In May of 2010, prompted by a series of high-profile scandals, the mayor of New Orleans asked the US Department of Justice to investigate the city police department (NOPD). Ten months later, the DOJ offered its blistering analysis: during the period of its review from 2005 onwards, the NOPD had repeatedly violated constitutional and federal law.

It used excessive force, and disproportionately against black residents; targeted racial minorities, non-native English speakers, and LGBTQ individuals; and failed to address violence against women. The problems, said assistant attorney general Thomas Perez at the time, were “serious, wide-ranging, systemic and deeply rooted within the culture of the department.”

Despite the disturbing findings, the city entered a secret partnership only a year later with data-mining firm Palantir to deploy a predictive policing system. The system used historical data, including arrest records and electronic police reports, to forecast crime and help shape public safety strategies, according to company and city government materials. At no point did those materials suggest any effort to clean or amend the data to address the violations revealed by the DOJ. In all likelihood, the corrupted data was fed directly into the system, reinforcing the department’s discriminatory practices.

Predictive policing algorithms are becoming common practice in cities across the US. Though lack of transparency makes exact statistics hard to pin down, PredPol, a leading vendor, boasts that it helps “protect” 1 in 33 Americans. The software is often touted as a way to help thinly stretched police departments make more efficient, data-driven decisions.

But new research suggests it’s not just New Orleans that has trained these systems with “dirty data.” In a paper released today, to be published in the NYU Law Review, researchers at the AI Now Institute, a research center that studies the social impact of artificial intelligence, found the problem to be pervasive among the jurisdictions it studied. This has significant implications for the efficacy of predictive policing and other algorithms used in the criminal justice system.

“Your system is only as good as the data that you use to train it on,” says Kate Crawford, cofounder and co-director of AI Now and an author on the study. “If the data itself is incorrect, it will cause more police resources to be focused on the same over-surveilled and often racially targeted communities. So what you’ve done is actually a type of tech-washing where people who use these systems assume that they are somehow more neutral or objective, but in actual fact they have ingrained a form of unconstitutionality or illegality.”

The researchers examined 13 jurisdictions, focusing on those that have used predictive policing systems and been subject to a government-commissioned investigation. The latter requirement ensured that the policing practices had legally verifiable documentation. In nine of the jurisdictions, they found strong evidence that the systems had been trained on “dirty data.”

The problem wasn’t just data skewed by disproportionate targeting of minorities, as in New Orleans. In some cases, police departments had a culture of purposely manipulating or falsifying data under intense political pressure to bring down official crime rates. In New York, for example, in order to artificially deflate crime statistics, precinct commanders regularly asked victims at crime scenes not to file complaints. Some police officers even planted drugs on innocent people to meet their quotas for arrests. In modern-day predictive policing systems, which rely on machine learning to forecast crime, those corrupted data points become legitimate predictors.

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Monsters and titans share the stage of mythology across cultures as the necessary realisations of the human imagination. From stone cave to urban dwelling, the theme is unremitting; kept in the imagination, such creatures perform, innocently enough, benign functions. The catch here is the human tendency to realise such creatures. They take the form of social engineering and utopia. Folly bound, such projects and ventures wind up corrupting and degrading. The monster is born, and the awful truth comes to the fore: the concentration camp, the surveillance state, newspeak, the armies of censorship.

The technology giants of the current era are the modern utopians, indulging human hunger and interests by shaping them. One company gives us the archetype. It is Google, which has the unusual distinction of being both noun and verb, entity and action. Google’s power is disproportionately vast, a creepy sprawl that cherishes transparency while lacking it, and treasuring information while regulating its reach. It is also an entity that has gone beyond being a mere repository of searches and data, an attempt to induce behavioural change on the part of users.

Google always gives the impression that its users are in the lead, autonomous, independent in a verdant land of digital frolicking. The idea that the company itself fosters such change, teasing out alterations in behaviour, is placed to one side. There are no Svengalis in Googleland, because we are all free. Free, but needing assistance amidst chaos and “multitasking”.

