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This is the real story of the Afghan biometric databases abandoned to the Taliban

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This is the real story of the Afghan biometric databases abandoned to the Taliban


According to Jacobsen’s book, AABIS aimed to cover 80% of the Afghan population by 2012, or roughly 25 million people. While there is no publicly available information on just how many records this database now contains, and neither the contractor managing the database nor officials from the US Defense Department have responded to requests for comment, one unconfirmed figure from the LinkedIn profile of its US-based program manager puts it at 8.1 million records. 

AABIS was widely used in a variety of ways by the previous Afghan government. Applications for government jobs and roles at most projects required a biometric check from the MOI system to ensure that applicants had no criminal or terrorist background. Biometric checks were also required for passport, national ID, and driver’s license applications, as well as registrations for the country’s college entrance exam. 

Another database, slightly smaller than AABIS, was connected to the “e-tazkira,” the country’s electronic national ID card. By the time the government fell, it had roughly 6.2 million applications in process, according to the National Statistics and Information Authority, though it is unclear how many applicants had already submitted biometric data. 

Biometrics were also used—or at least publicized—by other government departments as well. The Independent Election Commission used biometric scanners in an attempt to prevent voter fraud during the 2019 parliamentary elections, with questionable results. In 2020, the Ministry of Commerce and Industries announced that it would collect biometrics from those who were registering new businesses. 

Despite the plethora of systems, they were never fully connected to each other. An August 2019 audit by the US found that despite the $38 million spent to date, APPS had not met many of its aims: biometrics still weren’t integrated directly into its personnel files, but were just linked by the unique biometric number. Nor did the system connect directly to other Afghan government computer systems, like that of the Ministry of Finance, which sent out the salaries. APPS also still relied on manual data-entry processes, said the audit, which allowed room for human error or manipulation.

A global issue

Afghanistan is not the only country to embrace biometrics. Many countries are concerned about so-called “ghost beneficiaries”—fake identities that are used to illegally collect salaries or other funds. Preventing such fraud is a common justification for biometric systems, says Amba Kak, the director of global policy and programs at the AI Now institute and a legal expert on biometric systems.

“It’s really easy to paint this [APPS] as exceptional,” says Kak, who co-edited a book on global biometric policies. It “seems to have a lot of continuity with global experiences” around biometrics.

“Biometric ID as the only efficient means for legal identification is … flawed and a little dangerous.”

Amber Kak, AI Now

It’s widely recognized that having legal identification documents is a right, but “conflating biometric ID as the only efficient means for legal identification,” she says, is “flawed and a little dangerous.” 

Kak questions whether biometrics—rather than policy fixes—are the right solution to fraud, and adds that they are often “not evidence-based.” 

But driven largely by US military objectives and international funding, Afghanistan’s rollout of such technologies has been aggressive. Even if APPS and other databases had not yet achieved the level of function they were intended to, they still contain many terabytes of data on Afghan citizens that the Taliban can mine. 

“Identity dominance”—but by whom? 

The growing alarm over the biometric devices and databases left behind, and the reams of other data about ordinary life in Afghanistan, has not stopped the collection of people’s sensitive data in the two weeks between the Taliban’s entry into Kabul and the official withdrawal of American forces. 

This time, the data is being collected mostly by well-intentioned volunteers in unsecured Google forms and spreadsheets, highlighting either that the lessons on data security have not yet been learned—or that they must be relearned by every group involved. 

Singh says the issue of what happens to data during conflicts or governmental collapse needs to be given more attention. “We don’t take it seriously,” he says, “But we should, especially in these war-torn areas where information can be used to create a lot of havoc.”

Kak, the biometrics law researcher, suggests that perhaps the best way to protect sensitive data would be if “these kinds of [data] infrastructures … weren’t built in the first place.”

For Jacobsen, the author and journalist, it is ironic that the Department of Defense’s obsession with using data to establish identity might actually help the Taliban achieve its own version of identity dominance. “That would be the fear of what the Taliban is doing,” she says. 

Ultimately, some experts say the fact that Afghan government databases were not very interoperable may actually be a saving grace if the Taliban do try to use the data. “I suspect that the APPS still doesn’t work that well, which is probably a good thing in light of recent events,” said Dan Grazier, a veteran who works at watchdog group the Project on Government Oversight, by email. 

But for those connected to the APPS database, who may now find themselves or their family members hunted by the Taliban, it’s less irony and more betrayal. 

“The Afghan military trusted their international partners, including and led by the US, to build a system like this,” says one of the individuals familiar with the system. “And now that database is going to be used as the [new] government’s weapon.”

This article has been updated with comment from the Department of Defense.

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Facebook wants machines to see the world through our eyes

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Facebook wants machines to see the world through our eyes


For the last two years, Facebook AI Research (FAIR) has worked with 13 universities around the world to assemble the largest ever data set of first-person video—specifically to train deep-learning image-recognition models. AIs trained on the data set will be better at controlling robots that interact with people, or interpreting images from smart glasses. “Machines will be able to help us in our daily lives only if they really understand the world through our eyes,” says Kristen Grauman at FAIR, who leads the project.

