School districts used EPA funds to buy Lion buses. Now the company won't honor warranties.

Oil companies, utilities and others said Wednesday that California legislators must reauthorize cap and trade before adjourning Sept. 12.

The bipartisan group said it wanted to preserve free emissions permits for industry to manage costs.

A wall of reinforced sandbags held back the worst of the glacial flooding in Juneau. The effort to find a permanent solution is complicated.

Mustafa Babiker was proposed for the key role last month.

The Lombardy Glaciological Service will use drone imagery and remote sensing to keep track of the glacier's shrinkage.

While working to boost economic development in Detroit in the late 2010s, Nick Allen found he was running up against a problem.

The city was trying to spur more investment after long-term industrial flight to suburbs and other states. Relying more heavily on property taxes for revenue, the city was negotiating individualized tax deals with prospective businesses. That’s hardly a scenario unique to Detroit, but such deals involved lengthy approval processes that slowed investment decisions and made smaller projects seem unrealistic. 

Moreover, while creating small pockets of growth, these individualized tax abatements were not changing the city’s broader fiscal structure. They also favored those with leverage and resources to work the system for a break.

“The thing you really don’t want to do with taxes is have very particular, highly procedural ways of adjusting the burdens,” says Allen, now a doctoral student in MIT’s Department of Urban Studies and Planning (DUSP). “You want a simple process that fits people’s ideas about what fairness looks like.”

So, after starting his PhD program at MIT, Allen kept studying urban fiscal policy. Along with a group of other scholars, he has produced research papers making the case for a land-value tax — a common tax rate on land that, combined with reduced property taxes, could raise more local revenue by encouraging more city-wide investment, even while lowering tax burdens on residents and businesses. As a bonus, it could also reduce foreclosures.

In the last few years, this has become a larger topic in urban policy circles. The mayor of Detroit has endorsed the idea. The New York Times has written about the work of Allen and his colleagues. The land-value tax is now a serious policy option.

It is unusual for a graduate student to have their work become part of a prominent policy debate. But then, Allen is an unusual student. At MIT, he has not just conducted influential research in his field, but thrown himself into campus-based work with substantial impact as well. Allen has served on task forces assessing student stipend policy, expanding campus housing, and generating ideas for dining program reform.

For all these efforts, in May, Allen received the Karl Taylor Compton Prize, MIT’s highest student honor. At the ceremony, MIT Chancellor Melissa Nobles observed that Allen’s work helped Institute stakeholders “fully understand complex issues, ensuring his recommendations are not only well-informed but also practical and impactful.”

Looking to revive growth

Allen is a Minnesota native who received his BA from Yale University. In 2015, he enrolled in graduate school at MIT, receiving his master’s in city planning from DUSP in 2017. At the time, Allen worked on the Malaysia Sustainable Cities Project, headed by Professor Lawrence Susskind. At one point Allen spent a couple of months in a small Malaysian village studying the effects of coastal development on local fishing and farming.

Malaysia may be different than Michigan, but the issues that Allen encountered in Asia were similar to the ones he wanted to keep studying back in the U.S.: finding ways to finance growth.

“The core interests I have are around real estate, the physical environment, and these fiscal policy questions of how this all gets funded and what the responsibilities are of the state and private markets,” Allen says. “And that brought me to Detroit.”

Specifically, that landed him at the Detroit Economic Growth Corporation, a city-charted development agency that works to facilitate new investment. There, Allen started grappling with the city’s revenue problems. Once heralded as the richest city in America, Detroit has seen a lot of property go vacant, and has hiked property taxes on existing structures to compensate for that. Those rates then discouraged further investment and building.

To be sure, the challenges Detroit has faced stem from far more than tax policy and relate to many macroscale socioeconomic factors, including suburban flight, the shift of manufacturing to states with nonunion employees, and much more. But changing tax policy can be one lever to pull in response.

“It’s difficult to figure out how to revive growth in a place that’s been cannibalized by its losses,” Allen says.

Tasked with underwriting real estate projects, Allen started cataloguing the problems arising from Detroit’s property tax reliance, and began looking at past economics work on optimal tax policy in search of alternatives.

