Another visa holder was denied entry to the US – gpgmail


It has been one week since U.S. border officials denied entry to a 17-year-old Harvard freshman just days before classes were set to begin.

Ismail Ajjawi, a Palestinian student living in Lebanon, had his student visa canceled and was put on a flight home shortly after arriving at Boston Logan International Airport. Customs & Border Protection officers searched his phone and decided he was ineligible for entry because of his friends’ social media posts. Ajjawi told the officers he “should not be held responsible” for others’ posts, but it was not enough for him to clear the border.

The news prompted outcry and fury. But gpgmail has learned it was not an isolated case.

Since our story broke, we came across another case of a U.S. visa holder who was denied entry to the country on grounds that he was sent a graphic WhatsApp message. Dakhil — whose name we have changed to protect his identity — was detained for hours, but subsequently had his visa canceled. He was sent back to Pakistan and banned from entering the U.S. for five years.

Since 2015, the number of device searches has increased four-fold to over 30,200 each year. Lawmakers have accused the CBP of conducting itself unlawfully by searching devices without a warrant, but CBP says it does not need to obtain a warrant for device searches at the border. Several courts have tried to tackle the question of whether or not device searches are constitutional.

Abed Ayoub, legal and policy director at the American-Arab Anti-Discrimination Committee, told gpgmail that device searches and subsequent denials of entry had become the “new normal.”

This is Dakhil’s story.

* * *

As a a Pakistani national, Dakhil needed a visa to enter the U.S. He obtained a B1/B2 visa, which allowed him to temporarily enter the U.S. for work and to visit family. Months later, he arrived at George Bush Intercontinental Airport in Houston, Texas, tired but excited to see his cousin for the first time in years.

It didn’t take long before Dakhil realized something wasn’t right.

Dakhil, who had never traveled to the U.S. before, was waiting in the immigration line at the border when a CBP officer approached him to ask why he had traveled to the U.S. He said it was for a vacation to visit his family. The officer took his passport and, after a brief examination of its stamps, asked why Dakhil had visited Saudi Arabia. It was for Hajj and Umrah, he said. As a Muslim, he is obliged to make the pilgrimages to Mecca at least once in his lifetime. The officer handed back his passport and Dakhil continued to wait in line.

At his turn, Dakhil approached the CBP officer in his booth, who repeated much of the same questions. But, unsatisfied with his responses, the officer took Dakhil to a small room close but separate from the main immigration hall.

“He asked me everything,” Dakhil told gpgmail. The officer asked about his work, his travel history and how long he planned to stay in the U.S. He told the officer he planned to stay for three months with a plan to travel to Disney World in Florida and later New York City with his wife and newborn daughter, who were still waiting for visas.

The officer then rummaged through Dakhil’s carry-on luggage, pulling out his computer and other items. Then the officer took Dakhil’s phone, which he was told to unlock, and took it to another room.

For more than six hours, Dakhil was forced to sit in a bright, cold and windowless airport waiting room. There was nowhere to lie down. Others had pushed chairs together to try to sleep.

A U.S. immigration form detailing Dakhil deportation.

Dakhil said when the officer returned, the questioning continued. The officer demanded to know more about what he was planning to do in the U.S. One line of questioning focused on an officer’s accusation that Dakhil was planning to work at a gas station owned by his cousin — which Dakhil denied.

“I told him I had no intention to work,” he told gpgmail. The officer continued with his line of questioning, he said, but he continued to deny that he wanted to stay or work in the U.S. “I’m quite happy back in Karachi and doing good financially,” he said.

Two more officers had entered the room and began to interrogate him as the first officer continued to search bags. At one point he pulled out a gift for his cousin — a painting with Arabic inscriptions.

But Dakhil was convinced he would be allowed entry — the officers had found nothing derogatory, he said.

“Then the officer who took my phone showed me an image,” he told gpgmail. It was an image from 2009 of a child, who had been murdered and mutilated. Despite the graphic nature of the image, gpgmail confirmed the photo was widely distributed on the internet and easily searchable using the name of the child’s murderer.

“I was shocked. What should I say?” he told gpgmail, describing the panic he felt. “This image is disturbing, but you can’t control the forwarded messages,” he explained.