People have what the company calls “micro-moments”, those, as behavioural economist Dan Ariely describes as “on-the-go mobile moments” where decisions are reached by a user while engaged, simultaneously, in a range of tasks: hotels to book, travel choices to make, work schedules to fulfil. While Ariely is writing more broadly from the perspective of the ubiquitous digital marketer, the language is pure Googleleese, smacking of part persuasion and part imposition. “Want to develop a strategy to shape your consumer decisions?” asks Google. “Start by understanding the key micro-moments in their journey.” Understand them; feed their mind; hold their hand.

The addiction to Google produces what can no longer be seen as retarding, but fostering. A generation is growing up without a hard copy research library, a ready-to-hand list of classics, and the means to search through records without resorting to those damnable digital keys. Debates are bound to be had (some already pollute the digital space) about whether this is necessarily a condition to lament. Embrace digital amnesia! To Google is to exist.

What is undeniable is that the means to find information – instantaneous, glut-filled, desperately quick – has created users who inhabit a space that guides their thinking, pre-empting, cajoling and adjusting. One form of literacy, we might kindly say, is being supplanted by another: the Google imbecile is upon us.

Given the nature of such effects, it is little wonder that politicians find Google threatening to their mouldy and rusted on craft. The politician’s preserve is sound – or unsound – communication; success at the next election is dependent upon the idea that the electors understand, and approve, what has been relayed to them (whether that material is factual, or not, a lie or otherwise, is beside the point: the politician yearns to convince in order to win).

The old search engine titan supplies something of a snag in this regard. On the one hand, it offers the political classes the means to reach a global audience, an avenue to screech and promote the next hair-brained scheme that comes into the mind of the political apparat. But what if the message stymies on the way, finding delays in the means of what is called “search engine optimisation”? Is Google to blame, or bog standard ordinariness on the part of the politician?

US politicians think they have an answer. Only they are permitted control of the narrative, and disseminating the lie. Of late they have been trying to sketch out a path they are not used to: regulating industries once hailed as sentinels of freedom, promoters of liberty. Their complaints tend to lack consistency. On the one hand, they find various Google algorithms problematic (preference for alt-right sites, conspiratorial gruel as damaging), but their slant is wonky and skewed. Had these algorithms been driving favourable search terms (conformist, steady, unquestioning, anti-Trump), the matter would be a non-starter. Our message, they would say, is getting out there.

This week, the US Senate Committee on Commerce, Science and Transportation tried to make sense, in rather accusing fashion, of “persuasive technology”. Nanette Byrnes furnishes us with a definition: “the idea that computers, mobile phones, websites, and other technologies could be designed to influence people’s behaviour and even attitudes”. The Pope does remain resolutely Catholic.

The committee hearing featured such opinions as those of Senator John Thune (R-SD), who wished to use the proceedings to draft legislation that would “require internet platforms to give consumers the option to engage with the platform without having the experience shaped by algorithms.” The Senator is happy to accept that artificial intelligence “powers automations to display content to optimize engagement” but sees a devil in the works, as “AI algorithms can have an unintended and possibly even dangerous downside”. This is tantamount to wanting a Formula One Grand Prix without fast cars and an athletics competition in slow motion.

Facing the senators from Google’s side was Maggie Stanphill, director of Google User Experience. Her testimony was couched in words more akin to the glossiness of a travel brochure with a complimentary sprinkling of cocaine. “Google’s Digital Wellbeing Initiative is a top company goal, focusing on providing our users with insights about their digital habits and tools to support an intentional relationship with technology.” Google merely “creates products that improve the lives of the people who use them.” The company has provided access that has “democratized information and provided services for billions of people around the world.” When asked about whether Google was doing its bit in the persuasion business, Stanphill was unequivocal. “We do not use persuasive technology.”

The session’s theme was clear: oodles and masses of content are good, but must be appropriate. In Information Utopia, where digital Adam and Eve still run naked, wickedness will not be allowed. If people want to seek content that is “negative” (this horrendous arbitrary nature keeps appearing), they should not be allowed do. Gag them, and make sure the popular terms sought are white washed of any offensive or dangerous import. Impose upon the tech titans a responsibility to control the negative.

Senator Brian Schatz (D-Hawaii) complained of those companies “letting these algorithms run wild […] leaving humans to clean up the mess. Algorithms are amoral.” Tristan Harris, co-founder and executive director of the Centre for Humane Technology, spoke of the competition between companies to use algorithms which “more accurately predict what will keep users there the longest.” If you want to maximise the time spent searching terms or, in the case of YouTube, watching a video, focus “the entire ant colony of humanity towards crazytown.” For Harris, “technology hacks human weaknesses.” The moral? Do not give people what they want.

