout my career, I’ve tried to be a person who researches education and technology and translates findings for people who work in the field,” says Reich. “When tricky things come along I try to jump in and be helpful.” 

“A Guide to AI in Schools: Perspectives for the Perplexed,” published this fall, was developed with the support of an expert advisory panel and other researchers. The project includes input from more than 100 students and teachers from around the United States, sharing their experiences teaching and learning with new generative AI tools. 

“We’re trying to advocate for an ethos of humility as we examine AI in schools,” Reich says. “We’re sharing some examples from educators about how they’re using AI in interesting ways, some of which might prove sturdy and some of which might prove faulty. And we won’t know which is which for a long time.”

Finding answers to AI and education questions

The guidebook attempts to help K-12 educators, students, school leaders, policymakers, and others collect and share information, experiences, and resources. AI’s arrival has left schools scrambling to respond to multiple challenges, like how to ensure academic integrity and maintain data privacy. 

Reich cautions that the guidebook is not meant to be prescriptive or definitive, but something that will help spark thought and discussion. 

“Writing a guidebook on generative AI in schools in 2025 is a little bit like writing a guidebook of aviation in 1905,” the guidebook’s authors note. “No one in 2025 can say how best to manage AI in schools.”

Schools are also struggling to measure how student learning loss looks in the age of AI. “How does bypassing productive thinking with AI look in practice?” Reich asks. “If we think teachers provide content and context to support learning and students no longer perform the exercises housing the content and providing the context, that’s a serious problem.”

Reich invites people directly impacted by AI to help develop solutions to the challenges its ubiquity presents. “It’s like observing a conversation in the teacher’s lounge and inviting students, parents, and other people to participate about how teachers think about AI,” he says, “what they are seeing in their classrooms, and what they’ve tried and how it went.”

The guidebook, in Reich’s view, is ultimately a collection of hypotheses expressed in interviews with teachers: well-informed, initial guesses about the paths that schools could follow in the years ahead. 

Producing educator resources in a podcast

In addition to the guidebook, the Teaching Systems Lab also recently produced “The Homework Machine,” a seven-part series from the Teachlab podcast that explores how AI is reshaping K-12 education. 

Reich produced the podcast in collaboration with journalist Jesse Dukes. Each episode tackles a specific area, asking important questions about challenges related to issues like AI adoption, poetry as a tool for student engagement, post-Covid learning loss, pedagogy, and book bans. The podcast allows Reich to share timely information about education-related updates and collaborate with people interested in helping further the work.

“The academic publishing cycle doesn’t lend itself to helping people with near-term challenges like those AI presents,” Reich says. “Peer review takes a long time, and the research produced isn’t always in a form that’s helpful to educators.” Schools and districts are grappling with AI in real time, bypassing time-tested quality control measures. 

The podcast can help reduce the time it takes to share, test, and evaluate AI-related solutions to new challenges, which could prove useful in creating training and resources.  

“We hope the podcast will spark thought and discussion, allowing people to draw from others’ experiences,” Reich says.

The podcast was also produced into an hour-long radio special, which was broadcast by public radio stations across the country.

“We’re fumbling around in the dark”

Reich is direct in his assessment of where we are with understanding AI and its impacts on education. “We’re fumbling around in the dark,” he says, recalling past attempts to quickly integrate new tech into classrooms. These failures, Reich suggests, highlight the importance of patience and humility as AI research continues. “AI bypassed normal procurement processes in education; it just showed up on kids’ phones,” he notes. 

“We’ve been really wrong about tech in the past,” Reich says. Despite districts’ spending on tools like smartboards, for example, research indicates there’s no evidence that they improve learning or outcomes. In a new article for article for The Conversation, he argues that early teacher guidance in areas like web literacy has produced bad advice that still exists in our educational system. “We taught students and educators not to trust Wikipedia,” he recalls, “and to search for website credibility markers, both of which turned out to be incorrect.” Reich wants to avoid a similar rush to judgment on AI, recommending that we avoid guessing at AI-enabled instructional strategies.

These challenges, coupled with potential and observed student impacts, significantly raise the stakes for schools and students’ families in the AI race. “Education technology always provokes teacher anxiety,” Reich notes, “but the breadth of AI-related concerns is much greater than in other tech-related areas.” 

The dawn of the AI age is different from how we’ve previously received tech into our classrooms, Reich says. AI wasn’t adopted like other tech. It simply arrived. It’s now upending educational models and, in some cases, complicating efforts to improve student outcomes.

Reich is quick to point out that there are no clear, definitive answers on effective AI implementation and use in classrooms; those answers don’t currently exist. Each of the resources Reich helped develop invite engagement from the audiences they target, aggregating valuable responses others might find useful.

“We can develop long-term solutions to schools’ AI challenges, but it will take time and work,” he says. “AI isn’t like learning to tie knots; we don’t know what AI is, or is going to be, yet.” 

Reich also recommends learning more about AI implementation from a variety of sources. “Decentralized pockets of learning can help us test ideas, search for themes, and collect evidence on what works,” he says. “We need to know if learning is actually better with AI.” 

While teachers don’t get to choose regarding AI’s existence, Reich believes it’s important that we solicit their input and involve students and other stakeholders to help develop solutions that improve learning and outcomes. 

“Let’s race to answers that are right, not first,” Reich says.

It’s not news that Apple and Google use their app stores to shape what apps you can and cannot have on many of your devices. What is new is more governments—including the U.S. government—using legal and extralegal tools to lean on these gatekeepers in order to assert that same control. And rather than resisting, the gatekeepers are making it easier than ever. 

