Can AI help create “smart borders” between countries?
In 2016, border patrols in Greece, Latvia and Hungary received a prototype for an AI-powered lie detector to help screen asylum seekers. The detector, called iBorderCtrl, was funded by the European Commission in hopes to eventually mitigate refugee crises like the one sparked by the Syrian civil war a year prior.
iBorderCtrl, which analyzes micro expressions in the face, received but one slice of the Commission’s €34.9 billion border control and migration management budget. Still in development is the more ambitious EuMigraTool, a predictive AI system that will process internet news and social media posts to estimate not only the number of migrants heading for a particular country, but also the “risks of tensions between migrants and EU citizens.”
Both iBorderCtrl and EuMigraTool are part of a broader trend: the growing digitization of migration-related technologies. Outside of the EU, in refugee camps in Jordan, the United Nations introduced iris scanning software to distribute humanitarian aid, including food and medicine. And in the United States, Customs and Border Protection has attempted to automate its services through an app called CBP One, which both travelers and asylum seekers can use to apply for I-94 forms, the arrival-departure record cards for people who are not U.S. citizens or permanent residents.
According to Koen Leurs, professor of gender, media and migration studies at Utrecht University in the Netherlands, we have arrived at a point where migration management has become so reliant on digital technology that the former can no longer be studied in isolation from the latter. Investigating this reliance for his new book, Digital Migration, Leurs came to the conclusion that applications like those mentioned above are more often than not a double-edged sword, presenting both benefits and drawbacks.
There has been “a huge acceleration” in the way digital technologies “dehumanize people,” says Koen Leurs, professor of gender, media and migration studies at Utrecht University in the Netherlands. Governments treat asylum seekers as test subjects for new inventions, all along the borders of the developed world.
On the one hand, digital technology can make migration management more efficient and less labor intensive, enabling countries to process larger numbers of people in a time when global movement is on the rise due to globalization and political instability. Leurs also discovered that informal knowledge networks such as Informed Immigrant, an online resource that connects migrants to social workers and community organizers, have positively impacted the lives of their users. The same, Leurs notes, is true of platforms like Twitter, Facebook, and WhatsApp, all of which migrants use to stay in touch with each other as well as their families back home. “The emotional support you receive through social media is something we all came to appreciate during the COVID pandemic,” Leurs says. “For refugees, this had already been common knowledge for years.”
On the flipside, automatization of migration management – particularly through the use of AI – has spawned extensive criticism from human rights activists. Sharing their sentiment, Leurs attests that many so-called innovations are making life harder for migrants, not easier. He also says there has been “a huge acceleration” in the way digital technologies “dehumanize people,” and that governments treat asylum seekers as test subjects for new inventions, all along the borders of the developed world.
In Jordan, for example, refugees had to scan their irises in order to collect aid, prompting the question of whether such measures are ethical. Speaking to Reuters, Petra Molnar, a fellow at Harvard University’s Berkman Klein Center for Internet and Society, said that she was troubled by the fact that this experiment was done on marginalized people. “The refugees are guinea pigs,” she said. “Imagine what would happen at your local grocery store if all of a sudden iris scanning became a thing,” she pointed out. “People would be up in arms. But somehow it is OK to do it in a refugee camp.”
Artificial intelligence programs have been scrutinized for their unreliability, their complex processing, thwarted by the race and gender biases picked up from training data. In 2019, a female reporter from The Intercept tested iBorderCtrl and, despite answering all questions truthfully, was accused by the machine of lying four out of 16 times. Had she been waiting at checkpoint on the Greek or Latvian border, she would have been flagged for additional screening – a measure that could jeopardize her chance of entry. Because of its biases, and the negative press that this attracted, iBorderCtrl did not move past its test phase.
While facial recognition caused problems on the European border, it was helpful in Ukraine, where programs like those developed by software company Clearview AI are used to spot Russian spies, identify dead soldiers, and check movement in and out of war zones.