The rage against the algorithm, and the belief that no behavioural pushing is taking place in search technology, is misplaced on a few fronts. On a certain level, all accept how such modes of retrieving information work. Disagreement arises as to their consequences, a concession, effectively, to the Google user as imbecile. Stanphill is being disingenuous for assuming that persuasive technology is not a function of Google’s work (it patently is, given the company’s intention of improving the “intentional relationship with technology”). In her testimony, she spoke of building “products with privacy, transparency and control for the users, and we build a lifelong relationship with the user, which is primary.” The Senators, in turn, are concerned that the users, diapered by encouragements in their search interests, are incapable of making their own fragile minds up.

The nature of managed information in the digital experience is not, as Google, YouTube and like companies show, a case of broadening knowledge but reaffirming existing assumptions. The echo chamber bristles with confirmations not challenges, with the comforts of prejudice rather than the discomforts of heavy-artillery learning. But the elected citizens on the Hill, and the cyber utopians, continue to struggle and flounder in the digital jungle they had seen as an information utopia equal to all. For the Big Tech giants, it’s all rather simple: the attention grabbing spectacle, bums on seats, and downloads galore.

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Facebook has created devices that read all the words of your inner voice and Elon Musk’s Neuralink is creating brain-computer interfaces (BCI) that will have thousands and later millions of nodes.

Temporarily mitigating remote mind-reading of your inner voice could involve headphones for music and distractions of your own thoughts to less sensitive topics. However, the world is clearly developing where your thoughts and brain activity will be readable by anyone with relatively inexpensive devices.

We have already lost privacy to our financial activity, internet activity, retail activity and smartphones.

China, India, Iran, Russia, Japan, USA and European nations are actively working to improve existing electroencephalography, magnetic resonance, functional infrared, and the magnetic encephalography spectrums to develop future military applications. The US Air Force believes BCI interfaces could provide faster reaction times for firing missiles, drones and guns.

There are many dramatic advances in mind-reading which uses BCI (Brain-Computer Interfaces) to decode brain states to reconstruct what a subject is experiencing. There is a growing market for computer game and devices that are controlled by brain states (http://www.bcireview.com/). Wireless technology has made it possible to record from mobile humans.

BCI have been used to replace lost sensory interfaces. Cochlear implants were developed in the 1970s and over 219,000 people worldwide have received cochlear implants. Progress has been made on retinal and cortical implants to restore sight in blind patients. Blind patients have reported substantial “sight” using a camera to activate an array of electrodes on the tongue, one of the most sensitive sensory surfaces of the body.

The Tech giants and militaries around the world will be deploying this rapidly advancing versions of technology from now through 2035. Military think tanks are assuming this will be a dominant interface and highly impactful by 2030.

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At the final session of the 2019 Space Symposium in Colorado Springs, attendees straggled into a giant ballroom to listen to an Air Force official and a National Geospatial-Intelligence Agency (NGA) executive discuss, as the panel title put it, “Enterprise Disruption.” The presentation stayed as vague as the title until a direct question from the audience seemed to make the panelists squirm.

Just how good, the person wondered, had the military and intelligence communities’ algorithms gotten at interpreting data and taking action based on that analysis? They pointed out that the commercial satellite industry has software that can tally shipping containers on cargo ships and cars in parking lots soon after their pictures are snapped in space. “When will the Department of Defense have real-time, automated, global order of battle?” they asked.

“That’s a great question,” said Chirag Parikh, director of the NGA’s Office of Sciences and Methodologies. “And there’s a lot of really good classified answers.”

He paused and shifted in his seat. “What’s the next question?” he asked, smiling. But he continued talking, describing how “geospatial intelligence” no longer simply means pictures from satellites. It means anything with a timestamp and a location stamp, and the attempt to integrate all that sundry data.

Then, Parikh actually answered this question: When would that translate to near-instantaneous understanding and strategy development?

“If not now,” he said, “very soon.”

