There are more than 120 AI bills in Congress right now

More than 120 bills related to regulating artificial intelligence are currently floating around the US Congress.

They’re pretty varied. One aims to improve knowledge of AI in public schools, while another is pushing for model developers to disclose what copyrighted material they use in their training.  Three deal with mitigating AI robocalls, while two address biological risks from AI. There’s even a bill that prohibits AI from launching a nuke on its own.

The flood of bills is indicative of the desperation Congress feels to keep up with the rapid pace of technological improvements. “There is a sense of urgency. There’s a commitment to addressing this issue, because it is developing so quickly and because it is so crucial to our economy,” says Heather Vaughan, director of communications for the US House of Representatives Committee on Science, Space, and Technology.

Because of the way Congress works, the majority of these bills will never make it into law. But simply taking a look at all the different bills that are in motion can give us insight into policymakers’ current preoccupations: where they think the dangers are, what each party is focusing on, and more broadly, what vision the US is pursuing when it comes to AI and how it should be regulated.

That’s why, with help from the Brennan Center for Justice, which created a tracker with all the AI bills circulating in various committees in Congress right now, MIT Technology Review has taken a closer look to see if there’s anything we can learn from this legislative smorgasbord. 

As you can see, it can seem as if Congress is trying to do everything at once when it comes to AI. To get a better sense of what may actually pass, it’s useful to look at what bills are moving along to potentially become law. 

A bill typically needs to pass a committee, or a smaller body of Congress, before it is voted on by the whole Congress. Many will fall short at this stage, while others will simply be introduced and then never spoken of again. This happens because there are so many bills presented in each session, and not all of them are given equal consideration. If the leaders of a party don’t feel a bill from one of its members can pass, they may not even try to push it forward. And then, depending on the makeup of Congress, a bill’s sponsor usually needs to get some members of the opposite party to support it for it to pass. In the current polarized US political climate, that task can be herculean. 

Congress has passed legislation on artificial intelligence before. Back in 2020, the National AI Initiative Act was part of the Defense Authorization Act, which invested resources in AI research and provided support for public education and workforce training on AI.

And some of the current bills are making their way through the system. The Senate Commerce Committee pushed through five AI-related bills at the end of July. The bills focused on authorizing the newly formed US AI Safety Institute (AISI) to create test beds and voluntary guidelines for AI models. The other bills focused on expanding education on AI, establishing public computing resources for AI research, and criminalizing the publication of deepfake pornography. The next step would be to put the bills on the congressional calendar to be voted on, debated, or amended.

“The US AI Safety Institute, as a place to have consortium building and easy collaboration between corporate and civil society actors, is amazing. It’s exactly what we need,” says Yacine Jernite, an AI researcher at Hugging Face.

The progress of these bills is a positive development, says Varun Krovi, executive director of the Center for AI Safety Action Fund. “We need to codify the US AI Safety Institute into law if you want to maintain our leadership on the global stage when it comes to standards development,” he says. “And we need to make sure that we pass a bill that provides computing capacity required for startups, small businesses, and academia to pursue AI.”

Following the Senate’s lead, the House Committee on Science, Space, and Technology just passed nine more bills regarding AI on September 11. Those bills focused on improving education on AI in schools, directing the National Institute of Standards and Technology (NIST) to establish guidelines for artificial-intelligence systems, and expanding the workforce of AI experts. These bills were chosen because they have a narrower focus and thus might not get bogged down in big ideological battles on AI, says Vaughan.

“It was a day that culminated from a lot of work. We’ve had a lot of time to hear from members and stakeholders. We’ve had years of hearings and fact-finding briefings on artificial intelligence,” says Representative Haley Stevens, one of the Democratic members of the House committee.

Many of the bills specify that any guidance they propose for the industry is nonbinding and that the goal is to work with companies to ensure safe development rather than curtail innovation. 

For example, one of the bills from the House, the AI Development Practices Act, directs NIST to establish “voluntary guidance for practices and guidelines relating to the development … of AI systems” and a “voluntary risk management framework.” Another bill, the AI Advancement and Reliability Act, has similar language. It supports “the development of voluntary best practices and technical standards” for evaluating AI systems. 

“Each bill contributes to advancing AI in a safe, reliable, and trustworthy manner while fostering the technology’s growth and progress through innovation and vital R&D,” committee chairman Frank Lucas, an Oklahoma Republican, said in a press release on the bills coming out of the House.