Such tech could support people who need assistance around the home, or guide people in tasks they are learning to complete. “The video in this data set is much closer to how humans observe the world,” says Michael Ryoo, a computer vision researcher at Google Brain and Stony Brook University in New York, who is not involved in Ego4D.

But the potential misuses are clear and worrying. The research is funded by Facebook, a social media giant that has recently been accused in the US Senate of putting profits over people’s well-being—as corroborated by MIT Technology Review’s own investigations.

The business model of Facebook, and other Big Tech companies, is to wring as much data as possible from people’s online behavior and sell it to advertisers. The AI outlined in the project could extend that reach to people’s everyday offline behavior, revealing what objects are around your home, what activities you enjoyed, who you spent time with, and even where your gaze lingered—an unprecedented degree of personal information.

“There’s work on privacy that needs to be done as you take this out of the world of exploratory research and into something that’s a product,” says Grauman. “That work could even be inspired by this project.”

FACEBOOK

The biggest previous data set of first-person video consists of 100 hours of footage of people in the kitchen. The Ego4D data set consists of 3,025 hours of video recorded by 855 people in 73 different locations across nine countries (US, UK, India, Japan, Italy, Singapore, Saudi Arabia, Colombia, and Rwanda).

The participants had different ages and backgrounds; some were recruited for their visually interesting occupations, such as bakers, mechanics, carpenters, and landscapers.

Previous data sets typically consisted of semi-scripted video clips only a few seconds long. For Ego4D, participants wore head-mounted cameras for up to 10 hours at a time and captured first-person video of unscripted daily activities, including walking along a street, reading, doing laundry, shopping, playing with pets, playing board games, and interacting with other people. Some of the footage also includes audio, data about where the participants’ gaze was focused, and multiple perspectives on the same scene. It’s the first data set of its kind, says Ryoo.

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This NASA spacecraft is on its way to Jupiter’s mysterious asteroid swarms

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This NASA spacecraft is on its way to Jupiter’s mysterious asteroid swarms


Lucy will take black-and-white and color images, and use a diamond beam splitter to shine far-infrared light at the asteroids to take their temperature and make maps of their surface. It will also collect other measurements as it flies by. This data could help scientists understand how the planets may have formed.

Sarah Dodson-Robinson, an assistant professor of physics and astronomy at the University of Delaware, says Lucy could offer a definitive time line for not only when the planets originally formed, but where.

“If you can nail down when the Trojan asteroids formed, then you have some information about when did Jupiter form, and can start asking questions like ‘Where did Jupiter go in the solar system?’” she says. “Because it wasn’t always where it is now. It’s moved around.”

And to determine the asteroids’ ages, the spacecraft will search for surface craters that may be no bigger than a football field. 

“[The Trojans] haven’t had nearly as much colliding and breaking as some of the other asteroids that are nearer to us,” says Dodson-Robinson. “We’re potentially getting a look at some of these asteroids like they were shortly after they formed.”

On its 4-billion-mile journey, Lucy will receive three gravity assists from Earth, which will involve using the planet’s gravitational force to change the spacecraft’s trajectory without depleting its resources. Coralie Adam, deputy navigation team chief for the Lucy mission, says each push will increase the spacecraft’s velocity from 200 miles per hour to over 11,000 mph.

“If not for this Earth gravity assist, it would take five times the amount of fuel—or three metric tons—to reach Lucy’s target, which would make the mission unfeasible,” said Adam during an engineering media briefing also held on October 14.

Lucy’s mission is slated to end in 2033, but some NASA officials already feel confident that the spacecraft will last far longer. “There will be a good amount of fuel left onboard,” said Adam. “After the final encounter with the binary asteroids, as long as the spacecraft is healthy, we plan to propose to NASA to do an extended mission and explore more Trojans.”

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Reimagining our pandemic problems with the mindset of an engineer

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Reimagining our pandemic problems with the mindset of an engineer


The last 20 months turned every dog into an amateur epidemiologist and statistician. Meanwhile, a group of bona fide epidemiologists and statisticians came to believe that pandemic problems might be more effectively solved by adopting the mindset of an engineer: that is, focusing on pragmatic problem-solving with an iterative, adaptive strategy to make things work.

In a recent essay, “Accounting for uncertainty during a pandemic,” the researchers reflect on their roles during a public health emergency and on how they could be better prepared for the next crisis. The answer, they write, may lie in reimagining epidemiology with more of an engineering perspective and less of a “pure science” perspective.

Epidemiological research informs public health policy and its inherently applied mandate for prevention and protection. But the right balance between pure research results and pragmatic solutions proved alarmingly elusive during the pandemic.

We have to make practical decisions, so how much does the uncertainty really matter?