Apple’s decision to take down the ICEBlock app at least partially in response to threats from the U.S. government—with Google rapidly and voluntarily following suit—was bad enough. But it pales in comparison with Google’s new program, set to launch worldwide next year, requiring developers to register with the company in order to have their apps installable on Android certified devices—including paying a fee and providing personal information backed by government-issued identification. Google claims the new program of “is an extra layer of security that deters bad actors and makes it harder for them to spread harm,” but the registration requirements are barely tied to app effectiveness or security. Why, one wonders, does Google need to see your driver’s license to evaluate whether your app is safe?  Why, one also wonders, does Google want to create a database of virtually every Android app developer in the world? 

Those communities are likely to drop out of developing for Android altogether, depriving all Android users of valuable tools. 

F-Droid, a free and open-source repository for Android apps, has been sounding the alarm. As they’ve explained in an open letter, Google’s central registration system will be devastating for the Android developer community. Many mobile apps are created, improved, and distributed by volunteers, researchers, and/or small teams with limited financial resources. Others are created by developers who do not use the name attached to any government-issued identification. Others may have good reason to fear handing over their personal information to Google, or any other third party. Those communities are likely to drop out of developing for Android altogether, depriving all Android users of valuable tools. 

Google’s promise that it’s “working on” a program for “students and hobbyists” that may have different requirements falls far short of what is necessary to alleviate these concerns. 

It’s more important than ever to support technologies which decentralize and democratize our shared digital commons. A centralized global registration system for Android will inevitably chill this work. 

The point here is not that all the apps are necessarily perfect or even safe. The point is that when you set up a gate, you invite authorities to use it to block things they don’t like. And when you build a database, you invite governments (and private parties) to try to get access to that database. If you build it, they will come.  

Imagine you have developed a virtual private network (VPN) and corresponding Android mobile app that helps dissidents, journalists, and ordinary humans avoid corporate and government surveillance. In some countries, distributing that app could invite legal threats and even prosecution. Developers in those areas should not have to trust that Google would not hand over their personal information in response to a government demand just because they want their app to be installable by all Android users. By the same token, technologists that work on Android apps for reporting ICE misdeeds should not have to worry that Google will hand over their personal information to, say, the U.S. Department of Homeland Security. 

It’s easy to see how a new registration requirement for developers could give Google a new lever for maintaining its app store monopoly

Our tech infrastructure’s substantial dependence on just a few platforms is already creating new opportunities for those platforms to be weaponized to serve all kinds of disturbing purposes, from policing to censorship. In this context, it’s more important than ever to support technologies which decentralize and democratize our shared digital commons. A centralized global registration system for Android will inevitably chill this work. 

Not coincidentally, the registration system Google announced would also help cement Google’s outsized competitive power, giving the company an additional window—if it needed one, given the company’s already massive surveillance capabilities—into what apps are being developed, by whom, and how they are being distributed. It’s more than ironic that Google’s announcement came at the same time the company is fighting a court order (in the Epic Games v. Google lawsuit) that will require it to stop punishing developers who distribute their apps through app stores that compete with Google’s own. It’s easy to see how a new registration requirement for developers, potentially enforced by technical measures on billions of Android certified mobile devices, could give Google a new lever for maintaining its app store monopoly.  

EFF has signed on to F-Droid’s open letter. If you care about taking back control of tech, you should too. 

A recent study from Oregon State University estimated that more than 3,500 animal species are at risk of extinction because of factors including habitat alterations, natural resources being overexploited, and climate change.

To better understand these changes and protect vulnerable wildlife, conservationists like MIT PhD student and Computer Science and Artificial Intelligence Laboratory (CSAIL) researcher Justin Kay are developing computer vision algorithms that carefully monitor animal populations. A member of the lab of MIT Department of Electrical Engineering and Computer Science assistant professor and CSAIL principal investigator Sara Beery, Kay is currently working on tracking salmon in the Pacific Northwest, where they provide crucial nutrients to predators like birds and bears, while managing the population of prey, like bugs.

With all that wildlife data, though, researchers have lots of information to sort through and many AI models to choose from to analyze it all. Kay and his colleagues at CSAIL and the University of Massachusetts Amherst are developing AI methods that make this data-crunching process much more efficient, including a new approach called “consensus-driven active model selection” (or “CODA”) that helps conservationists choose which AI model to use. Their work was named a Highlight Paper at the International Conference on Computer Vision (ICCV) in October.

That research was supported, in part, by the National Science Foundation, Natural Sciences and Engineering Research Council of Canada, and Abdul Latif Jameel Water and Food Systems Lab (J-WAFS). Here, Kay discusses this project, among other conservation efforts.

Q: In your paper, you pose the question of which AI models will perform the best on a particular dataset. With as many as 1.9 million pre-trained models available in the HuggingFace Models repository alone, how does CODA help us address that challenge?

A: Until recently, using AI for data analysis has typically meant training your own model. This requires significant effort to collect and annotate a representative training dataset, as well as iteratively train and validate models. You also need a certain technical skill set to run and modify AI training code. The way people interact with AI is changing, though — in particular, there are now millions of publicly available pre-trained models that can perform a variety of predictive tasks very well. This potentially enables people to use AI to analyze their data without developing their own model, simply by downloading an existing model with the capabilities they need. But this poses a new challenge: Which model, of the millions available, should they use to analyze their data? 

Typically, answering this model selection question also requires you to spend a lot of time collecting and annotating a large dataset, albeit for testing models rather than training them. This is especially true for real applications where user needs are specific, data distributions are imbalanced and constantly changing, and model performance may be inconsistent across samples. Our goal with CODA was to substantially reduce this effort. We do this by making the data annotation process “active.” Instead of requiring users to bulk-annotate a large test dataset all at once, in active model selection we make the process interactive, guiding users to annotate the most informative data points in their raw data. This is remarkably effective, often requiring users to annotate as few as 25 examples to identify the best model from their set of candidates. 