In April 2021, not long after iBorderCtrl was shut down, the European Commission proposed the world’s first-ever legal framework for AI regulation: the Artificial Intelligence Act. The act, which is still being developed, promises to prevent potentially “harmful” AI practices from being used in migration management. In the most recent draft, approved by the European Parliament’s Liberties and Internal Market committees, the ban included emotion recognition systems (like iBorderCtrl), predictive policing systems (like EUMigraTool), and biometric categorization systems (like iris scanners). The act also stipulates that AI must be subject to strict oversight and accountability measures.
While some worry the AI Act is not comprehensive enough, others wonder if it is in fact going too far. Indeed, many proponents of machine learning argue that, by placing a categorical ban on certain systems, governments will thwart the development of potentially useful technology. While facial recognition caused problems on the European border, it was helpful in Ukraine, where programs like those developed by software company Clearview AI are used to spot Russian spies, identify dead soldiers, and check movement in and out of war zones.
Instead of flat-out banning AI, why not strive to make it more reliable? “One of the most compelling arguments against AI is that it is inherently biased,” says Vera Raposo, an assistant professor of law at NOVA University in Lisbon specializing in digital law. “In truth, AI itself is not biased; it becomes biased due to human influence. It seems that complete eradication of biases is unattainable, but mitigation is possible. We can strive to reduce biases by employing more comprehensive and unbiased data in AI training and encompassing a wider range of individuals. We can also work on developing less biased algorithms, although this is challenging given that coders, being human, inherently possess biases of their own.”
AI is most effective when it enhances human performance rather than replacing it.
Accessibility is another obstacle that needs to be overcome. Leurs points out that, in migration management, AI often functions as a “black box” because the migration officers operating it are unable to comprehend its complex decision-making process and thus unable to scrutinize its results. One solution to this problem is to have law enforcement work closely with AI experts. Alternatively, machine learning could be limited to gathering and summarizing information, leaving evaluation of that information to actual people.
Raposo agrees AI is most effective when it enhances human performance rather than replacing it. On the topic of transparency, she does note that making an AI that is both sophisticated and easy to understand is a little bit like having your cake and eating it too. “In numerous domains,” she explains, “we might need to accept a reduced level of explainability in exchange for a high degree of accuracy (assuming we cannot have both).” Using healthcare as an analogy, she adds that “some medications work in ways not fully understood by either doctors or pharma companies, yet persist due to demonstrated efficacy in clinical trials.”
Leurs believes digital technologies used in migration management can be improved through a push for more conscientious research. “Technology is a poison and a medicine for that poison,” he argues, which is why new tech should be developed with its potential applications in mind. “Ethics has become a major concern in recent years. Increasingly, and particularly in the study of forced migration, researchers are posing critical questions like ‘what happens with the data that is gathered?’ and ‘who will this harm?’” In some cases, Leurs thinks, that last question may need to be reversed: we should be thinking about how we can actively disarm oppressive structures. “After all, our work should align with the interests of the communities it is going to affect.”
DNA- and RNA-based electronic implants may revolutionize healthcare
Implantable electronic devices can significantly improve patients’ quality of life. A pacemaker can encourage the heart to beat more regularly. A neural implant, usually placed at the back of the skull, can help brain function and encourage higher neural activity. Current research on neural implants finds them helpful to patients with Parkinson’s disease, vision loss, hearing loss, and other nerve damage problems. Several of these implants, such as Elon Musk’s Neuralink, have already been approved by the FDA for human use.
Yet, pacemakers, neural implants, and other such electronic devices are not without problems. They require constant electricity, limited through batteries that need replacements. They also cause scarring. “The problem with doing this with electronics is that scar tissue forms,” explains Kate Adamala, an assistant professor of cell biology at the University of Minnesota Twin Cities. “Anytime you have something hard interacting with something soft [like muscle, skin, or tissue], the soft thing will scar. That's why there are no long-term neural implants right now.” To overcome these challenges, scientists are turning to biocomputing processes that use organic materials like DNA and RNA. Other promised benefits include “diagnostics and possibly therapeutic action, operating as nanorobots in living organisms,” writes Evgeny Katz, a professor of bioelectronics at Clarkson University, in his book DNA- And RNA-Based Computing Systems.