“There’s a real nose-to-the-ground empiricism you start with, asking why we have a system nobody would choose,” Allen says. “There were two parts to that, for me. One was initially looking at the difficulty of making individual projects work, from affordable housing to big industrial plants, along with, secondly, this wave of tax foreclosures in the city.”

Engineering, but for policy

After two years in Detroit, Allen returned to MIT, this time as a doctoral student in DUSP and with a research program oriented around the issues he had worked on. In pursuing that, Allen has worked closely with John E. Anderson, an economist at the University of Nebraska at Lincoln. With a nationwide team of economists convened by the Lincoln Institute of Land Policy, they worked to address the city’s questions on property tax reform.

One paper used current data to show that a land-value tax should lower tax-connected foreclosures in the city. Two other papers study the use of the tax in certain parts of Pennsylvania, one of the few states where it has been deployed. There, the researchers concluded, the land-value tax both leads to greater business development and raises property values.

“What we found overall, looking at past tax reduction in Detroit and other cities, is that in reducing the rate at which people in deep tax distress go through foreclosure, it has a fairly large effect,” Allen says. “It has some effect on allowing business to reinvest in properties. We are seeing a lot more attraction of investment. And it’s got the virtue of being a rules-based system.”

Those empirical results, he notes, helped confirm the sense that a policy change could help growth in Detroit.

“That really validated the hunch we were following,” Allen says.

The widespread attention the policy proposal has garnered could not really have been predicted. The tax has not yet been implemented in Detroit, although it has been a prominent part of civic debates there. Allen has been asked to consult on tax policy by officials in numerous large cities, and is hopeful the concept will gain still more traction.

Meanwhile, at MIT, Allen has one more year to go in his doctoral program. On top of his academic research, he has been an active participant in Institute matters, helping reshape graduate-school policies on multiple fronts.

For instance, Allen was part of the Graduate Housing Working Group, whose efforts helped spur MIT to build Graduate Junction, a new housing complex for 675 graduate students on Vassar Street in Cambridge, Massachusetts. The name also refers to the Grand Junction rail line that runs nearby; the complex formally opened in 2024.

“Innovative places struggle to build housing fast enough,” Allen said at the time Graduate Junction opened, also noting that “new housing for students reduces price pressure on the rest of the Cambridge community.”

Commenting on it now, he adds, “Maybe to most people graduate housing policy doesn’t sound that fun, but to me these are very absorbing questions.”

And ultimately, Allen says, the intellectual problems in either domain can be similar, whether he is working on city policy issues or campus enhancements.

“The reason I think planning fits so well here at MIT is, a lot of what I do is like policy engineering,” Allen says. “It’s really important to understand system constraints, and think seriously about finding solutions that can be built to purpose. I think that’s why I’ve felt at home here at MIT, working on these outside public policy topics, and projects for the Institute. You need to take seriously what people say about the constraints in their lives.”

Nature Climate Change, Published online: 20 August 2025; doi:10.1038/s41558-025-02410-1

Climate beliefs do not exist in isolation but form an interconnected network known as a belief system. This study analyses the density and inconsistency of belief systems and their associations with informational and socioeconomic factors to inform effective climate change communication strategies.

John “JJ” Joannopoulos, the Francis Wright Davis Professor of Physics at MIT and director of the MIT Institute for Soldier Nanotechnologies (ISN), passed away on Aug. 17. He was 78. 

Joannopoulos was a prolific researcher in the field of theoretical condensed-matter physics, and an early pioneer in the study and application of photonic crystals. Many of his discoveries, in the ways materials can be made to manipulate light, have led to transformative and life-saving technologies, from chip-based optical wave guides, to wireless energy transfer to health-monitoring textiles, to precision light-based surgical tools.

His remarkable career of over 50 years was spent entirely at MIT, where he was known as much for his generous and unwavering mentorship as for his contributions to science. He made a special point to keep up rich and meaningful collaborations with many of his former students and postdocs, dozens of whom have gone on to faculty positions at major universities, and to leadership roles in the public and private sectors. In his five decades at MIT, he made lasting connections across campus, both in service of science, and friendship.