“It’s emblematic of the approach that the US has taken when it comes to tech policy. We hope that we would move on from voluntary agreements to mandating them,” says Krovi.

Avoiding mandates is a practical matter for the House committee. “Republicans don’t go in for mandates for the most part. They generally aren’t going to go for that. So we would have a hard time getting support,” says Vaughan. “We’ve heard concerns about stifling innovation, and that’s not the approach that we want to take.” When MIT Technology Review asked about the origin of these concerns, they were attributed to unidentified “third parties.” 

And fears of slowing innovation don’t just come from the Republican side. “What’s most important to me is that the United States of America is establishing aggressive rules of the road on the international stage,” says Stevens. “It’s concerning to me that actors within the Chinese Communist Party could outpace us on these technological advancements.”

But these bills come at a time when big tech companies have ramped up lobbying efforts on AI. “Industry lobbyists are in an interesting predicament—their CEOs have said that they want more AI regulation, so it’s hard for them to visibly push to kill all AI regulation,” says David Evan Harris, who teaches courses on AI ethics at the University of California, Berkeley. “On the bills that they don’t blatantly try to kill, they instead try to make them meaningless by pushing to transform the language in the bills to make compliance optional and enforcement impossible.”

“A [voluntary commitment] is something that is also only accessible to the largest companies,” says Jernite at Hugging Face, claiming that sometimes the ambiguous nature of voluntary commitments allows big companies to set definitions for themselves. “If you have a voluntary commitment—that is, ‘We’re going to develop state-of-the-art watermarking technology’—you don’t know what state-of-the-art means. It doesn’t come with any of the concrete things that make regulation work.”

“We are in a very aggressive policy conversation about how to do this right, and how this carrot and stick is actually going to work,” says Stevens, indicating that Congress may ultimately draw red lines that AI companies must not cross.

There are other interesting insights to be gleaned from looking at the bills all together. Two-thirds of the AI bills are sponsored by Democrats. This isn’t too surprising, since some House Republicans have claimed to want no AI regulations, believing that guardrails will slow down progress.

The topics of the bills (as specified by Congress) are dominated by science, tech, and communications (28%), commerce (22%), updating government operations (18%), and national security (9%). Topics that don’t receive much attention include labor and employment (2%), environmental protection (1%), and civil rights, civil liberties, and minority issues (1%).

The lack of a focus on equity and minority issues came into view during the Senate markup session at the end of July. Senator Ted Cruz, a Republican, added an amendment that explicitly prohibits any action “to ensure inclusivity and equity in the creation, design, or development of the technology.” Cruz said regulatory action might slow US progress in AI, allowing the country to fall behind China.

On the House side, there was also a hesitation to work on bills dealing with biases in AI models. “None of our bills are addressing that. That’s one of the more ideological issues that we’re not moving forward on,” says Vaughan.

The lead Democrat on the House committee, Representative Zoe Lofgren, told MIT Technology Review, “It is surprising and disappointing if any of my Republican colleagues have made that comment about bias in AI systems. We shouldn’t tolerate discrimination that’s overt and intentional any more than we should tolerate discrimination that occurs because of bias in AI systems. I’m not really sure how anyone can argue against that.”

After publication, Vaughan clarified that “[Bias] is one of the bigger, more cross-cutting issues, unlike the narrow, practical bills we considered that week. But we do care about bias as an issue,” and she expects it to be addressed within an upcoming House Task Force report.

One issue that may rise above the partisan divide is deepfakes. The Defiance Act, one of several bills addressing them, is cosponsored by a Democratic senator, Amy Klobuchar, and a Republican senator, Josh Hawley. Deepfakes have already been abused in elections; for example, someone faked Joe Biden’s voice for a robocall to tell citizens not to vote. And the technology has been weaponized to victimize people by incorporating their images into pornography without their consent. 

“I certainly think that there is more bipartisan support for action on these issues than on many others,” says Daniel Weiner, director of the Brennan Center’s Elections & Government Program. “But it remains to be seen whether that’s going to win out against some of the more traditional ideological divisions that tend to arise around these issues.” 

Although none of the current slate of bills have resulted in laws yet, the task of regulating any new technology, and specifically advanced AI systems that no one entirely understands, is difficult. The fact that Congress is making any progress at all may be surprising in itself. 