While a computer gives these inputs in binary code or "bits," such as a 0 or 1, biocomputing uses DNA strands as inputs, whether double or single-stranded, and often uses fluorescent RNA as an output.
Adamala’s research focuses on developing such biocomputing systems using DNA, RNA, proteins, and lipids. Using these molecules in the biocomputing systems allows the latter to be biocompatible with the human body, resulting in a natural healing process. In a recent Nature Communications study, Adamala and her team created a new biocomputing platform called TRUMPET (Transcriptional RNA Universal Multi-Purpose GatE PlaTform) which acts like a DNA-powered computer chip. “These biological systems can heal if you design them correctly,” adds Adamala. “So you can imagine a computer that will eventually heal itself.”
The basics of biocomputing
Biocomputing and regular computing have many similarities. Like regular computing, biocomputing works by running information through a series of gates, usually logic gates. A logic gate works as a fork in the road for an electronic circuit. The input will travel one way or another, giving two different outputs. An example logic gate is the AND gate, which has two inputs (A and B) and two different results. If both A and B are 1, the AND gate output will be 1. If only A is 1 and B is 0, the output will be 0 and vice versa. If both A and B are 0, the result will be 0. While a computer gives these inputs in binary code or "bits," such as a 0 or 1, biocomputing uses DNA strands as inputs, whether double or single-stranded, and often uses fluorescent RNA as an output. In this case, the DNA enters the logic gate as a single or double strand.
If the DNA is double-stranded, the system “digests” the DNA or destroys it, which results in non-fluorescence or “0” output. Conversely, if the DNA is single-stranded, it won’t be digested and instead will be copied by several enzymes in the biocomputing system, resulting in fluorescent RNA or a “1” output. And the output for this type of binary system can be expanded beyond fluorescence or not. For example, a “1” output might be the production of the enzyme insulin, while a “0” may be that no insulin is produced. “This kind of synergy between biology and computation is the essence of biocomputing,” says Stephanie Forrest, a professor and the director of the Biodesign Center for Biocomputing, Security and Society at Arizona State University.
Biocomputing circles are made of DNA, RNA, proteins and even bacteria.
Evgeny Katz
The TRUMPET’s promise
Depending on whether the biocomputing system is placed directly inside a cell within the human body, or run in a test-tube, different environmental factors play a role. When an output is produced inside a cell, the cell's natural processes can amplify this output (for example, a specific protein or DNA strand), creating a solid signal. However, these cells can also be very leaky. “You want the cells to do the thing you ask them to do before they finish whatever their businesses, which is to grow, replicate, metabolize,” Adamala explains. “However, often the gate may be triggered without the right inputs, creating a false positive signal. So that's why natural logic gates are often leaky." While biocomputing outside a cell in a test tube can allow for tighter control over the logic gates, the outputs or signals cannot be amplified by a cell and are less potent.
TRUMPET, which is smaller than a cell, taps into both cellular and non-cellular biocomputing benefits. “At its core, it is a nonliving logic gate system,” Adamala states, “It's a DNA-based logic gate system. But because we use enzymes, and the readout is enzymatic [where an enzyme replicates the fluorescent RNA], we end up with signal amplification." This readout means that the output from the TRUMPET system, a fluorescent RNA strand, can be replicated by nearby enzymes in the platform, making the light signal stronger. "So it combines the best of both worlds,” Adamala adds.
These organic-based systems could detect cancer cells or low insulin levels inside a patient’s body.
The TRUMPET biocomputing process is relatively straightforward. “If the DNA [input] shows up as single-stranded, it will not be digested [by the logic gate], and you get this nice fluorescent output as the RNA is made from the single-stranded DNA, and that's a 1,” Adamala explains. "And if the DNA input is double-stranded, it gets digested by the enzymes in the logic gate, and there is no RNA created from the DNA, so there is no fluorescence, and the output is 0." On the story's leading image above, if the tube is "lit" with a purple color, that is a binary 1 signal for computing. If it's "off" it is a 0.