Dakhil told the officer that the image was sent to him in a WhatsApp group. It’s difficult to distinguish where a saved image came from on WhatsApp, because it automatically downloads received images and videos to a user’s phone. Questionable content — even from unsolicited messages — found during a border search could be enough to deny the traveler entry.

The image was used to warn parents about kidnappings and abductions of children in his native Karachi. He described it as one of those viral messages that you forward to your friends and family to warn parents about the dangers to their children. The officer pressed for details about who sent the message. Dakhil told the officer that the sender was someone he met on his Hajj pilgrimage in 2011.

“We hardly knew each other,” he said, saying they stayed in touch through WhatsApp but barely spoke.

Dakhil told the officer that the image could be easily found on the internet, but the officer was more interested in the names of the WhatsApp group members.

“You can search the image over the internet,” Dakhil told the officer. But the officer declined and said the images were his responsibility. “We found this on your cellphone,” the officer said. At one point the officer demanded to know if Dakhil was organ smuggling.

After 15 hours answering questions and waiting, the officers decided that Dakhil would be denied entry and would have his five-year visa cancelled. He was also told his family would also have their visas cancelled. The officers asked Dakhil if he wanted to claim for asylum, which he declined.

“I was treated like a criminal,” Dakhil said. “They made my life miserable.”

* * *

It’s been almost nine months since Dakhil was turned away at the U.S. border.

He went back to the U.S. Embassy in Karachi twice to try to seek answers, but embassy officials said they could not reverse a CBP decision to deny a traveler entry to the United States. Frustrated but determined to know more, Dakhil asked for his records through a Freedom of Information Act (FOIA) request — which anyone can do — but had to pay hundreds of dollars for its processing.

He provided gpgmail with the documents he obtained. One record said that Dakhil was singled out because his name matched a “rule hit,” such as a name on a watchlist or a visit to a country under sanctions or embargoes, which typically requires additional vetting before the traveler can be allowed into the U.S.

The record did not say what flagged Dakhil for additional screening, and his travel history did not include an embargoed country.

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CBP’s reason for denying entry to Dakhil obtained through a FOIA request.

One document said CBP denied Dakhil entry to the U.S. “due to the derogatory images found on his cellphone,” and his alleged “intent to engage in unauthorized employment during his entry.” But Dakhil told gpgmail that he vehemently denies the CBP’s allegations that he was traveling to the U.S. to work.

He said the document portrays a different version of events than what he experienced.

“They totally changed this scenario,” he said, rebutting several remarks and descriptions reported by the officers. “They only disclosed what they wanted to disclose,” he said. “They want to justify their decision, so they mentioned working in a gas station by themselves,” he claimed.

The document also said Dakhil “was permitted to view the WhatsApp group message thread on his phone and he stated that it was sent to him in September 2018,” but this was not enough to satisfy the CBP officers who ruled he should be denied entry. The document said Dakhil stated that he “never took this photo and doesn’t believe [the sender is] involved either,” but he was “advised that he was responsible for all the contents on his phone to include all media and he stated that he understood.”

The same document confirmed the contents of his phone was uploaded to the CBP’s central database and provided to the FBI’s Joint Terrorism Task Force.

Dakhil was “found inadmissible” and was put on the next flight back to Karachi, more than a day after he was first approached by the CBP officer in the immigration line.

A spokesperson for Customs & Border Protection declined to comment on individual cases, but provided a boilerplate statement.

“CBP is responsible for ensuring the safety and admissibility of the goods and people entering the United States. Applicants must demonstrate they are admissible into the U.S. by overcoming all grounds of inadmissibility including health-related grounds, criminality, security reasons, public charge, labor certification, illegal entrants and immigration violations, documentation requirements, and miscellaneous grounds,” the spokesperson said. “This individual was deemed inadmissible to the United States based on information discovered during the CBP inspection.”

CBP said it also has the right to cancel visas if a traveler is deemed inadmissible to the United States.

It’s unlikely Dakhil will return to the U.S., but he said he had hope for the Harvard student who suffered a similar fate.

“Let’s hope he can fight and make it,” he said.


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The risks of amoral A.I. – gpgmail


Artificial intelligence is now being used to make decisions about lives, livelihoods, and interactions in the real world in ways that pose real risks to people.

We were all skeptics once. Not that long ago, conventional wisdom held that machine intelligence showed great promise, but it was always just a few years away. Today there is absolute faith that the future has arrived.

