Moscow is the latest major city to introduce live facial recognition cameras to its streets, with Mayor Sergei Sobyanin announcing that the technology is operating “on a mass scale” earlier this month, according to a report from Russian business paper Vedomosti.
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Moscow started trialing live facial recognition in 2017, using technology from Russian firm NtechLab to scan footage from the Russian capital’s network of 160,000 CCTV cameras. The company is best known for its FindFace software, which it launched in 2016 and let users match anyone in a picture to their profile on VK, known as Russia’s Facebook.
The app was criticized by some, particularly as it was used to dox and harass sex workers, and NtechLab eventually shut it down in favor of enterprise and government work.
While the world debates the utility and ethics of existing facial recognition technology, new biometrics are constantly being developed. They are likely to replace facial recognition in the long term.
This system, dubbed Jetson, is able to measure, from up to 200 metres away, the minute vibrations induced in clothing by someone’s heartbeat. Since hearts differ in both shape and contraction pattern, the details of heartbeats differ, too. The effect of this on the fabric of garments produces what Ideal Innovations, a firm involved in the Jetson project, calls a “heartprint”—a pattern reckoned sufficiently distinctive to confirm someone’s identity.
To measure heartprints remotely Jetson employs gadgets called laser vibrometers. These work by detecting minute variations in a laser beam that has been reflected off an object of interest. They have been used for decades to study things like bridges, aircraft bodies, warship cannons and wind turbines—searching for otherwise-invisible cracks, air pockets and other dangerous defects in materials. However, only in the past five years or so has laser vibrometry become good enough to distinguish the vibrations induced in fabric by heartprints.
The technology London plans to deploy goes beyond many of the facial recognition systems used elsewhere, which match a photo against a database to identify a person. The new systems, created by the company NEC, attempt to identify people on a police watch list in real time with security cameras, giving officers a chance to stop them in the specific location.
The German Data Ethics Commission issued a 240-page report with 75 recommendations for regulating data, algorithmic systems, and AI. It is one of the strongest views on ethical AI to date and favors explicit regulation.
The Data Ethics Commission holds the view that regulation is necessary, and cannot be replaced by ethical principles.
Opinion of the Data Ethics Commission – Executive Summary at 7 (emphasis original).
The report divides ethical considerations into concerns about either data or algorithmic systems. For data, the report suggests that rights associated with the data will play a significant role in the ethical landscape. For example, ensuring that individuals provide informed consent for use of their personal data addresses a number of significant ethical issues.
For algorithmic systems, however, the report suggests that the AI systems might have no connection to the affected individuals. As a result, even non-personal data for which there are no associated rights could be used in an unethical manner. The report concludes that regulation is necessary to the extent there is a potential for harm.
The report identifies five levels of algorithmic system criticality. Applications with zero or negligible potential for harm would face no regulation. The regulatory burden would increase as the potential for harm increases, up to a total ban. For applications with serious potential for harm, the report recommends constant oversight.
The framework appears to be a good candidate for future ethical AI regulation in Europe, and perhaps (by default) the world.
Humans are inscrutable in a way that algorithms are not. Our explanations for our behavior are shifting and constructed after the fact. To measure racial discrimination by people, we must create controlled circumstances in the real world where only race differs. For an algorithm, we can create equally controlled just by feeding it the right data and observing its behavior.
This is a fascinating complement to the concern that deep learning algorithms are a black box and we do not understand how they work. Even so, they are much easier to study than humans. Algorithms are tractable in a way that humans are not.
At its core, this essay is an argument for AI regulation, and an argument that such regulation will actually work.
Landlords and lenders are pushing the Department of Housing and Urban Development to make it easier for businesses to discriminate against possible tenants using automated tools. Under a new proposal that just finished its public comment period, HUD suggested raising the bar for some legal challenges, making discrimination cases less likely to succeed.
The HUD proposed rule adds a new burden-shifting framework that would require plaintiffs to plead five specific elements to make a prima facie case that “a challenged practice actually or predictably results in a disparate impact on a protected class of persons . . . .” Current regulations permit complaints against such practices “even if the practice was not motivated by discriminatory intent.” The new rule continues to allow such complaints, but would allow defendants to rebut the claim at the pleading stage by asserting that a plaintiff has not alleged facts sufficient to support a prima facie claim.
One new requirement is that the plaintiff plead that the practice is “arbitrary, artificial, and unnecessary.” This introduces a kind of balancing test even if the practice has discriminatory impact. (A balancing test is already somewhat present in Supreme Court precedent, and the rule purports to be following this precedent.) As a result, if the challenged practice nevertheless serves a “legitimate objective,” the defendant may rebut the claim at the pleading stage.