While still in research, TRUMPET and other biocomputing systems promise significant benefits to personalized healthcare and medicine. These organic-based systems could detect cancer cells or low insulin levels inside a patient’s body. The study’s lead author and graduate student Judee Sharon is already beginning to research TRUMPET's ability for earlier cancer diagnoses. Because the inputs for TRUMPET are single or double-stranded DNA, any mutated or cancerous DNA could theoretically be detected from the platform through the biocomputing process. Theoretically, devices like TRUMPET could be used to detect cancer and other diseases earlier.
Adamala sees TRUMPET not only as a detection system but also as a potential cancer drug delivery system. “Ideally, you would like the drug only to turn on when it senses the presence of a cancer cell. And that's how we use the logic gates, which work in response to inputs like cancerous DNA. Then the output can be the production of a small molecule or the release of a small molecule that can then go and kill what needs killing, in this case, a cancer cell. So we would like to develop applications that use this technology to control the logic gate response of a drug’s delivery to a cell.”
Although platforms like TRUMPET are making progress, a lot more work must be done before they can be used commercially. “The process of translating mechanisms and architecture from biology to computing and vice versa is still an art rather than a science,” says Forrest. “It requires deep computer science and biology knowledge,” she adds. “Some people have compared interdisciplinary science to fusion restaurants—not all combinations are successful, but when they are, the results are remarkable.”
In today’s podcast episode, Leaps.org Deputy Editor Lina Zeldovich speaks about the health and ecological benefits of farming crickets for human consumption with Bicky Nguyen, who joins Lina from Vietnam. Bicky and her business partner Nam Dang operate an insect farm named CricketOne. Motivated by the idea of sustainable and healthy protein production, they started their unconventional endeavor a few years ago, despite numerous naysayers who didn’t believe that humans would ever consider munching on bugs.
Yet, making creepy crawlers part of our diet offers many health and planetary advantages. Food production needs to match the rise in global population, estimated to reach 10 billion by 2050. One challenge is that some of our current practices are inefficient, polluting and wasteful. According to nonprofit EarthSave.org, it takes 2,500 gallons of water, 12 pounds of grain, 35 pounds of topsoil and the energy equivalent of one gallon of gasoline to produce one pound of feedlot beef, although exact statistics vary between sources.
Meanwhile, insects are easy to grow, high on protein and low on fat. When roasted with salt, they make crunchy snacks. When chopped up, they transform into delicious pâtes, says Bicky, who invents her own cricket recipes and serves them at industry and public events. Maybe that’s why some research predicts that edible insects market may grow to almost $10 billion by 2030. Tune in for a delectable chat on this alternative and sustainable protein.
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Further reading:
More info on Bicky Nguyen
https://yseali.fulbright.edu.vn/en/faculty/bicky-n...
The environmental footprint of beef production
https://www.earthsave.org/environment.htm
https://www.watercalculator.org/news/articles/beef-king-big-water-footprints/
https://www.frontiersin.org/articles/10.3389/fsufs.2019.00005/full
https://ourworldindata.org/carbon-footprint-food-methane
Insect farming as a source of sustainable protein
https://www.insectgourmet.com/insect-farming-growing-bugs-for-protein/
https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/insect-farming
Cricket flour is taking the world by storm
https://www.cricketflours.com/
https://talk-commerce.com/blog/what-brands-use-cricket-flour-and-why/
Lina Zeldovich has written about science, medicine and technology for Popular Science, Smithsonian, National Geographic, Scientific American, Reader’s Digest, the New York Times and other major national and international publications. A Columbia J-School alumna, she has won several awards for her stories, including the ASJA Crisis Coverage Award for Covid reporting, and has been a contributing editor at Nautilus Magazine. In 2021, Zeldovich released her first book, The Other Dark Matter, published by the University of Chicago Press, about the science and business of turning waste into wealth and health. You can find her on http://linazeldovich.com/ and @linazeldovich.