It’s not that surprising with cars that (sometimes and under certain conditions) drive themselves and software that beats humans at games like chess and Go. You can’t blame people for being impressed.

But board games, even complicated ones, are a far cry from the messiness and uncertainty of real-life, and autonomous cars still aren’t actually sharing the road with us (at least not without some catastrophic failures).

AI is being used in a surprising number of applications, making judgments about job performance, hiring, loans, and criminal justice among many others. Most people are not aware of the potential risks in these judgments. They should be. There is a general feeling that technology is inherently neutral — even among many of those developing AI solutions. But AI developers make decisions and choose tradeoffs that affect outcomes. Developers are embedding ethical choices within the technology but without thinking about their decisions in those terms.

These tradeoffs are usually technical and subtle, and the downstream implications are not always obvious at the point the decisions are made.

The fatal Uber accident in Tempe, Arizona, is a (not-subtle) but good illustrative example that makes it easy to see how it happens.

The autonomous vehicle system actually detected the pedestrian in time to stop but the developers had tweaked the emergency braking system in favor of not braking too much, balancing a tradeoff between jerky driving and safety. The Uber developers opted for the more commercially viable choice. Eventually autonomous driving technology will improve to a point that allows for both safety and smooth driving, but will we put autonomous cars on the road before that happens? Profit interests are pushing hard to get them on the road immediately.

Physical risks pose an obvious danger, but there has been real harm from automated decision-making systems as well. AI does, in fact, have the potential to benefit the world. Ideally, we mitigate for the downsides in order to get the benefits with minimal harm.

A significant risk is that we advance the use of AI technology at the cost of reducing individual human rights. We’re already seeing that happen. One important example is that the right to appeal judicial decisions is weakened when AI tools are involved. In many other cases, individuals don’t even know that a choice not to hire, promote, or extend a loan to them was informed by a statistical algorithm. 

Buyer Beware

Buyers of the technology are at a disadvantage when they know so much less about it than the sellers do. For the most part decision makers are not equipped to evaluate intelligent systems. In economic terms, there is an information asymmetry that puts AI developers in a more powerful position over those who might use it. (Side note: the subjects of AI decisions generally have no power at all.) The nature of AI is that you simply trust (or not) the decisions it makes. You can’t ask technology why it decided something or if it considered other alternatives or suggest hypotheticals to explore variations on the question you asked. Given the current trust in technology, vendors’ promises about a cheaper and faster way to get the job done can be very enticing.

So far, we as a society have not had a way to assess the value of algorithms against the costs they impose on society. There has been very little public discussion even when government entities decide to adopt new AI solutions. Worse than that, information about the data used for training the system plus its weighting schemes, model selection, and other choices vendors make while developing the software are deemed trade secrets and therefore not available for discussion.

Image via Getty Images / sorbetto

The Yale Journal of Law and Technology published a paper by Robert Brauneis and Ellen P. Goodman where they describe their efforts to test the transparency around government adoption of data analytics tools for predictive algorithms. They filed forty-two open records requests to various public agencies about their use of decision-making support tools.

Their “specific goal was to assess whether open records processes would enable citizens to discover what policy judgments these algorithms embody and to evaluate their utility and fairness”. Nearly all of the agencies involved were either unwilling or unable to provide information that could lead to an understanding of how the algorithms worked to decide citizens’ fates. Government record-keeping was one of the biggest problems, but companies’ aggressive trade secret and confidentiality claims were also a significant factor.

Using data-driven risk assessment tools can be useful especially in cases identifying low-risk individuals who can benefit from reduced prison sentences. Reduced or waived sentences alleviate stresses on the prison system and benefit the individuals, their families, and communities as well. Despite the possible upsides, if these tools interfere with Constitutional rights to due process, they are not worth the risk.

All of us have the right to question the accuracy and relevance of information used in judicial proceedings and in many other situations as well. Unfortunately for the citizens of Wisconsin, the argument that a company’s profit interest outweighs a defendant’s right to due process was affirmed by that state’s supreme court in 2016.

Fairness is in the Eye of the Beholder

Of course, human judgment is biased too. Indeed, professional cultures have had to evolve to address it. Judges for example, strive to separate their prejudices from their judgments, and there are processes to challenge the fairness of judicial decisions.