We’re very excited about CODA offering a new perspective on how to best utilize human effort in the development and deployment of machine-learning (ML) systems. As AI models become more commonplace, our work emphasizes the value of focusing effort on robust evaluation pipelines, rather than solely on training.

Q: You applied the CODA method to classifying wildlife in images. Why did it perform so well, and what role can systems like this have in monitoring ecosystems in the future?

A: One key insight was that when considering a collection of candidate AI models, the consensus of all of their predictions is more informative than any individual model’s predictions. This can be seen as a sort of “wisdom of the crowd:” On average, pooling the votes of all models gives you a decent prior over what the labels of individual data points in your raw dataset should be. Our approach with CODA is based on estimating a “confusion matrix” for each AI model — given the true label for some data point is class X, what is the probability that an individual model predicts class X, Y, or Z? This creates informative dependencies between all of the candidate models, the categories you want to label, and the unlabeled points in your dataset.

Consider an example application where you are a wildlife ecologist who has just collected a dataset containing potentially hundreds of thousands of images from cameras deployed in the wild. You want to know what species are in these images, a time-consuming task that computer vision classifiers can help automate. You are trying to decide which species classification model to run on your data. If you have labeled 50 images of tigers so far, and some model has performed well on those 50 images, you can be pretty confident it will perform well on the remainder of the (currently unlabeled) images of tigers in your raw dataset as well. You also know that when that model predicts some image contains a tiger, it is likely to be correct, and therefore that any model that predicts a different label for that image is more likely to be wrong. You can use all these interdependencies to construct probabilistic estimates of each model’s confusion matrix, as well as a probability distribution over which model has the highest accuracy on the overall dataset. These design choices allow us to make more informed choices over which data points to label and ultimately are the reason why CODA performs model selection much more efficiently than past work.

There are also a lot of exciting possibilities for building on top of our work. We think there may be even better ways of constructing informative priors for model selection based on domain expertise — for instance, if it is already known that one model performs exceptionally well on some subset of classes or poorly on others. There are also opportunities to extend the framework to support more complex machine-learning tasks and more sophisticated probabilistic models of performance. We hope our work can provide inspiration and a starting point for other researchers to keep pushing the state of the art.

Q: You work in the Beerylab, led by Sara Beery, where researchers are combining the pattern-recognition capabilities of machine-learning algorithms with computer vision technology to monitor wildlife. What are some other ways your team is tracking and analyzing the natural world, beyond CODA?

A: The lab is a really exciting place to work, and new projects are emerging all the time. We have ongoing projects monitoring coral reefs with drones, re-identifying individual elephants over time, and fusing multi-modal Earth observation data from satellites and in-situ cameras, just to name a few. Broadly, we look at emerging technologies for biodiversity monitoring and try to understand where the data analysis bottlenecks are, and develop new computer vision and machine-learning approaches that address those problems in a widely applicable way. It’s an exciting way of approaching problems that sort of targets the “meta-questions” underlying particular data challenges we face. 

The computer vision algorithms I’ve worked on that count migrating salmon in underwater sonar video are examples of that work. We often deal with shifting data distributions, even as we try to construct the most diverse training datasets we can. We always encounter something new when we deploy a new camera, and this tends to degrade the performance of computer vision algorithms. This is one instance of a general problem in machine learning called domain adaptation, but when we tried to apply existing domain adaptation algorithms to our fisheries data we realized there were serious limitations in how existing algorithms were trained and evaluated. We were able to develop a new domain adaptation framework, published earlier this year in Transactions on Machine Learning Research, that addressed these limitations and led to advancements in fish counting, and even self-driving and spacecraft analysis.

One line of work that I’m particularly excited about is understanding how to better develop and analyze the performance of predictive ML algorithms in the context of what they are actually used for. Usually, the outputs from some computer vision algorithm — say, bounding boxes around animals in images — are not actually the thing that people care about, but rather a means to an end to answer a larger problem — say, what species live here, and how is that changing over time? We have been working on methods to analyze predictive performance in this context and reconsider the ways that we input human expertise into ML systems with this in mind. CODA was one example of this, where we showed that we could actually consider the ML models themselves as fixed and build a statistical framework to understand their performance very efficiently. We have been working recently on similar integrated analyses combining ML predictions with multi-stage prediction pipelines, as well as ecological statistical models. 

The natural world is changing at unprecedented rates and scales, and being able to quickly move from scientific hypotheses or management questions to data-driven answers is more important than ever for protecting ecosystems and the communities that depend on them. Advancements in AI can play an important role, but we need to think critically about the ways that we design, train, and evaluate algorithms in the context of these very real challenges.

By stimulating cancer cells to produce a molecule that activates a signaling pathway in nearby immune cells, MIT researchers have found a way to force tumors to trigger their own destruction.

Activating this signaling pathway, known as the cGAS-STING pathway, worked even better when combined with existing immunotherapy drugs known as checkpoint blockade inhibitors, in a study of mice. That dual treatment was successfully able to control tumor growth.

The researchers turned on the cGAS-STING pathway in immune cells using messenger RNA delivered to cancer cells. This approach may avoid the side effects of delivering large doses of a STING activator, and takes advantage of a natural process in the body. This could make it easier to develop a treatment for use in patients, the researchers say.

“Our approach harnesses the tumor’s own machinery to produce immune-stimulating molecules, creating a powerful antitumor response,” says Natalie Artzi, a principal research scientist at MIT’s Institute for Medical Engineering and Science, an associate professor of medicine at Harvard Medical School, a core faculty member at the Wyss Institute for Biologically Inspired Engineering at Harvard, and the senior author of the study.