Parkih didn’t mention any particular programs that might help enable this kind of autonomous, real-time interpretation. But an initiative called Sentient has relevant capabilities. A product of the National Reconnaissance Office (NRO), Sentient is (or at least aims to be) an omnivorous analysis tool, capable of devouring data of all sorts, making sense of the past and present, anticipating the future, and pointing satellites toward what it determines will be the most interesting parts of that future. That, ideally, makes things simpler downstream for human analysts at other organizations, like the NGA, with which the satellite-centric NRO partners.

Until now, Sentient has been treated as a government secret, except for vague allusions in a few speeches and presentations. But recently released documents — many formerly classified secret or top secret — reveal new details about the program’s goals, progress, and reach.

Research related to Sentient has been going on since at least October 2010, when the agency posted a request for Sentient Enterprise white papers. A presentation says the program achieved its first R&D milestone in 2013, but details about what that milestone actually was remain redacted. (Deputy director of NRO’s Office of Public Affairs Karen Furgerson declined to comment on this timing in an email to The Verge.) A 2016 House Armed Services Committee hearing on national security space included a quick summary of this data-driven brain, but public meetings haven’t mentioned it since. In 2018, a presentation posted online claimed Sentient would go live that year, although Furgerson told The Verge it was currently under development.

The NRO has not said much about Sentient publicly because it is a classified program,” says Furgerson in an email, “and NRO rarely appears before Congress in open hearings.”

The agency has been developing this artificial brain for years, but details available to the public remain scarce. “It ingests high volumes of data and processes it,” says Furgerson. “Sentient catalogs normal patterns, detects anomalies, and helps forecast and model adversaries’ potential courses of action.” The NRO did not provide examples of patterns or anomalies, but one could imagine that things like “not moving a missile” versus “moving a missile” might be on the list. Those forecasts in hand, Sentient could turn satellites’ sensors to the right place at the right time to catch ill will (or whatever else it wants to see) in action. “Sentient is a thinking system,” says Furgerson.

It’s not all dystopian: the documents released by the NRO also imply that Sentient can make satellites more efficient and productive. It could also free up humans to focus on deep analysis rather than tedious needle-finding. But it could also contain unquestioned biases, come to dubious conclusions, and raise civil liberties concerns. Because of its secretive nature, we don’t know much about those potential problems.

“The NRO’s and the Intelligence Community’s standard practice is to NOT disclose sensitive sources and methods, as such disclosure introduces high risk of adversary nations’ countering them,” says Furgerson. “Such loss harms our nation and its allies; it decreases U.S. information advantage and national security. For those reasons, details about Sentient remain classified and what we can say about it is limited.”

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Amazon said this week its facial recognition software can detect a person’s fear.

Rekognition is one of many Amazon Web Services (AWS) cloud services available for developers. It can be used for facial analysis or sentiment analysis, which identifies different expressions and predicts emotions from images of people’s faces. The service uses artificial intelligence to “learn” from the reams of data it processes.

The tech giant revealed updates to the controversial tool on Monday that include improving the accuracy and functionality of its face analysis features such as identifying gender, emotions and age range.

“With this release, we have further improved the accuracy of gender identification,” Amazon said in a blog post. “In addition, we have improved accuracy for emotion detection (for all 7 emotions: ‘Happy’, ‘Sad’, ‘Angry’, ‘Surprised’, ‘Disgusted’, ‘Calm’ and ‘Confused’) and added a new emotion: ‘Fear.’”

Artificial intelligence researchers have invested plenty of resources to try and read a person’s emotions by analyzing their facial features, movements, voice and more. Some tech companies involved in the space include Microsoft, Affectiva and Kairos.

But some experts have pointed out that, while there is scientific evidence suggesting there are correlations between facial expressions and emotions, the way people communicate major emotions varies across cultures and situations. Sometimes, similar types of facial movements can express more than one category of emotions, and so researchers have warned “it is not possible to confidently infer happiness from a smile, anger from a scowl, or sadness from a frown, as much of current technology tries to do when applying what are mistakenly believed to be scientific facts.”

The availability of facial recognition technology has also raised concerns about its potential use in surveillance and for the possibility that it could intrude on privacy.

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Imagine you’re hiking through the woods near a border. Suddenly, you hear a mechanical buzzing, like a gigantic bee. Two quadcopters have spotted you and swoop in for a closer look. Antennae on both drones and on a nearby autonomous ground vehicle pick up the radio frequencies coming from the cell phone in your pocket. They send the signals to a central server, which triangulates your exact location and feeds it back to the drones. The robots close in.