In the United States, the 1968 Fair Housing Act was passed to ensure that real-estate professionals conduct their business without discriminating against clients. Technology companies do not have such a culture. Recent news has shown just the opposite. For individual AI developers, the focus is on getting the algorithms correct with high accuracy for whatever definition of accuracy they assume in their modeling.

I recently listened to a podcast where the conversation wondered whether talk about bias in AI wasn’t holding machines to a different standard than humans—seeming to suggest that machines were being put at a disadvantage in some imagined competition with humans.

As true technology believers, the host and guest eventually concluded that once AI researchers have solved the machine bias problem, we’ll have a new, even better standard for humans to live up to, and at that point the machines can teach humans how to avoid bias. The implication is that there is an objective answer out there, and while we humans have struggled to find it, the machines can show us the way. The truth is that in many cases there are contradictory notions about what it means to be fair.

A handful of research papers have come out in the past couple of years that tackle the question of fairness from a statistical and mathematical point-of-view. One of the papers, for example, formalizes some basic criteria to determine if a decision is fair.

In their formalization, in most situations, differing ideas about what it means to be fair are not just different but actually incompatible. A single objective solution that can be called fair simply doesn’t exist, making it impossible for statistically trained machines to answer these questions. Considered in this light, a conversation about machines giving human beings lessons in fairness sounds more like theater of the absurd than a purported thoughtful conversation about the issues involved.

Image courtesy of gpgmail/Bryce Durbin

When there are questions of bias, a discussion is necessary. What it means to be fair in contexts like criminal sentencing, granting loans, job and college opportunities, for example, have not been settled and unfortunately contain political elements. We’re being asked to join in an illusion that artificial intelligence can somehow de-politicize these issues. The fact is, the technology embodies a particular stance, but we don’t know what it is.

Technologists with their heads down focused on algorithms are determining important structural issues and making policy choices. This removes the collective conversation and cuts off input from other points-of-view. Sociologists, historians, political scientists, and above all stakeholders within the community would have a lot to contribute to the debate. Applying AI for these tricky problems paints a veneer of science that tries to dole out apolitical solutions to difficult questions. 

Who Will Watch the (AI) Watchers?

One major driver of the current trend to adopt AI solutions is that the negative externalities from the use of AI are not borne by the companies developing it. Typically, we address this situation with government regulation. Industrial pollution, for example, is restricted because it creates a future cost to society. We also use regulation to protect individuals in situations where they may come to harm.

Both of these potential negative consequences exist in our current uses of AI. For self-driving cars, there are already regulatory bodies involved, so we can expect a public dialog about when and in what ways AI driven vehicles can be used. What about the other uses of AI? Currently, except for some action by New York City, there is exactly zero regulation around the use of AI. The most basic assurances of algorithmic accountability are not guaranteed for either users of technology or the subjects of automated decision making.

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Image via Getty Images / nadia_bormotova

Unfortunately, we can’t leave it to companies to police themselves. Facebook’s slogan, “Move fast and break things” has been retired, but the mindset and the culture persist throughout Silicon Valley. An attitude of doing what you think is best and apologizing later continues to dominate.

This has apparently been effective when building systems to upsell consumers or connect riders with drivers. It becomes completely unacceptable when you make decisions affecting people’s lives. Even if well-intentioned, the researchers and developers writing the code don’t have the training or, at the risk of offending some wonderful colleagues, the inclination to think about these issues.

I’ve seen firsthand too many researchers who demonstrate a surprising nonchalance about the human impact. I recently attended an innovation conference just outside of Silicon Valley. One of the presentations included a doctored video of a very famous person delivering a speech that never actually took place. The manipulation of the video was completely imperceptible.

When the researcher was asked about the implications of deceptive technology, she was dismissive of the question. Her answer was essentially, “I make the technology and then leave those questions to the social scientists to work out.” This is just one of the worst examples I’ve seen from many researchers who don’t have these issues on their radars. I suppose that requiring computer scientists to double major in moral philosophy isn’t practical, but the lack of concern is striking.

Recently we learned that Amazon abandoned an in-house technology that they had been testing to select the best resumes from among their applicants. Amazon discovered that the system they created developed a preference for male candidates, in effect, penalizing women who applied. In this case, Amazon was sufficiently motivated to ensure their own technology was working as effectively as possible, but will other companies be as vigilant?