“A scientific giant, inspiring leader, and a masterful communicator, John carried a generous and loving heart,” says Yoel Fink PhD ’00, an MIT professor of materials science and engineering who was Joannopoulos’ former student and a longtime collaborator. “He chose to see the good in people, keeping his mind and heart always open. Asking little for himself, he gave everything in care of others. John lived a life of deep impact and meaning — savoring the details of truth-seeking, achieving rare discoveries and mentoring generations of students to achieve excellence. With warmth, humor, and a never-ending optimism, JJ left an indelible impact on science and on all who had the privilege to know him. Above all, he was a loving husband, father, grandfather, friend, and mentor.”

“In the end, the most remarkable thing about him was his unmatched humanity, his ability to make you feel that you were the most important thing in the world that deserved his attention, no matter who you were,” says Raul Radovitzky, ISN associate director and the Jerome C. Hunsaker Professor in MIT’s Department of Aeronautics and Astronautics. “The legacy he leaves is not only in equations and innovations, but in the lives he touched, the minds he inspired, and the warmth he spread in every room he entered.”

“JJ was a very special colleague: a brilliant theorist who was also adept at identifying practical applications; a caring and inspiring mentor of younger scientists; a gifted teacher who knew every student in his class by name,” says Deepto Chakrabarty ’88, the William A. M. Burden Professor in Astrophysics and head of MIT’s Department of Physics. “He will be deeply missed.”

Layers of light

John Joannopoulos was born in 1947 in New York City, where his parents both emigrated from Greece. His father was a playwright, and his mother worked as a psychologist. From an early age, Joannopoulos knew he wanted to be a physicist — mainly because the subject was his most challenging in school. In a recent interview with MIT News, he enthusiastically shared: “You probably wouldn’t believe this, but it’s true: I wanted to be a physics professor since I was in high school! I loved the idea of being able to work with students, and being able to have ideas.”

He attended the University of California at Berkeley, where he received a bachelor’s degree in 1968, and a PhD in 1974, both in physics. That same year, he joined the faculty at MIT, where he would spend his 50-plus-year career — though at the time, the chances of gaining a long-term foothold at the Institute seemed slim, as Joannopoulos told MIT News.

“The chair of the physics department was the famous nuclear physicist, Herman Feshbach, who told me the probability that I would get tenure was something like 30 percent,” Joannopoulos recalled. “But when you’re young and just starting off, it was certainly better than zero, and I thought, that was fine — there was hope down the line.”

Starting out at MIT, Joannopoulos knew exactly what he wanted to do. He quickly set up a group to study theoretical condensed-matter physics, and specifically, ab initio physics, meaning physics “from first principles.” In this initial work, he sought to build theoretical models to predict the electronic behavior and structure of materials, based solely on the atomic numbers of the atoms in a material. Such foundational models could be applied to understand and design a huge range of materials and structures.

Then, in the early 1990s, Joannopoulos took a research turn, spurred by a paper by physicist Eli Yablonovitch at the University of California at Los Angeles, who did some preliminary work on materials that can affect the behavior of photons, or particles of light. Joannopoulos recognized a connection with his first-principles work with electrons. Along with his students, he applied that approach to predict the fundamental behavior of photons in different classes of materials. His group was one of the first to pioneer the field of photonic crystals, and the study of how materials can be manipulated at the nanoscale to control the behavior of light traveling through. In 1995, Joannopoulos co-authored the first textbook on the subject.

And in 1998, he took on a more-than-century-old assumption about how light should reflect, and turned it on its head. That assumption predicted that light, shining onto a structure made of multiple refractive layers, could reflect back, but only for a limited range of angles. But in fact, Joannopoulos and his group showed that the opposite is true: If the structure’s layers followed a particular design criteria, the structure as a whole could reflect light coming from any and all angles. This structure, was called the “perfect mirror.”