“By increasing cGAS levels inside cancer cells, we can enhance delivery efficiency — compared to targeting the more scarce immune cells in the tumor microenvironment — and stimulate the natural production of cGAMP, which then activates immune cells locally,” she says. “This strategy not only strengthens antitumor immunity but also reduces the toxicity associated with direct STING agonist delivery, bringing us closer to safer and more effective cancer immunotherapies.”

Alexander Cryer, a visiting scholar at IMES, is the lead author of the paper, which appears this week in the Proceedings of the National Academy of Sciences.

Immune activation

STING (short for stimulator of interferon genes) is a protein that helps to trigger immune responses. When STING is activated, it turns on a pathway that initiates production of type one interferons, which are cytokines that stimulate immune cells.

Many research groups, including Artzi’s, have explored the possibility of artificially stimulating this pathway with molecules called STING agonists, which could help immune cells to recognize and attack tumor cells. This approach has worked well in animal models, but it has had limited success in clinical trials, in part because the required doses can cause harmful side effects.

While working on a project exploring new ways to deliver STING agonists, Cryer became intrigued when he learned from previous work that cancer cells can produce a STING activator known as cGAMP. The cells then secrete cGAMP, which can activate nearby immune cells.

“Part of my philosophy of science is that I really enjoy using endogenous processes that the body already has, and trying to utilize them in a slightly different context. Evolution has done all the hard work. We just need to figure out how push it in a different direction,” Cryer says. “Once I saw that cancer cells produce this molecule, I thought: Maybe there’s a way to take this process and supercharge it.”

Within cells, the production of cGAMP is catalyzed by an enzyme called cGAS. To get tumor cells to activate STING in immune cells, the researchers devised a way to deliver messenger RNA that encodes cGAS. When this enzyme detects double-stranded DNA in the cell body, which can be a sign of either infection or cancer-induced damage, it begins producing cGAMP.

“It just so happens that cancer cells, because they’re dividing so fast and not particularly accurately, tend to have more double-stranded DNA fragments than healthy cells,” Cryer says.

The tumor cells then release cGAMP into tumor microenvironment, where it can be taken up by neighboring immune cells and activate their STING pathway.

Targeting tumors

Using a mouse model of melanoma, the researchers evaluated their new strategy’s potential to kill cancer cells. They injected mRNA encoding cGAS, encapsulated in lipid nanoparticles, into tumors. One group of mice received this treatment alone, while another received a checkpoint blockade inhibitor, and a third received both treatments.

Given on their own, cGAS and the checkpoint inhibitor each significantly slowed tumor growth. However, the best results were seen in the mice that received both treatments. In that group, tumors were completely eradicated in 30 percent of the mice, while none of the tumors were fully eliminated in the groups that received just one treatment.

An analysis of the immune response showed that the mRNA treatment stimulated production of interferon as well as many other immune signaling molecules. A variety of immune cells, including macrophages and dendritic cells, were activated. These cells help to stimulate T cells, which can then destroy cancer cells.

The researchers were able to elicit these responses with just a small dose of cancer-cell-produced cGAMP, which could help to overcome one of the potential obstacles to using cGAMP on its own as therapy: Large doses are required to stimulate an immune response, and these doses can lead to widespread inflammation, tissue damage, and autoimmune reactions. When injected on its own, cGAMP tends to spread through the body and is rapidly cleared from the tumor, while in this study, the mRNA nanoparticles and cGAMP remained at the tumor site.

“The side effects of this class of molecule can be pretty severe, and one of the potential advantages of our approach is that you’re able to potentially subvert some toxicity that you might see if you’re giving the free molecules,” Cryer says.

The researchers now hope to work on adapting the delivery system so that it could be given as a systemic injection, rather than injecting it into the tumor. They also plan to test the mRNA therapy in combination with chemotherapy drugs or radiotherapy that damage DNA, which could make the therapy even more effective because there could be even more double-stranded DNA available to help activate the synthesis of cGAMP.

When the independent Tunisian online media collective Nawaat announced that the government had suspended its activities for one month, the news landed like a punch in the gut for anyone who remembers what the Arab uprisings promised: dignity, democracy, and a free press.

But Tunisia’s October 31 suspension of Nawaat—delivered quietly, without formal notice, and justified under Decree-Law 2011-88—is not just a bureaucratic decision. It’s a warning shot aimed at the very idea of independent civic life.

The silencing of a revolutionary media outlet

Nawaat’s statement, published last week, recounts how the group discovered the suspension: not through any official communication, but by finding the order slipped under its office door. The move came despite Nawaat’s documented compliance with all the legal requirements under Decree 88, the 2011 law that once symbolized post-revolutionary openness for associations.

Instead, the Decree, once seen as a safeguard for civic freedom, is now being weaponized as a tool of control. Nawaat’s team describes the action as part of a broader campaign of harassment: tax audits, financial investigations, and administrative interrogations that together amount to an attempt to “stifle all media resistance to the dictatorship.”

For those who have followed Tunisia’s post-2019 trajectory, the move feels chillingly familiar. Since President Kais Saied consolidated power in 2021, civil society organizations, journalists, and independent voices have faced escalating repression. Amnesty International has documented arrests of reporters, the use of counter-terrorism laws against critics, and the closure of NGOs. And now, the government has found in Decree 88 a convenient veneer of legality to achieve what old regimes did by force.

Adopted in the hopeful aftermath of the revolution, Decree-Law 2011-88 was designed to protect the right to association. It allowed citizens to form organizations without prior approval and receive funding freely—a radical departure from the Ben Ali era’s suffocating controls.

But laws are only as democratic as the institutions that enforce them. Over the years, Tunisian authorities have chipped away at these protections. Administrative notifications, once procedural, have become tools for sanction. Financial transparency requirements have turned into pretexts for selective punishment.

When a government can suspend an association that has complied with every rule, the rule of law itself becomes a performance.