As a matter of fact, Reuters reports that other companies are blithely moving ahead with AI for hiring. A third-party vendor selling such technology actually has no incentive to test that it’s not biased unless customers demand it, and as I mentioned, decision makers are mostly not in a position to have that conversation. Again, human bias plays a part in hiring too. But companies can and should deal with that.

With machine learning, they can’t be sure what discriminatory features the system might learn. Absent the market forces, unless companies are compelled to be transparent about the development and their use of opaque technology in domains where fairness matters, it’s not going to happen.

Accountability and transparency are paramount to safely using AI in real-world applications. Regulations could require access to basic information about the technology. Since no solution is completely accurate, the regulation should allow adopters to understand the effects of errors. Are errors relatively minor or major? Uber’s use of AI killed a pedestrian. How bad is the worst-case scenario in other applications? How are algorithms trained? What data was used for training and how was it assessed to determine its fitness for the intended purpose? Does it truly represent the people under consideration? Does it contain biases? Only by having access to this kind of information can stakeholders make informed decisions about appropriate risks and tradeoffs.

At this point, we might have to face the fact that our current uses of AI are getting ahead of its capabilities and that using it safely requires a lot more thought than it’s getting now.


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Tastemakers raises $1.4M to sell Africa experiences to the world – gpgmail


New York based startup Tastemakers has raised a $1.4 million seed-round—led Precursor Ventures—for its business that connects Africa adventures to global consumers.

Tastemakers’ platform curates, prices, and lists African travel and cultural experiences—from paragliding tours to wine-tasting to concerts.

The startup generates revenues by taking a 20% commission on each transaction. Community managers in Africa screen and select experiences that go up on the site .

Tastemakers will use the investment to grow the number of experiences offered from 200 to 10,000 and build out machine learning capabilities to better match suppliers, experiences, and clients—CEO and founder Cherae Robinson told gpgmail.

She likened the site to an Airbnb for commoditizing and connecting people to Africa travel experiences at scale.

On the startup’s addressable market, Robinson references a segment of culture curious travelers: people who are travelling to experience things such foreign art, food, music, or dance workshops.

“We looked at who’s doing these kinds of tours and and the number of people booking…and we found that globally, based on triangulating that, there are about 700 million people globally booking culture forward experiences,” said Robinson.

For different reasons—from negative stereotypes or the difficulty of identifying tourist options in Africa—most of these excursions are occurring in other parts of the world, according to Robinson.

She sees Tastemakers’ value proposition as the site that can bring a greater percentage of these culture travelers to Africa.

On revenue potential, Robinson is pretty up front on numbers and goals. “If we can capture 1% of that [700 million] market in the next five years that’s $2.2 billion generated on our platform,” she said, noting an average booking cost of $308. She believes Tastemakers could hit those figures by 2025—and by applying their 20 percent commission—reach income of $434 million.

Tastemakers Africa Ghana III

Precursor Ventures Managing Partner Charles Hudson invested in Tastemakers for its potential as an early entrant in an off the grid travel market attracting more curiosity.

“I just had a sense that Africa was having a moment, and whether its Black Panther or more startups that have a foot in Africa, that there were more people interested in going to Africa,” he told gpgmail.

“And it’s not like going to New York City…You have providers that are hard to find and hard to book..that are not super well marketed. If you can become an aggregator and curator of those, you could effectively become the largest source of lead generation,” Hudson said.

Tastemakers is looking at  ancillary partnership and revenue share opportunities. It uses Stripe and WorldRemit to process mobile payments for transactions on the site and has done promotional partnerships with Uber Africa. The startup also counts Kempinski Hotels as its biggest lodging partner.

Tastemakers also offers advisory services to sellers on the site, to better determine price-points and on marketing their travel experiences more effectively online.

CEO Cherae Robinson is clear about the company’s for-profit status, but sees upside for Africa beyond generating business from tourism. “I strategically don’t brand Tastemakers as a social impact startup…but we’re driving benefits of the sharing economy to diverse populations both in Africa and in underrepresented communities in the technology and tourism sectors,” she said.