Cameras and other sensors on the machines recognize you as human and try to ascertain your intentions. Are you a threat? Are you illegally crossing a border? Do you have a gun? Are you engaging in acts of terrorism or organized crime? The machines send video feeds to their human operator, a border guard in an office miles away, who checks the videos and decides that you are not a risk. The border guard pushes a button, and the robots disengage and continue on their patrol.

This is not science fiction. The European Union is financing a project to develop drones piloted by artificial intelligence and designed to autonomously patrol Europe’s borders. The drones will operate in swarms, coordinating and corroborating information among fleets of quadcopters, small fixed-wing airplanes, ground vehicles, submarines, and boats. Developers of the project, known as Roborder, say the robots will be able to identify humans and independently decide whether they represent a threat. If they determine that you may have committed a crime, they will notify border police.

President Donald Trump has used the specter of criminals crossing the southern border to stir nationalist political sentiment and energize his base. In Europe, two years after the height of the migration crisis that brought more than a million people to the continent, mostly from the Middle East and Africa, immigration remains a hot-button issue, even as the number of new arrivals has dropped. Political parties across the European Union are winning elections on anti-immigrant platforms and enacting increasingly restrictive border policies. Tech ethicists and privacy advocates worry that Roborder and projects like it outsource too much law enforcement work to nonhuman actors and could easily be weaponized against people in border areas.

“The development of these systems is a dark step into morally dangerous territory,” said Noel Sharkey, emeritus professor of robotics and artificial intelligence at Sheffield University in the U.K. and one of the founders of the International Committee for Robot Arms Control, a nonprofit that advocates against the military use of robotics. Sharkey lists examples of weaponized drones currently on the market: flying robots equipped with Tasers, pepper spray, rubber bullets, and other weapons. He warns of the implications of combining that technology with AI-based decision-making and using it in politically-charged border zones. “It’s only a matter of time before a drone will be able to take action to stop people,” Sharkey told The Intercept.

Roborder’s developers also may be violating the terms of their funding, according to documents about the project obtained via European Union transparency regulations. The initiative is mostly financed by an €8 million EU research and innovation grant designed for projects that are exclusively nonmilitary, but Roborder’s developers acknowledge that parts of their proposed system involve military technology or could easily be converted for military use.

Much of the development of Roborder is classified, but The Intercept obtained internal reports related to ethical considerations and concerns about the program. That documentation was improperly redacted and inadvertently released in full.

In one of the reports, Roborder’s developers sought to address ethical criteria that are tied to their EU funding. Developers considered whether their work could be modified or enhanced to harm humans and what could happen if the technology or knowledge developed in the project “ended up in the wrong hands.” These ethical issues are raised, wrote the developers, when “research makes use of classified information, materials or techniques; dangerous or restricted materials[;] and if specific results of the research could present a danger to participants or to society as a whole.”

Roborder’s developers argued that these ethical concerns did not apply to their work, stating that their only goal was to develop and test the new technology, and that it would not be sold or transferred outside of the European Union during the life cycle of the project. But in interviews with The Intercept, project developers acknowledged that their technology could be repurposed and sold, even outside of Europe, after the European project cycle has finished, which is expected to happen next year.

Beyond the Roborder project, the ethics reports filed with the European Commission suggest a larger question: When it comes to new technology with the potential to be used against vulnerable people in places with few human rights protections, who decides what we should and should not develop?

Roborder won its funding grant in 2017 and has set out to develop a marketable prototype — “a swarm of robotics to support border monitoring” — by mid-2020. Its developers hope to build and equip a collection of air, sea, and land drones that can be combined and sent out on border patrol missions, scanning for “threats” autonomously based on information provided by human operators, said Stefanos Vrochidis, Roborder’s project manager.

The drones will employ optical, infrared, and thermal cameras; radar; and radio frequency sensors to determine threats along the border. Cell phone frequencies will be used to triangulate the location of people suspected of criminal activity, and cameras will identify humans, guns, vehicles, and other objects. “The main objective is to have as many sensors in the field as possible to assist patrol personnel,” said Kostas Ioannidis, Roborder’s technical manager.

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