The net result of the proposed rule will be to make it easier for new technologies, especially artificial intelligence technologies, to pass muster under housing discrimination laws. If the technology has a legitimate objective, it may not run afoul of HUD rules despite having a disparate impact on a protected class of persons.
This is not theoretical. HUD sued Facebook for housing discrimination earlier this year.
One of the most important AI ethics tasks is to educate developers and especially users about what AI’s can do well and what they cannot do well. AI systems do amazing things, and users mostly assume these things are done accurately based on a few demonstrations. For example, the police assume facial recognition systems accurately tag bad guys, and that license plate databases accurately contain lists of stolen cars. But these systems are brittle, and an excellent example of this is the fun, new ImageNet Roulette [update 2/22/20: no longer available] web tool put together by artist and researcher Trevor Paglen.
ImageNet Roulette is a provocation designed to help us see into the ways that humans are classified in machine learning systems. It uses a neural network trained on the “Person” categories from the ImageNet dataset which has over 2,500 labels used to classify images of people.
The service claims not to keep any uploaded photos, so if you trust them, you can upload a webcam image of yourself and see how the internet classifies your face.
Of course no human would look at a random image of another human devoid of context and attempt to assign a description such as “pipe smoker” or “newspaper reader.” We would say, “I don’t know. It just looks like a person.”
But AI’s aren’t that smart yet. They don’t know what they can’t know. So ImageNet Roulette calculates probabilities that an image falls into a given description, and then it outputs the highest probability description. It’s a shot in the dark. You might think it is seeing something deep, but nope. It has 2,500 labels and it has to apply one. I apparently look like a sociologist.
In contrast to recent U.S. municipal decisions restricting government use of facial recognition technology, a UK court has ruled that police use of the technology does not violate any fundamental rights.
In one of the first lawsuits to address the use of live facial recognition technology by governments, a British court ruled on Wednesday that police use of the systems is acceptable and does not violate privacy and human rights.
Phenomenal essay by Lucas Kunce, a U.S. Marine who served in Iraq and Afghanistan, responding to news that 4,600 Google employees signed a petition urging the company to refuse to build weapons technology:
People frequently threw objects of all sizes at our vehicles in anger and protest. Aside from roadside bombs, the biggest threat at the time, particularly in crowded areas, was an armor-piercing hand-held grenade. It looked like a dark soda can with a handle protruding from the bottom. Or, from a distance and with only an instant to decide, it looked just like many of the other objects that were thrown at us.
One day in Falluja, at the site of a previous attack, an Iraqi man threw a dark oblong object at one of the vehicles in my sister team. The Marine in the turret, believing it was an armor-piercing grenade, shot the man in the chest. The object turned out to be a shoe.
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When I think about A.I. and weapons development, I don’t imagine Skynet, the Terminator, or some other Hollywood dream of killer robots. I picture the Marines I know patrolling Falluja with a heads-up display like HoloLens, tied to sensors and to an A.I. system that can process data faster and more precisely than humanly possible — an interface that helps them identify an object as a shoe, or an approaching truck as too light to be laden with explosives.
I enjoyed this interview and especially the title: “Humans Don’t Realize How Biased They Are Until AI Reproduces the Same Bias, Says UNESCO AI Chair.”
What are some core problems or research areas you want to approach?
People are now solving problems just by throwing an enormous amount of computation and data at them and trying every possible way. You can afford to do that if you are a big company and have a lot of resources, but people in developing countries cannot afford the data or the computational resources. So the theoretical challenge, or the fundamental challenge, is how to develop methods that are better understood and therefore don’t need experiments with hundreds of variants to get things to work.
Another thing is that some of the problems with current datasets, especially in terms of the usefulness of these systems for different cultures, is that there is a cultural bias in the data that has been collected. It is Western data informed with the Western way of seeing and doing things, so to some extent having data from different cultures and different environments is going to help make things more useful. You need to learn from data that is more relevant to the task.
“Solving” is probably too strong, but for addressing those problems, as I’ve said, the problem is that we don’t realise that they are the reflections of our own problems. We don’t realise how biased we are until we see an AI reproduce the same bias, and we see that it’s biased.
I chuckle a bit when I hear about biased humans going over biased data in the hopes of creating unbiased data. Bias is a really hard problem, and it’s always going to be with us in one form or another. Education and awareness are the most important tools for addressing.