That insight led to another: If the structure were rolled into a tube, the resulting hollow fiber could act as a perfect optical conduit. Any light traveling through the fiber would reflect and bounce around within the fiber, with none scattering away. Joannopoulos and his group applied this insight to develop the first precision “optical scalpel” — a fiber that can be safely handled, while delivering a highly focused laser, precise and powerful enough to perform delicate surgical procedures. Joannopoulos helped to commercialize the new tool with a startup, Omniguide, that has since provided the optical scalpel to assist in hundreds of thousands of medical procedures around the world.

Legendary mentor

In 2006, Joannopoulos took the helm as director of MIT’s Institute for Soldier Nanotechnologies — a post he steadfastly held for almost 20 years. During his dedicated tenure, he worked with ISN members across campus and in departments outside his own, getting to know and champion their work. He has facilitated countless collaborations between MIT faculty, industry partners, and the U.S. Department of Defense. Among the many projects he raised support for were innovations in lightweight armor, hyperspectral imaging, energy-efficient batteries, and smart and responsive fabrics.

Joannopoulos helped to translate many basic science insights into practical applications. He was a cofounder of six spinoff companies based on his fundamental research, and helped to create dozens more companies, which have advanced technologies as wide-ranging as laser surgery tools, to wireless electric power transmission, transparent display technologies, and optical computing. He was awarded 126 patents for his many discoveries, and has authored over 750 peer-reviewed papers.

In recognition of his wide impact and contributions, Joannopoulos was elected to the National Academy of Sciences and the American Academy of Arts and Sciences. He was also a fellow of both the American Physical Society and the American Association for the Advancement of Science. Over his 50-plus-year career, he was the recipient of many scientific awards and honors including the Max Born Award, and the Aneesur Rahman Prize in Computational Physics. Joannopoulos was also a gifted classroom teacher, and was recognized at MIT with the Buechner Teaching Prize in Physics and the Graduate Teaching Award in Science.

This year, Joannopoulos was the recipient of MIT’s Killian Achievement Award, which recognizes the extraordinary lifetime contributions of a member of the MIT faculty. In addition to the many accomplishments Joannopoulos has made in science, the award citation emphasized his lasting impact on the generations of students he has mentored:

“Professor Joannopoulos has served as a legendary mentor to generations of students, inspiring them to achieve excellence in science while at the same time facilitating the practical benefit to society through entrepreneurship,” the citation reads. “Through all of these individuals he has impacted — not to mention their academic descendants — Professor Joannopoulos has had a vast influence on the development of science in recent decades.”

“JJ was an amazing scientist: He published hundreds of papers that have been cited close to 200,000 times. He was also a serial entrepreneur: Companies he cofounded raised hundreds of millions of dollars and employed hundreds of people,” says MIT Professor Marin Soljacic ’96, a former postdoc under Joannopoulos who with him cofounded a startup, Witricity. “He was an amazing mentor, a close friend, and like a scientific father to me. He always had time for me, any time of the day, and as much as I needed.”

Indeed, Joannopoulos strived to meaningfully support his many students. In the classroom, he “was legendary,” says friend and colleague Patrick Lee ’66, PhD ’70, who recalls that Joannopoulos would make a point of memorizing the names and faces of more than 100 students on the first day of class, and calling them each by their first name, starting on the second day, and for the rest of the term.

What’s more, Joannopoulos encouraged graduate students and postdocs to follow their ideas, even when they ran counter to his own.

“John did not produce clones,” says Lee, who is an MIT professor emeritus of physics. “He showed them the way to do science by example, by caring and by sharing his optimism. I have never seen someone so deeply loved by his students.”

Even students who stepped off the photonics path have kept in close contact with their mentor, as former student and MIT professor Josh Winn ’94, SM ’94, PhD ’01 has done.

“Even though our work together ended more than 25 years ago, and I now work in a different field, I still feel like part of the Joannopoulos academic family,” says Winn, who is now a professor of astrophysics at Princeton University. “It's a loyal group with branches all over the world. We even had our own series of conferences, organized by former students to celebrate John's 50th, 60th, and 70th birthdays. Most professors would consider themselves fortunate to have even one such ‘festschrift’ honoring their legacy.”