“Congress is not sleeping on this by any stretch of the means,” says Stevens. “We are evaluating and asking the right questions and also working alongside our partners in the Biden-Harris administration to get us to the best place for the harnessing of artificial intelligence.”

Update: We added further comments from the Republican spokesperson.

AI could be a game changer for people with disabilities

As a lifelong disabled person who constantly copes with multiple conditions, I have a natural tendency to view emerging technologies with skepticism. Most new things are built for the majority of people—in this case, people without disabilities—and the truth of the matter is there’s no guarantee I’ll have access to them.

There are certainly exceptions to the rule. A prime example is the iPhone. Although discrete accessibility software did not appear until the device’s third-generation model, in 2009, earlier generations were still revolutionary for me. After I’d spent years using flip phones with postage-stamp-size screens and hard-to-press buttons, the fact that the original iPhone had a relatively large screen and a touch-based UI was accessibility unto itself. 

AI could make these kinds of jumps in accessibility more common across a wide range of technologies. But you probably haven’t heard much about that possibility. While the New York Times sues OpenAI over ChatGPT’s scraping of its content and everyone ruminates over the ethics of AI tools, there seems to be less consideration of the good ChatGPT can do for people of various abilities. For someone with visual and motor delays, using ChatGPT to do research can be a lifesaver. Instead of trying to manage a dozen browser tabs with Google searches and other pertinent information, you can have ChatGPT collate everything into one space. Likewise, it’s highly plausible that artists who can’t draw in the conventional manner could use voice prompts to have Midjourney or Adobe Firefly create what they’re thinking of. That might be the only way for such a person to indulge an artistic passion. 

For those who, like me, are blind or have low vision, the ability to summon a ride on demand and go anywhere without imposing on anyone else for help is a huge deal.

Of course, data needs to be vetted for accuracy and gathered with permission—there are ample reasons to be wary of AI’s potential to serve up wrong or potentially harmful, ableist information about the disabled community. Still, it feels unappreciated (and underreported) that AI-based software can truly be an assistive technology, enabling people to do things they otherwise would be excluded from. AI could give a disabled person agency and autonomy. That’s the whole point of accessibility—freeing people in a society not designed for their needs.

The ability to automatically generate video captions and image descriptions provides additional examples of how automation can make computers and productivity technology more accessible. And more broadly, it’s hard not to be enthused about ever-burgeoning technologies like autonomous vehicles. Most tech journalists and other industry watchers are interested in self-driving cars for the sheer novelty, but the reality is the AI software behind vehicles like Waymo’s fleet of Jaguar SUVs is quite literally enabling many in the disability community to exert more agency over their transport. For those who, like me, are blind or have low vision, the ability to summon a ride on demand and go anywhere without imposing on anyone else for help is a huge deal. It’s not hard to envision a future in which, as the technology matures, autonomous vehicles are normalized to the point where blind people could buy their own cars. 

At the same time, AI is enabling serious advances in technology for people with limb differences. How exciting will it be, decades from now, to have synthetic arms and legs, hands or feet, that more or less function like the real things? Similarly, the team at Boston-based Tatum Robotics is combining hardware with AI to make communication more accessible for deaf-blind people: A robotic hand forms hand signs, or words in American Sign Language that can be read tactilely against the palm. Like autonomous vehicles, these applications have enormous potential to positively influence the everyday lives of countless people. All this goes far beyond mere chatbots.

It should be noted that disabled people historically have been among the earliest adopters of new technologies. AI is no different, yet public discourse routinely fails to meaningfully account for this. After all, AI plays to a computer’s greatest strength: automation. As time marches on, the way AI grows and evolves will be unmistakably and indelibly shaped by disabled people and our myriad needs and tolerances. It will offer us more access to information, to productivity, and most important, to society writ large.

Steven Aquino is a freelance tech journalist covering accessibility and assistive technologies. He is based in San Francisco.

DHS plans to collect biometric data from migrant children “down to the infant”

The US Department of Homeland Security (DHS) plans to collect and analyze photos of the faces of migrant children at the border in a bid to improve facial recognition technology, MIT Technology Review can reveal. This includes children “down to the infant,” according to John Boyd, assistant director of the department’s Office of Biometric Identity Management (OBIM), where a key part of his role is to research and develop future biometric identity services for the government. 