Bureaucratic authoritarianism

What’s happening in Tunisia is not an isolated episode. Across the region, governments have refined the art of silencing dissent without firing a shot. But whether through Egypt’s NGO Law, Morocco’s press code, or Algeria’s foreign-funding restrictions, the outcome is the same: fewer independent outlets, and fewer critical voices.

These are the tools of bureaucratic authoritarianism…the punishment is quiet, plausible, and difficult to contest. A one-month suspension might sound minor, but for a small newsroom like Nawaat—which operates with limited funding and constant political pressure—it can mean disrupted investigations, delayed publications, and lost trust from readers and sources alike.

A decade of resistance

To understand why Nawaat matters, remember where it began. Founded in 2004 under Zine El Abidine Ben Ali’s dictatorship, Nawaat became a rare space for citizen journalism and digital dissent. During the 2011 uprising, its reporting and documentation helped the world witness Tunisia’s revolution.

Over the past two decades, Nawaat has earned international recognition, including an EFF Pioneer Award in 2011, for its commitment to free expression and technological empowerment. It’s not just a media outlet; it’s a living archive of Tunisia’s struggle for dignity and rights.

That legacy is precisely what makes it threatening to the current regime. Nawaat represents a continuity of civic resistance that authoritarianism cannot easily erase.

The cost of silence

Administrative suspensions like this one are designed to send a message: You can be shut down at any time. They impose psychological costs that are harder to quantify than arrests or raids. Journalists start to self-censor. Donors hesitate to renew grants. The public, fatigued by uncertainty, tunes out.

But the real tragedy lies in what this means for Tunisians’ right to know. Nawaat’s reporting on corruption, surveillance, and state violence fills the gaps left by state-aligned media. Silencing it deprives citizens of access to truth and accountability.

As Nawaat’s statement puts it:

“This arbitrary decision aims to silence free voices and stifle all media resistance to the dictatorship.”

The government’s ability to pause a media outlet, even temporarily, sets a precedent that could be replicated across Tunisia’s civic sphere. If Nawaat can be silenced today, so can any association tomorrow.

So what can be done? Nawaat has pledged to challenge the suspension in court, but litigation alone won’t fix a system where independence is eroding from within. What’s needed is sustained, visible, and international solidarity.

Tunisia’s government may succeed in pausing Nawaat’s operations for a month. But it cannot erase the two decades of documentation, dissent, and hope the outlet represents. Nor can it silence the networks of journalists, technologists, and readers who know what is at stake.

EFF has long argued that the right to free expression is inseparable from the right to digital freedom. Nawaat’s suspension shows how easily administrative and legal tools can become weapons against both. When states combine surveillance, regulatory control, and economic pressure, they don’t need to block websites or jail reporters outright—they simply tighten the screws until free expression becomes impossible.

That’s why what happens in Tunisia matters far beyond its borders. It’s a test of whether the ideals of 2011 still mean anything in 2025.

And Nawaat, for its part, has made its position clear:

“We will continue to defend our independence and our principles. We will not be silenced.”

By Gigi Sohn, Chair, EFF Board of Directors 

With the impending departure of longtime, renowned, and beloved Executive Director Cindy Cohn, EFF and leadership advisory firm Russell Reynolds Associates have developed a profile for her successor.  While Cindy is irreplaceable, we hope that everyone who knows and loves EFF will help us find our next leader.  

First and foremost, we are looking for someone who’ll meet this pivotal moment in EFF’s history. As authoritarian surveillance creeps around the globe and society grapples with debates over AI and other tech, EFF needs a forward-looking, strategic, and collaborative executive director to bring fresh eyes and new ideas while building on our past successes.  

The San Francisco-based executive director, who reports to our board of directors, will have responsibility over all administrative, financial, development and programmatic activities at EFF.  They will lead a dedicated team of legal, technical, and advocacy professionals, steward EFF’s strong organizational culture, and ensure long-term organizational sustainability and impact. That means being: 

• Our visionary — partnering with the board and staff to define and advance a courageous, forward-looking strategic vision for EFF; leading development, prioritization, and execution of a comprehensive strategic plan that balances proactive agenda-setting with responsive action; and ensuring clarity of mission and purpose, aligning organizational priorities and resources for maximum impact. 

• Our face and voice — serving as a compelling, credible public voice and thought leader for EFF’s mission and work, amplifying the expertise of staff and engaging diverse audiences including media, policymakers, and the broader public, while also building and nurturing partnerships and coalitions across the technology, legal, advocacy, and philanthropic sectors. 

• Our chief money manager — stewarding relationships with individual donors, foundations, and key supporters; developing and implementing strategies to diversify and grow EFF’s revenue streams, including membership, grassroots, institutional, and major gifts; and ensuring financial discipline, transparency, and sustainability in partnership with the board and executive team. 

• Our fearless leader — fostering a positive, inclusive, high-performing, and accountable culture that honors EFF’s activist DNA while supporting professional growth, partnering with unionized staff, and maintaining a collaborative, constructive relationship with the staff union. 

It’ll take a special someone to lead us with courage, vision, personal integrity, and deep understanding of EFF’s unique role at the intersection of law and technology. For more details — including the compensation range and how to apply — click here for the full position specification. And if you know someone who you believe fits the bill, all nominations (strictly confidential, of course) are welcome at eff@russellreynolds.com.  

More than 80 law enforcement agencies across the United States have used language perpetuating harmful stereotypes against Romani people when searching the nationwide Flock Safety automated license plate reader (ALPR) network, according to audit logs obtained and analyzed by the Electronic Frontier Foundation. 