MIT professor of mathematics Steven Johnson ’95, PhD ’01, a former student and frequent collaborator, has experienced personally, and seen many times over, Joannopoulos’ generous and open-door mentorship.

“In every collaboration, I’ve unfailingly observed him to cast a wide net to value multiple voices, to ensure that everyone feels included and valued, and to encourage collaborations across groups and fields and institutions,” Johnson says. “Kind, generous, and brimming with infectious enthusiasm and positivity, he set an example so many of his lucky students have striven to follow.”

Joannopoulos started at MIT around the same time as Marc Kastner, who had a nearby office on the second floor of Building 13.

“I would often hear loud arguments punctuated by boisterous laughter, coming from John’s office, where he and his students were debating physics,” recalls Kastner, who is the Donner Professor of Physics Emeritus at MIT. “I am sure this style of interaction is what made him such a great mentor.”

“He exuded such enthusiasm for science and good will to others that he was just good fun to be around,” adds friend and colleague Erich Ippen, MIT professor emeritus of physics.

“John was indeed a great man — a very special one. Everyone who ever worked with him understands this,” says Stanford University physics professor Robert Laughlin PhD ’79, one of Joannopoulos’ first graduate students, who went on to win the 1998 Nobel Prize in Physics. “He sprinkled a kind of transformative magic dust on people that induced them to dedicate every waking moment to the task of making new and wonderful things. You can find traces of it in lots of places around the world that matter, all of them the better for it. There’s quite a pile of it in my office.”

Joannopoulos is survived by his wife, Kyri Dunussi-Joannopoulos; their three daughters, Maria, Lena, and Alkisti; and their families. Details for funeral and memorial services are forthcoming.

A zero-day vulnerability in WinRAR is being exploited by at least two Russian criminal groups:

The vulnerability seemed to have super Windows powers. It abused alternate data streams, a Windows feature that allows different ways of representing the same file path. The exploit abused that feature to trigger a previously unknown path traversal flaw that caused WinRAR to plant malicious executables in attacker-chosen file paths %TEMP% and %LOCALAPPDATA%, which Windows normally makes off-limits because of their ability to execute code.

More details in the article...

The Transportation secretary says the effort is about safety, but some rail experts and the wind industry say there's little evidence for that.

The U.S. Chemical Safety board is investigating an Aug. 11 explosion that killed two people and injured 10 at a U.S. Steel plant.

The state told the court that the administration is “wrong on the law.”

An international nonprofit has helped carbon markets in Peru, Kenya, Pakistan and Mexico. Its guide is aimed at "ensuring integrity."

Communities across the nation are also building flood walls, berms and levees to protect areas that lack adequate natural protection.

A changing climate has made residents of northern Pakistan's river-carved mountainous areas more vulnerable to sudden, heavy rains.

Pedro Sanchez said he’ll present a plan to enhance nationwide coordination to improve the country’s readiness for climate disasters.

The sudden, violent storms dump large volumes of rain in a short time — usually about 4 inches within an hour over a localized area.

Using machine learning, MIT chemical engineers have created a computational model that can predict how well any given molecule will dissolve in an organic solvent — a key step in the synthesis of nearly any pharmaceutical. This type of prediction could make it much easier to develop new ways to produce drugs and other useful molecules.

The new model, which predicts how much of a solute will dissolve in a particular solvent, should help chemists to choose the right solvent for any given reaction in their synthesis, the researchers say. Common organic solvents include ethanol and acetone, and there are hundreds of others that can also be used in chemical reactions.

“Predicting solubility really is a rate-limiting step in synthetic planning and manufacturing of chemicals, especially drugs, so there’s been a longstanding interest in being able to make better predictions of solubility,” says Lucas Attia, an MIT graduate student and one of the lead authors of the new study.

The researchers have made their model freely available, and many companies and labs have already started using it. The model could be particularly useful for identifying solvents that are less hazardous than some of the most commonly used industrial solvents, the researchers say.