As Boyd explained at a conference in June, the key question for OBIM is, “If we pick up someone from Panama at the southern border at age four, say, and then pick them up at age six, are we going to recognize them?”

Facial recognition technology (FRT) has traditionally not been applied to children, largely because training data sets of real children’s faces are few and far between, and consist of either low-quality images drawn from the internet or small sample sizes with little diversity. Such limitations reflect the significant sensitivities regarding privacy and consent when it comes to minors. 

In practice, the new DHS plan could effectively solve that problem. According to Syracuse University’s Transactional Records Access Clearinghouse (TRAC), 339,234 children arrived at the US-Mexico border in 2022, the last year for which numbers are currently available. Of those children, 150,000 were unaccompanied—the highest annual number on record. If the face prints of even 1% of those children had been enrolled in OBIM’s craniofacial structural progression program, the resulting data set would dwarf nearly all existing data sets of real children’s faces used for aging research.

It’s unclear to what extent the plan has already been implemented; Boyd tells MIT Technology Review that to the best of his knowledge, the agency has not yet started collecting data under the program, but he adds that as “the senior executive,” he would “have to get with [his] staff to see.” He could only confirm that his office is “funding” it. Despite repeated requests, Boyd did not provide any additional information. 

Boyd says OBIM’s plan to collect facial images from children under 14 is possible due to recent “rulemaking” at “some DHS components,” or sub-offices, that have removed age restrictions on the collection of biometric data. US Customs and Border Protection (CBP), the US Transportation Security Administration, and US Immigration and Customs Enforcement declined to comment before publication. US Citizenship and Immigration Services (USCIS) did not respond to multiple requests for comment. OBIM referred MIT Technology Review back to DHS’s main press office. 

DHS did not comment on the program prior, but sent an emailed statement following publication: “The Department of Homeland Security uses various forms of technology to execute its mission, including some biometric capabilities. DHS ensures all technologies, regardless of type, are operated under the established authorities and within the scope of the law. We are committed to protecting the privacy, civil rights, and civil liberties of all individuals who may be subject to the technology we use to keep the nation safe and secure.”

Boyd spoke publicly about the plan in June at the Federal Identity Forum and Exposition, an annual identity management conference for federal employees and contractors. But close observers of DHS that we spoke with—including a former official, representatives of two influential lawmakers who have spoken out about the federal government’s use of surveillance technologies, and immigrants’ rights organizations that closely track policies affecting migrants—were unaware of any new policies allowing biometric data collection of children under 14. 

That is not to say that all of them are surprised. “That tracks,” says one former CBP official who has visited several migrant processing centers on the US-Mexico border and requested anonymity to speak freely. He says “every center” he visited “had biometric identity collection, and everybody was going through it,” though he was unaware of a specific policy mandating the practice. “I don’t recall them separating out children,” he adds.

“The reports of CBP, as well as DHS more broadly, expanding the use of facial recognition technology to track migrant children is another stride toward a surveillance state and should be a concern to everyone who values privacy,” Justin Krakoff, deputy communications director for Senator Jeff Merkley of Oregon, said in a statement to MIT Technology Review. Merkley has been an outspoken critic of both DHS’s immigration policies and of government use of facial recognition technologies

Beyond concerns about privacy, transparency, and accountability, some experts also worry about testing and developing new technologies using data from a population that has little recourse to provide—or withhold—consent. 

Could consent “actually take into account the vast power differentials that are inherent in the way that this is tested out on people?” asks Petra Molnar, author of The Walls Have Eyes: Surviving Migration in the Age of AI. “And if you arrive at a border … and you are faced with the impossible choice of either: get into a country if you give us your biometrics, or you don’t.”

“That completely vitiates informed consent,” she adds.

This question becomes even more challenging when it comes to children, says Ashley Gorski, a senior staff attorney with the American Civil Liberties Union. DHS “should have to meet an extremely high bar to show that these kids and their legal guardians have meaningfully consented to serve as test subjects,” she says. “There’s a significant intimidation factor, and children aren’t as equipped to consider long-term risks.”

Murky new rules

The Office of Biometric Identity Management, previously known as the US Visitor and Immigrant Status Indicator Technology Program (US-VISIT), was created after 9/11 with the specific mandate of collecting biometric data—initially only fingerprints and photographs—from all non-US citizens who sought to enter the country. 