When police run a search through the Flock Safety network, which links thousands of ALPR systems, they are prompted to leave a reason and/or case number for the search. Between June 2024 and October 2025, cops performed hundreds of searches for license plates using terms such as "roma" and "g*psy," and in many instances, without any mention of a suspected crime. Other uses include "g*psy vehicle," "g*psy group," "possible g*psy," "roma traveler" and "g*psy ruse," perpetuating systemic harm by demeaning individuals based on their race or ethnicity. 

These queries were run through thousands of police departments' systems—and it appears that none of these agencies flagged the searches as inappropriate. 

These searches are, by definition, racist. 

Word Choices and Flock Searches 

We are using the terms "Roma" and “Romani people” as umbrella terms, recognizing that they represent different but related groups. Since 2020, the U.S. federal government has officially recognized "Anti-Roma Racism" as including behaviors such as "stereotyping Roma as persons who engage in criminal behavior" and using the slur "g*psy." According to the U.S. Department of State, this language “leads to the treatment of Roma as an alleged alien group and associates them with a series of pejorative stereotypes and distorted images that represent a specific form of racism.” 

Nevertheless, police officers have run hundreds of searches for license plates using the terms "roma" and "g*psy." (Unlike the police ALPR queries we’ve uncovered, we substitute an asterisk for the Y to avoid repeating this racist slur). In many cases, these terms have been used on their own, with no mention of crime. In other cases, the terms have been used in contexts like "g*psy scam" and "roma burglary," when ethnicity should have no relevance to how a crime is investigated or prosecuted. 

A “g*psy scam” and “roma burglary” do not exist in criminal law separate from any other type of fraud or burglary. Several agencies contacted by EFF have since acknowledged the inappropriate use and expressed efforts to address the issue internally. 

"The use of the term does not reflect the values or expected practices of our department," a representative of the Palos Heights (IL) Police Department wrote to EFF after being confronted with two dozen searches involving the term "g*psy." "We do not condone the use of outdated or offensive terminology, and we will take this inquiry as an opportunity to educate those who are unaware of the negative connotation and to ensure that investigative notations and search reasons are documented in a manner that is accurate, professional, and free of potentially harmful language."

Of course, the broader issue is that allowing "g*psy" or "Roma" as a reason for a search isn't just offensive, it implies the criminalization an ethnic group. In fact, the Grand Prairie Police Department in Texas searched for "g*psy" six times while using Flock's "Convoy" feature, which allows an agency to identify vehicles traveling together—in essence targeting an entire traveling community of Roma without specifying a crime. 

At the bottom of this post is a list of agencies and the terms they used when searching the Flock system. 

Anti-Roma Racism in an Age of Surveillance 

Racism against Romani people has been a problem for centuries, with one of its most horrific manifestations  during the Holocaust, when the Third Reich and its allies perpetuated genocide by murdering hundreds of thousands of Romani people and sterilizing thousands more. Despite efforts by the UN and EU to combat anti-Roma discrimination, this form of racism persists. As scholars Margareta Matache and Mary T. Bassett explain, it is perpetuated by modern American policing practices: 

In recent years, police departments have set up task forces specialised in “G*psy crimes”, appointed “G*psy crime” detectives, and organised police training courses on “G*psy criminality”. The National Association of Bunco Investigators (NABI), an organisation of law enforcement professionals focusing on “non-traditional organised crime”, has even created a database of individuals arrested or suspected of criminal activity, which clearly marked those who were Roma.

Thus, it is no surprise that a 2020 Harvard University survey of Romani Americans found that 4 out of 10 respondents reported being subjected to racial profiling by police. This demonstrates the ongoing challenges they face due to systemic racism and biased policing. 

Notably, many police agencies using surveillance technologies like ALPRs have adopted some sort of basic policy against biased policing or the use of these systems to target people based on race or ethnicity. But even when such policies are in place, an agency’s failure to enforce them allows these discriminatory practices to persist. These searches were also run through the systems of thousands of other police departments that may have their own policies and state laws that prohibit bias-based policing—yet none of those agencies appeared to have flagged the searches as inappropriate. 

The Flock search data in question here shows that surveillance technology exacerbates racism, and even well-meaning policies to address bias can quickly fall apart without proper oversight and accountability. 

Cops In Their Own Words

EFF reached out to a sample of the police departments that ran these searches. Here are five representative responses we received from police departments in Illinois, California, and Virginia. They do not inspire confidence.

1. Lake County Sheriff's Office, IL 

In June 2025, the Lake County Sheriff's Office ran three searches for a dark colored pick-up truck, using the reason: "G*PSY Scam." The search covered 1,233 networks, representing 14,467 different ALPR devices. 

In response to EFF, a sheriff's representative wrote via email:

“Thank you for reaching out and for bringing this to our attention.  We certainly understand your concern regarding the use of that terminology, which we do not condone or support, and we want to assure you that we are looking into the matter.

Any sort of discriminatory practice is strictly prohibited at our organization. If you have the time to take a look at our commitment to the community and our strong relationship with the community, I firmly believe you will see discrimination is not tolerated and is quite frankly repudiated by those serving in our organization. 

We appreciate you bringing this to our attention so we can look further into this and address it.”

2. Sacramento Police Department, CA

In May 2025, the Sacramento Police Department ran six searches using the term "g*psy."  The search covered 468 networks, representing 12,885 different ALPR devices. 

In response to EFF, a police representative wrote:

“Thank you again for reaching out. We looked into the searches you mentioned and were able to confirm the entries. We’ve since reminded the team to be mindful about how they document investigative reasons. The entry reflected an investigative lead, not a disparaging reference. 

We appreciate the chance to clarify.”

3. Palos Heights Police Department, IL

In September 2024, the Palos Heights Police Department ran more than two dozen searches using terms such as "g*psy vehicle," "g*psy scam" and "g*psy concrete vehicle." Most searches hit roughly 1,000 networks. 