“There are some solvents which are known to dissolve most things. They’re really useful, but they’re damaging to the environment, and they’re damaging to people, so many companies require that you have to minimize the amount of those solvents that you use,” says Jackson Burns, an MIT graduate student who is also a lead author of the paper. “Our model is extremely useful in being able to identify the next-best solvent, which is hopefully much less damaging to the environment.”

William Green, the Hoyt Hottel Professor of Chemical Engineering and director of the MIT Energy Initiative, is the senior author of the study, which appears today in Nature Communications. Patrick Doyle, the Robert T. Haslam Professor of Chemical Engineering, is also an author of the paper.

Solving solubility

The new model grew out of a project that Attia and Burns worked on together in an MIT course on applying machine learning to chemical engineering problems. Traditionally, chemists have predicted solubility with a tool known as the Abraham Solvation Model, which can be used to estimate a molecule’s overall solubility by adding up the contributions of chemical structures within the molecule. While these predictions are useful, their accuracy is limited.

In the past few years, researchers have begun using machine learning to try to make more accurate solubility predictions. Before Burns and Attia began working on their new model, the state-of-the-art model for predicting solubility was a model developed in Green’s lab in 2022.

That model, known as SolProp, works by predicting a set of related properties and combining them, using thermodynamics, to ultimately predict the solubility. However, the model has difficulty predicting solubility for solutes that it hasn’t seen before.

“For drug and chemical discovery pipelines where you’re developing a new molecule, you want to be able to predict ahead of time what its solubility looks like,” Attia says.

Part of the reason that existing solubility models haven’t worked well is because there wasn’t a comprehensive dataset to train them on. However, in 2023 a new dataset called BigSolDB was released, which compiled data from nearly 800 published papers, including information on solubility for about 800 molecules dissolved about more than 100 organic solvents that are commonly used in synthetic chemistry.

Attia and Burns decided to try training two different types of models on this data. Both of these models represent the chemical structures of molecules using numerical representations known as embeddings, which incorporate information such as the number of atoms in a molecule and which atoms are bound to which other atoms. Models can then use these representations to predict a variety of chemical properties.

One of the models used in this study, known as FastProp and developed by Burns and others in Green’s lab, incorporates “static embeddings.” This means that the model already knows the embedding for each molecule before it starts doing any kind of analysis.

The other model, ChemProp, learns an embedding for each molecule during the training, at the same time that it learns to associate the features of the embedding with a trait such as solubility. This model, developed across multiple MIT labs, has already been used for tasks such as antibiotic discovery, lipid nanoparticle design, and predicting chemical reaction rates.

The researchers trained both types of models on over 40,000 data points from BigSolDB, including information on the effects of temperature, which plays a significant role in solubility. Then, they tested the models on about 1,000 solutes that had been withheld from the training data. They found that the models’ predictions were two to three times more accurate than those of SolProp, the previous best model, and the new models were especially accurate at predicting variations in solubility due to temperature.

“Being able to accurately reproduce those small variations in solubility due to temperature, even when the overarching experimental noise is very large, was a really positive sign that the network had correctly learned an underlying solubility prediction function,” Burns says.

Accurate predictions

The researchers had expected that the model based on ChemProp, which is able to learn new representations as it goes along, would be able to make more accurate predictions. However, to their surprise, they found that the two models performed essentially the same. That suggests that the main limitation on their performance is the quality of the data, and that the models are performing as well as theoretically possible based on the data that they’re using, the researchers say.

“ChemProp should always outperform any static embedding when you have sufficient data,” Burns says. “We were blown away to see that the static and learned embeddings were statistically indistinguishable in performance across all the different subsets, which indicates to us that that the data limitations that are present in this space dominated the model performance.”

The models could become more accurate, the researchers say, if better training and testing data were available — ideally, data obtained by one person or a group of people all trained to perform the experiments the same way.

“One of the big limitations of using these kinds of compiled datasets is that different labs use different methods and experimental conditions when they perform solubility tests. That contributes to this variability between different datasets,” Attia says.

Because the model based on FastProp makes its predictions faster and has code that is easier for other users to adapt, the researchers decided to make that one, known as FastSolv, available to the public. Multiple pharmaceutical companies have already begun using it.