 


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Artificial intelligence can contribute to a safer world – gpgmail


We all see the headlines nearly every day. A drone disrupting the airspace in one of the world’s busiest airports, putting aircraft at risk (and inconveniencing hundreds of thousands of passengers) or attacks on critical infrastructure. Or a shooting in a place of worship, a school, a courthouse. Whether primitive (gunpowder) or cutting-edge (unmanned aerial vehicles) in the wrong hands, technology can empower bad actors and put our society at risk, creating a sense of helplessness and frustration.

Current approaches to protecting our public venues are not up to the task, and, frankly appear to meet Einstein’s definition of insanity: “doing the same thing over and over and expecting a different result.” It is time to look past traditional defense technologies and see if newer approaches can tilt the pendulum back in the defender’s favor. Artificial Intelligence (AI) can play a critical role here, helping to identify, classify and promulgate counteractions on potential threats faster than any security personnel.

Using technology to prevent violence, specifically by searching for concealed weapons has a long history. Alexander Graham Bell invented the first metal detector in 1881 in an unsuccessful attempt to locate the fatal slug as President James Garfield lay dying of an assassin’s bullet. The first commercial metal detectors were developed in the 1960s. Most of us are familiar with their use in airports, courthouses and other public venues to screen for guns, knives and bombs.

However, metal detectors are slow and full of false positives – they cannot distinguish between a Smith & Wesson and an iPhone. It is not enough to simply identify a piece of metal; it is critical to determine whether it is a threat. Thus, the physical security industry has developed newer approaches, including full-body scanners – which are now deployed on a limited basis. While effective to a point, the systems in use today all have significant drawbacks. One is speed. Full body scanners, for example, can process only about 250 people per hour, not much faster than a metal detector. While that might be okay for low volume courthouses, it’s a significant problem for larger venues like a sporting arena.

Image via Getty Images

Fortunately, new AI technologies are enabling major advances in physical security capabilities. These new systems not only deploy advanced sensors to screen for guns, knives and bombs, they get smarter with each screen, creating an increasingly large database of known and emerging threats while segmenting off alarms for common, non-threatening objects (keys, change, iPads, etc.)

As part of a new industrial revolution in physical security, engineers have developed a welcomed approach to expediting security screenings for threats through machine learning algorithms, facial recognition, and advanced millimeter wave and other RF sensors to non-intrusively screen people as they walk through scanning devices. It’s like walking through sensors at the door at Nordstrom, the opposite of the prison-like experience of metal detectors with which we are all too familiar. These systems produce an analysis of what someone may be carrying in about a hundredth of a second, far faster than full body scanners. What’s more, people do not need to empty their pockets during the process, further adding speed. Even so, these solutions can screen for firearms, explosives, suicide vests or belts at a rate of about 900 people per hour through one lane.

Using AI, advanced screening systems enable people to walk through quickly and provide an automated decision but without creating a bottleneck. This volume greatly improves traffic flow while also improving the accuracy of detection and makes this technology suitable for additional facilities such as stadiums and other public venues such as Lincoln Center in New York City and the Oakland airport.

Apollo Shield’s anti-drone system.

So much for the land, what about the air?   Increasingly drones are being used as weapons. Famously, this was seen in a drone attack last year against Venezuelan president Nicolas Maduro. An airport drone incident drew widespread attention when a drone shut down Gatwick Airport in late 2018 inconveniency stranded tens of thousands of people.

People are rightly concerned about how easy it is to get a gun. Drones are also easy to acquire and operate, and quite difficult to monitor and to defend against. AI is now being deployed to prevent drone attacks, whether at airports, stadiums, or critical infrastructure. For example, new AI-powered radar technology is being used to detect, classify, monitor and safely capture drones identified as dangerous.

Additionally, these systems use can rapidly develop a map of the airspace and effectively create a security “dome” around specific venues or areas. These systems have an integration component to coordinate with on-the-ground security teams and first responders. Some even have a capture drone to incarcerate a suspicious drone. When a threatening drone is detected and classified by the system as dangerous, the capture drone is dispatched and nets the invading drone. The hunter then tows the targeted drone to a safe zone for the threat to be evaluated and if needed, destroyed.

While there is much dialogue about the potential risk of AI affecting our society, there is also a positive side to these technologies. Coupled with our best physical security approaches, AI can help prevent violent incidents.


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