In response to EFF, a police representative said the searches were related to a singular criminal investigation into a vehicle involved in a "suspicious circumstance/fraudulent contracting incident" and is "not indicative of a general search based on racial or ethnic profiling." However, the agency acknowledged the language was inappropriate: 

“The use of the term does not reflect the values or expected practices of our department. We do not condone the use of outdated or offensive terminology, and we will take this inquiry as an opportunity to educate those who are unaware of the negative connotation and to ensure that investigative notations and search reasons are documented in a manner that is accurate, professional, and free of potentially harmful language.

We appreciate your outreach on this matter and the opportunity to provide clarification.”

4. Irvine Police Department, CA

In February and May 2025, the Irvine Police Department ran eight searches using the term "roma" in the reason field. The searches covered 1,420 networks, representing 29,364 different ALPR devices. 

In a call with EFF, an IPD representative explained that the cases were related to a series of organized thefts. However, they acknowledged the issue, saying, "I think it's an opportunity for our agency to look at those entries and to use a case number or use a different term." 

5. Fairfax County Police Department, VA

Between December 2024 and April 2025, the Fairfax County Police Department ran more than 150 searches involving terms such as "g*psy case" and "roma crew burglaries." Fairfax County PD continued to defend its use of this language.

In response to EFF, a police representative wrote:

“Thank you for your inquiry. When conducting searches in investigative databases, our detectives must use the exact case identifiers, terms, or names connected to a criminal investigation in order to properly retrieve information. These entries reflect terminology already tied to specific cases and investigative files from other agencies, not a bias or judgment about any group of people. The use of such identifiers does not reflect bias or discrimination and is not inconsistent with our Bias-Based Policing policy within our Human Relations General Order.”

A National Trend

Roma individuals and families are not the only ones being systematically and discriminatorily targeted by ALPR surveillance technologies. For example, Flock audit logs show agencies ran 400 more searches using terms targeting Traveller communities more generally, with a specific focus on Irish Travellers, often without any mention of a crime. 

Across the country, these tools are enabling and amplifying racial profiling by embedding longstanding policing biases into surveillance technologies. For example, data from Oak Park, IL, show that 84% of drivers stopped in Flock-related traffic incidents were Black—despite Black people making up only 19% of the local population. ALPR systems are far from being neutral tools for public safety and are increasingly being used to fuel discriminatory policing practices against historically marginalized people. 

The racially coded language in Flock's logs mirrors long-standing patterns of discriminatory policing. Terms like "furtive movements," "suspicious behavior," and "high crime area" have always been cited by police to try to justify stops and searches of Black, Latine, and Native communities. These phrases might not appear in official logs because they're embedded earlier in enforcement—in the traffic stop without clear cause, the undocumented stop-and-frisk, the intelligence bulletin flagging entire neighborhoods as suspect. They function invisibly until a body-worn camera, court filing, or audit brings them to light. Flock's network didn’t create racial profiling; it industrialized it, turning deeply encoded and vague language into scalable surveillance that can search thousands of cameras across state lines. 

The Path Forward

U.S. Sen. Ron Wyden, D-OR, recently recommended that local governments reevaluate their decisions to install Flock Safety in their communities. We agree, but we also understand that sometimes elected officials need to see the abuse with their own eyes first. 

We know which agencies ran these racist searches, and they should be held accountable. But we also know that the vast majority of Flock Safety's clients—thousands of police and sheriffs—also allowed those racist searches to run through their Flock Safety systems unchallenged. 

Elected officials must act decisively to address the racist policing enabled by Flock's infrastructure. First, they should demand a complete audit of all ALPR searches conducted in their jurisdiction and a review of search logs to determine (a) whether their police agencies participated in discriminatory policing and (b) what safeguards, if any, exist to prevent such abuse. Second, officials should institute immediate restrictions on data-sharing through Flock's nationwide network. As demonstrated by California law, for example, police agencies should not be able to share their ALPR data with federal authorities or out-of-state agencies, thus eliminating a vehicle for discriminatory searches spreading across state lines.

Ultimately, elected officials must terminate Flock Safety contracts entirely. The evidence is now clear: audit logs and internal policies alone cannot prevent a surveillance system from becoming a tool for racist policing. The fundamental architecture of Flock—thousands of cameras feeding into a nationwide searchable network—makes discrimination inevitable when enforcement mechanisms fail.

As Sen. Wyden astutely explained, "local elected officials can best protect their constituents from the inevitable abuses of Flock cameras by removing Flock from their communities.”

Table Overview and Notes

The following table compiles terms used by agencies to describe the reasons for searching the Flock Safety ALPR database. In a small number of cases, we removed additional information such as case numbers, specific incident details, and officers' names that were present in the reason field. 

We removed one agency from the list due to the agency indicating that the word was a person's name and not a reference to Romani people. 

In general, we did not include searches that used the term "Romanian," although many of those may also be indicative of anti-Roma bias. We also did not include uses of "traveler" or “Traveller” when it did not include a clear ethnic modifier; however, we believe many of those searches are likely relevant.  

A text-based version of the spreadsheet is available here. 

These days, the most important meeting attendee isn’t a person: It’s the AI notetaker.

This system assigns action items and determines the importance of what is said. If it becomes necessary to revisit the facts of the meeting, its summary is treated as impartial evidence.

But clever meeting attendees can manipulate this system’s record by speaking more to what the underlying AI weights for summarization and importance than to their colleagues. As a result, you can expect some meeting attendees to use language more likely to be captured in summaries, timing their interventions strategically, repeating key points, and employing formulaic phrasing that AI models are more likely to pick up on. Welcome to the world of AI summarization optimization (AISO)...

Rising utility bills have catapulted energy into the top tier of campaign issues.