Seth Guikema

“I always imagined that in this kind of emergency, epidemiologists would be useful people,” Jon Zelner, a coauthor of the essay, says. “But our role has been more complex and more poorly defined than I had expected at the outset of the pandemic.” An infectious disease modeler and social epidemiologist at the University of Michigan, Zelner witnessed an “insane proliferation” of research papers, “many with very little thought about what any of it really meant in terms of having a positive impact.”

“There were a number of missed opportunities,” Zelner says—caused by missing links between the ideas and tools epidemiologists proposed and the world they were meant to help.

Giving up on certainty

Coauthor Andrew Gelman, a statistician and political scientist at Columbia University, set out “the bigger picture” in the essay’s introduction. He likened the pandemic’s outbreak of amateur epidemiologists to the way war makes every citizen into an amateur geographer and tactician: “Instead of maps with colored pins, we have charts of exposure and death counts; people on the street argue about infection fatality rates and herd immunity the way they might have debated wartime strategies and alliances in the past.”

And along with all the data and public discourse—Are masks still necessary? How long will vaccine protection last?—came the barrage of uncertainty.

In trying to understand what just happened and what went wrong, the researchers (who also included Ruth Etzioni at the University of Washington and Julien Riou at the University of Bern) conducted something of a reenactment. They examined the tools used to tackle challenges such as estimating the rate of transmission from person to person and the number of cases circulating in a population at any given time. They assessed everything from data collection (the quality of data and its interpretation were arguably the biggest challenges of the pandemic) to model design to statistical analysis, as well as communication, decision-making, and trust. “Uncertainty is present at each step,” they wrote.

And yet, Gelman says, the analysis still “doesn’t quite express enough of the confusion I went through during those early months.”

One tactic against all the uncertainty is statistics. Gelman thinks of statistics as “mathematical engineering”—methods and tools that are as much about measurement as discovery. The statistical sciences attempt to illuminate what’s going on in the world, with a spotlight on variation and uncertainty. When new evidence arrives, it should generate an iterative process that gradually refines previous knowledge and hones certainty.

Good science is humble and capable of refining itself in the face of uncertainty.

Marc Lipsitch

Susan Holmes, a statistician at Stanford who was not involved in this research, also sees parallels with the engineering mindset. “An engineer is always updating their picture,” she says—revising as new data and tools become available. In tackling a problem, an engineer offers a first-order approximation (blurry), then a second-order approximation (more focused), and so on.

Gelman, however, has previously warned that statistical science can be deployed as a machine for “laundering uncertainty”—deliberately or not, crappy (uncertain) data are rolled together and made to seem convincing (certain). Statistics wielded against uncertainties “are all too often sold as a sort of alchemy that will transform these uncertainties into certainty.”

We witnessed this during the pandemic. Drowning in upheaval and unknowns, epidemiologists and statisticians—amateur and expert alike—grasped for something solid as they tried to stay afloat. But as Gelman points out, wanting certainty during a pandemic is inappropriate and unrealistic. “Premature certainty has been part of the challenge of decisions in the pandemic,” he says. “This jumping around between uncertainty and certainty has caused a lot of problems.”

Letting go of the desire for certainty can be liberating, he says. And this, in part, is where the engineering perspective comes in.

A tinkering mindset

For Seth Guikema, co-director of the Center for Risk Analysis and Informed Decision Engineering at the University of Michigan (and a collaborator of Zelner’s on other projects), a key aspect of the engineering approach is diving into the uncertainty, analyzing the mess, and then taking a step back, with the perspective “We have to make practical decisions, so how much does the uncertainty really matter?” Because if there’s a lot of uncertainty—and if the uncertainty changes what the optimal decisions are, or even what the good decisions are—then that’s important to know, says Guikema. “But if it doesn’t really affect what my best decisions are, then it’s less critical.”

For instance, increasing SARS-CoV-2 vaccination coverage across the population is one scenario in which even if there is some uncertainty regarding exactly how many cases or deaths vaccination will prevent, the fact that it is highly likely to decrease both, with few adverse effects, is motivation enough to decide that a large-scale vaccination program is a good idea.

An engineer is always updating their picture.

Susan Holmes

Engineers, Holmes points out, are also very good at breaking problems down into critical pieces, applying carefully selected tools, and optimizing for solutions under constraints. With a team of engineers building a bridge, there is a specialist in cement and a specialist in steel, a wind engineer and a structural engineer. “All the different specialties work together,” she says.

For Zelner, the notion of epidemiology as an engineering discipline is something he  picked up from his father, a mechanical engineer who started his own company designing health-care facilities. Drawing on a childhood full of building and fixing things, his engineering mindset involves tinkering—refining a transmission model, for instance, in response to a moving target.

“Often these problems require iterative solutions, where you’re making changes in response to what does or doesn’t work,” he says. “You continue to update what you’re doing as more data comes in and you see the successes and failures of your approach. To me, that’s very different—and better suited to the complex, non-stationary problems that define public health—than the kind of static one-and-done image a lot of people have of academic science, where you have a big idea, test it, and your result is preserved in amber for all time.” 

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