Since then, DHS has begun collecting face prints, iris and retina scans, and even DNA, among other modalities. It is also testing new ways of gathering this data—including through contactless fingerprint collection, which is currently deployed at five sites on the border, as Boyd shared in his conference presentation. 

Since 2023, CBP has been using a mobile app, CBP One, for asylum seekers to submit biometric data even before they enter the United States; users are required to take selfies periodically to verify their identity. The app has been riddled with problems, including technical glitches and facial recognition algorithms that are unable to recognize darker-skinned people. This is compounded by the fact that not every asylum seeker has a smartphone. 

Then, just after crossing into the United States, migrants must submit to collection of biometric data, including DNA. For a sense of scale, a recent report from Georgetown Law School’s Center on Privacy and Technology found that CBP has added 1.5 million DNA profiles, primarily from migrants crossing the border, to law enforcement databases since it began collecting DNA “from any person in CBP custody subject to fingerprinting” in January 2020. The researchers noted that an overrepresentation of immigrants—the majority of whom are people of color—in a DNA database used by law enforcement could subject them to over-policing and lead to other forms of bias. 

Generally, these programs only require information from individuals aged 14 to 79. DHS attempted to change this back in 2020, with proposed rules for USCIS and CBP that would have expanded biometric data collection dramatically, including by age. (USCIS’s proposed rule would have doubled the number of people from whom biometric data would be required, including any US citizen who sponsors an immigrant.) But the USCIS rule was withdrawn in the wake of the Biden administration’s new “priorities to reduce barriers and undue burdens in the immigration system.” Meanwhile, for reasons that remain unclear, the proposed CBP rule was never enacted. 

This would make it appear “contradictory” if DHS were now collecting the biometric data of children under 14, says Dinesh McCoy, a staff attorney with Just Futures Law, an immigrant rights group that tracks surveillance technologies. 

Neither Boyd nor DHS’s media office would confirm which specific policy changes he was referring to in his presentation, though MIT Technology Review has identified a 2017 memo, issued by then-Secretary of Homeland Security John F. Kelly, that encouraged DHS components to remove “age as a basis for determining when to collect biometrics.” 

The DHS’s Office of the Inspector General (OIG) referred to this memo as the “overarching policy for biometrics at DHS” in a September 2023 report, though none of the press offices MIT Technology Review contacted—including the main DHS press office, OIG, and OBIM, among others—would confirm whether this was still the relevant policy; we have not been able to confirm any related policy changes since then. 

The OIG audit also found a number of fundamental issues related to DHS’s oversight of biometric data collection and use—including that its 10-year strategic framework for biometrics, covering 2015 to 2025, “did not accurately reflect the current state of biometrics across the Department, such as the use of facial recognition verification and identification.” Nor did it provide clear guidance for the consistent collection and use of biometrics across DHS, including age requirements. 

But there is also another potential explanation for the new OBIM program: Boyd says it is being conducted under the auspices of the DHS’s undersecretary of science and technology, the office that leads much of the agency’s research efforts. Because it is for research, rather than to be used “in DHS operations to inform processes or decision making,” many of the standard restrictions for DHS use of face recognition and face capture technologies do not apply, according to a DHS directive

Do you have any additional information on DHS’s craniofacial structural progression initiative? Please reach out with a non-work email to tips@technologyreview.com or securely on Signal at 626.765.5489. 

Some lawyers allege that changing the age limit for data collection via department policy, not by a federal rule, which requires a public comment period, is problematic. McCoy, for instance, says any lack of transparency here amplifies the already “extremely challenging” task of “finding [out] in a systematic way how these technologies are deployed”—even though that is key for accountability.

Advancing the field

At the identity forum and in a subsequent conversation, Boyd explained that this data collection is meant to advance the development of effective FRT algorithms. Boyd leads OBIM’s Future Identity team, whose mission is to “research, review, assess, and develop technology, policy, and human factors that enable rapid, accurate, and secure identity services” and to make OBIM “the preferred provider for identity services within DHS.” 

Driven by high-profile cases of missing children, there has long been interest in understanding how children’s faces age. At the same time, there have been technical challenges to doing so, both preceding FRT and with it. 

At its core, facial recognition identifies individuals by comparing the geometry of various facial features in an original face print with subsequent images. Based on this comparison, a facial recognition algorithm assigns a percentage likelihood that there is a match. 