Executives with the state's largest utility, Dominion, were asked whether a win by Abigail Spanberger could prompt President Donald Trump to retaliate against the large offshore wind project.

The tech tycoon's new online encyclopedia recognizes the root cause of global warming — humanity's burning of fossil fuels — but provides a platform for contrarian views.

The Rhodium Group found that international climate targets for this century are way off target.

The Illinois Legislature defeated a proposal aimed a minimizing property insurance premiums by strengthening state oversight of rates.

The letter targets the EU’s efforts to move ahead with the Corporate Sustainability Due Diligence Directive.

The plans show that Europe still sees itself as a champion of climate ambition.

The initiative will make possible relay-style flight missions to observe a storm that spans the countries' zones, according to an official.

The forecast is dire, with little sign of water supplies recovering in the months ahead, according to an official.

Managing a power grid is like trying to solve an enormous puzzle.

Grid operators must ensure the proper amount of power is flowing to the right areas at the exact time when it is needed, and they must do this in a way that minimizes costs without overloading physical infrastructure. Even more, they must solve this complicated problem repeatedly, as rapidly as possible, to meet constantly changing demand.

To help crack this consistent conundrum, MIT researchers developed a problem-solving tool that finds the optimal solution much faster than traditional approaches while ensuring the solution doesn’t violate any of the system’s constraints. In a power grid, constraints could be things like generator and line capacity.

This new tool incorporates a feasibility-seeking step into a powerful machine-learning model trained to solve the problem. The feasibility-seeking step uses the model’s prediction as a starting point, iteratively refining the solution until it finds the best achievable answer.

The MIT system can unravel complex problems several times faster than traditional solvers, while providing strong guarantees of success. For some extremely complex problems, it could find better solutions than tried-and-true tools. The technique also outperformed pure machine learning approaches, which are fast but can’t always find feasible solutions.

In addition to helping schedule power production in an electric grid, this new tool could be applied to many types of complicated problems, such as designing new products, managing investment portfolios, or planning production to meet consumer demand.

“Solving these especially thorny problems well requires us to combine tools from machine learning, optimization, and electrical engineering to develop methods that hit the right tradeoffs in terms of providing value to the domain, while also meeting its requirements. You have to look at the needs of the application and design methods in a way that actually fulfills those needs,” says Priya Donti, the Silverman Family Career Development Professor in the Department of Electrical Engineering and Computer Science (EECS) and a principal investigator at the Laboratory for Information and Decision Systems (LIDS).

Donti, senior author of an open-access paper on this new tool, called FSNet, is joined by lead author Hoang Nguyen, an EECS graduate student. The paper will be presented at the Conference on Neural Information Processing Systems.

Combining approaches

Ensuring optimal power flow in an electric grid is an extremely hard problem that is becoming more difficult for operators to solve quickly.

“As we try to integrate more renewables into the grid, operators must deal with the fact that the amount of power generation is going to vary moment to moment. At the same time, there are many more distributed devices to coordinate,” Donti explains.

Grid operators often rely on traditional solvers, which provide mathematical guarantees that the optimal solution doesn’t violate any problem constraints. But these tools can take hours or even days to arrive at that solution if the problem is especially convoluted.

On the other hand, deep-learning models can solve even very hard problems in a fraction of the time, but the solution might ignore some important constraints. For a power grid operator, this could result in issues like unsafe voltage levels or even grid outages.

“Machine-learning models struggle to satisfy all the constraints due to the many errors that occur during the training process,” Nguyen explains.

For FSNet, the researchers combined the best of both approaches into a two-step problem-solving framework.

Focusing on feasibility

In the first step, a neural network predicts a solution to the optimization problem. Very loosely inspired by neurons in the human brain, neural networks are deep learning models that excel at recognizing patterns in data.

Next, a traditional solver that has been incorporated into FSNet performs a feasibility-seeking step. This optimization algorithm iteratively refines the initial prediction while ensuring the solution does not violate any constraints.

Because the feasibility-seeking step is based on a mathematical model of the problem, it can guarantee the solution is deployable.

“This step is very important. In FSNet, we can have the rigorous guarantees that we need in practice,” Hoang says.

The researchers designed FSNet to address both main types of constraints (equality and inequality) at the same time. This makes it easier to use than other approaches that may require customizing the neural network or solving for each type of constraint separately.

“Here, you can just plug and play with different optimization solvers,” Donti says.

By thinking differently about how the neural network solves complex optimization problems, the researchers were able to unlock a new technique that works better, she adds.

They compared FSNet to traditional solvers and pure machine-learning approaches on a range of challenging problems, including power grid optimization. Their system cut solving times by orders of magnitude compared to the baseline approaches, while respecting all problem constraints.

FSNet also found better solutions to some of the trickiest problems.

“While this was surprising to us, it does make sense. Our neural network can figure out by itself some additional structure in the data that the original optimization solver was not designed to exploit,” Donti explains.

In the future, the researchers want to make FSNet less memory-intensive, incorporate more efficient optimization algorithms, and scale it up to tackle more realistic problems.

“Finding solutions to challenging optimization problems that are feasible is paramount to finding ones that are close to optimal. Especially for physical systems like power grids, close to optimal means nothing without feasibility. This work provides an important step toward ensuring that deep-learning models can produce predictions that satisfy constraints, with explicit guarantees on constraint enforcement,” says Kyri Baker, an associate professor at the University of Colorado Boulder, who was not involved with this work.

"A persistent challenge for machine learning-based optimization is feasibility. This work elegantly couples end-to-end learning with an unrolled feasibility-seeking procedure that minimizes equality and inequality violations. The results are very promising and I look forward to see where this research will head," adds Ferdinando Fioretto, an assistant professor at the University of Virginia, who was not involved with this work.