“There are applications throughout the drug discovery pipeline,” Burns says. “We’re also excited to see, outside of formulation and drug discovery, where people may use this model.”

The research was funded, in part, by the U.S. Department of Energy.

In a victory for transparency, the government contractor Pen-Link agreed to disclose the prices and descriptions of surveillance products that it sold to a local California Sheriff's office.

The settlement ends a months-long California public records lawsuit with the Electronic Frontier Foundation and the San Joaquin County Sheriff’s Office. The settlement provides further proof that the surveillance tools used by governments are not secret and shouldn’t be treated that way under the law.

Last year, EFF submitted a California public records request to the San Joaquin County Sheriff’s Office for information about its work with Pen-Link and its subsidy Cobwebs Technology. Pen-Link went to court to try to block the disclosure, claiming the names of its products and prices were trade secrets. EFF later entered the case to obtain the records it requested.  

The Records Show the Sheriff Bought Online Monitoring Tools

The records disclosed in the settlement show that in late 2023, the Sheriff’s Office paid $180,000 for a two-year subscription to the Tangles “Web Intelligence Platform,” which is a Cobwebs Technologies product that allows the Sheriff to monitor online activity. The subscription allows the Sheriff to perform hundreds of searches and requests per month. The source of information includes the “Dark Web” and “Webloc,” according to the price quotation. According to the settlement, the Sheriff’s Office was offered but did not purchase a series of other add-ons including “AI Image processing” and “Webloc Geo source data per user/Seat.”

Have you been blocked from receiving similar information? We’d like to hear from you.

The intelligence platform overall has been described in other documents as analyzing data from the “open, deep, and dark web, to mobile and social.” And Webloc has been described as a platform that “provides access to vast amounts of location-based data in any specified geographic location.” Journalists at multiple news outlets have chronicled Pen-Link's technology and have published Cobwebs training manuals that demonstrate that its product can be used to target activists and independent journalists. Major local, state, and federal agencies use Pen-Link's technology.

The records also show that in late 2022 the Sheriff’s Office purchased some of Pen-Link’s more traditional products that help law enforcement execute and analyze data from wiretaps and pen-registers after a court grants approval. 

Government Surveillance Tools Are Not Trade Secrets

The public has a right to know what surveillance tools the government is using, no matter whether the government develops its own products or purchases them from private contractors. There are a host of policy, legal, and factual reasons that the surveillance tools sold by contractors like Pen-Link are not trade secrets.

Public information about these products and prices helps communities have informed conversations and make decisions about how their government should operate. In this case, Pen-Link argued that its products and prices are trade secrets partially because governments rely on the company to “keep their data analysis capabilities private.” The company argued that clients would “lose trust” and governments may avoid “purchasing certain services” if the purchases were made public. This troubling claim highlights the importance of transparency. The public should be skeptical of any government tool that relies on secrecy to operate.

Information about these tools is also essential for defendants and criminal defense attorneys, who have the right to discover when these tools are used during an investigation. In support of its trade secret claim, Pen-Link cited terms of service that purported to restrict the government from disclosing its use of this technology without the company’s consent. Terms like this cannot be used to circumvent the public’s right to know, and governments should not agree to them.

Finally, in order for surveillance tools and their prices to be protected as a trade secret under the law, they have to actually be secret. However, Pen-Link’s tools and their prices are already public across the internet—in previous public records disclosures, product descriptions, trademark applications, and government websites.

 Lessons Learned

Government surveillance contractors should consider the policy implications, reputational risks, and waste of time and resources when attempting to hide from the public the full terms of their sales to law enforcement.

Cases like these, known as reverse-public records act lawsuits, are troubling because a well-resourced company can frustrate public access by merely filing the case. Not every member of the public, researcher, or journalist can afford to litigate their public records request. Without a team of internal staff attorneys, it would have cost EFF tens of thousands of dollars to fight this lawsuit.

 Luckily in this case, EFF had the ability to fight back. And we will continue our surveillance transparency work. That is why EFF required some attorneys’ fees to be part of the final settlement.

Related Cases: Pen-Link v. County of San Joaquin Sheriff’s Office