But as children grow and develop, their bone structure changes significantly, making it difficult for facial recognition algorithms to identify them over time. (These changes tend to be even more pronounced  in children under 14. In contrast, as adults age, the changes tend to be in the skin and muscle, and have less variation overall.) More data would help solve this problem, but there is a dearth of high-quality data sets of children’s faces with verifiable ages. 

“What we’re trying to do is to get large data sets of known individuals,” Boyd tells MIT Technology Review. That means taking high-quality face prints “under controlled conditions where we know we’ve got the person with the right name [and] the correct birth date”—or, in other words, where they can be certain about the “provenance of the data.” 

For example, one data set used for aging research consists of 305 celebrities’ faces as they aged from five to 32. But these photos, scraped from the internet, contain too many other variables—such as differing image qualities, lighting conditions, and distances at which they were taken—to be truly useful. Plus, speaking to the provenance issue that Boyd highlights, their actual ages in each photo can only be estimated. 

Another tactic is to use data sets of adult faces that have been synthetically de-aged. Synthetic data is considered more privacy-preserving, but it too has limitations, says Stephanie Schuckers, director of the Center for Identification Technology Research (CITeR). “You can test things with only the generated data,” Schuckers explains, but the question remains: “Would you get similar results to the real data?”

(Hosted at Clarkson University in New York, CITeR brings together a network of academic and government affiliates working on identity technologies. OBIM is a member of the research consortium.) 

Schuckers’s team at CITeR has taken another approach: an ongoing longitudinal study of a cohort of 231 elementary and middle school students from the area around Clarkson University. Since 2016, the team has captured biometric data every six months (save for two years of the covid-19 pandemic), including facial images. They have found that the open-source face recognition models they tested can in fact successfully recognize children three to four years after they were initially enrolled. 

But the conditions of this study aren’t easily replicable at scale. The study images are taken in a controlled environment, all the participants are volunteers, the researchers sought consent from parents and the subjects themselves, and the research was approved by the university’s Institutional Review Board. Schuckers’s research also promises to protect privacy by requiring other researchers to request access, and by providing facial datasets separately from other data that have been collected. 

What’s more, this research still has technical limitations, including that the sample is small, and it is overwhelmingly Caucasian, meaning it might be less accurate when applied to other races. 

Schuckers says she was unaware of DHS’s craniofacial structural progression initiative. 

Far-reaching implications

Boyd says OBIM takes privacy considerations seriously, and that “we don’t share … data with commercial industries.” Still, OBIM has 144 government partners with which it does share information, and it has been criticized by the Government Accountability Office for poorly documenting who it shares information with, and with what privacy-protecting measures. 

Even if the data does stay within the federal government, OBIM’s findings regarding the accuracy of FRT for children over time could nevertheless influence how—and when—the rest of the government collects biometric data, as well as whether the broader facial recognition industry may also market its services for children. (Indeed, Boyd says sharing “results,” or the findings of how accurate FRT algorithms are, is different than sharing the data itself.) 

That this technology is being tested on people who are offered fewer privacy protections than would be afforded to US citizens is just part of the wider trend of using people from the developing world, whether they are migrants coming to the border or civilians in war zones, to help improve new technologies. 

In fact, Boyd previously helped advance the Department of Defense’s biometric systems in Iraq and Afghanistan, where he acknowledged that individuals lacked the privacy protections that would have been granted in many other contexts, despite the incredibly high stakes. Biometric data collected in those war zones—in some areas, from every fighting-age male—was used to identify and target insurgents, and being misidentified could mean death. 

These projects subsequently played a substantial role in influencing the expansion of biometric data collection by the Department of Defense, which now happens globally. And architects of the program, like Boyd, have taken important roles in expanding the use of biometrics at other agencies. 

“It’s not an accident” that this testing happens in the context of border zones, says Molnar. Borders are “the perfect laboratory for tech experimentation, because oversight is weak, discretion is baked into the decisions that get made … it allows the state to experiment in ways that it wouldn’t be allowed to in other spaces.” 

But, she notes, “just because it happens at the border doesn’t mean that that’s where it’s going to stay.”

Update: This story was updated to include comment from DHS.

Do you have any additional information on DHS’s craniofacial structural progression initiative? Please reach out with a non-work email to tips@technologyreview.com or securely on Signal at 626.765.5489.