"Who needs Terminators when you have precision clickbait and ultra-deepfakes?" asks IEEE Spectrum: Hollywood's worst-case scenario involving artificial intelligence (AI) is familiar as a blockbuster sci-fi film: Machines acquire humanlike intelligence, achieving sentience, and inevitably turn into evil overlords that attempt to destroy the human race. This narrative capitalizes on our innate fear of technology, a reflection of the profound change that often accompanies new technological developments. However, as Malcolm Murdock, machine-learning engineer and author of the 2019 novel The Quantum Price, puts it, "AI doesn't have to be sentient to kill us all. There are plenty of other scenarios that will wipe us out before sentient AI becomes a problem." Their article presents six real-world AI worst-case scenarios that "could simply happen by default, unfolding organically — that is, if nothing is done to stop them." It includes the possibility of deepfakes and large-scale disinformation, as well as AI-enabled "predictive control" that ultimately robs us of our free will. But it also presents an alternative worst-case scenario: that "we become so scared of the power of this tremendous technology that we resist harnessing it for the actual good it can do in the world." Thanks to Slashdot reader schwit1 for sharing the article.

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When it comes to forecasting the elements, many seem ready to welcome the machine. But humans still outperform the algorithms -- especially in bad conditions. From a report: [...] Similarly, research published by NOAA Weather Prediction Service director David Novak and his colleagues show that while human forecasters may not be able to "beat" the models on your typical sunny, fair-weather day, they still produce more accurate predictions than the algorithm-crunchers in bad weather. Over the two decades of information Novak's team studied, humans were 20 to 40 percent more accurate at forecasting near-future precipitation than the Global Forecast System (GFS) and the North American Mesoscale Forecast System (NAM), the most commonly used national models. Humans also made statistically significant improvements to temperature forecasting over both model's guidance. "Oftentimes, we find that in the bigger events is when the forecasters can make some value-added improvements to the automated guidance," says Novak. Particularly in adverse conditions, great improvements to the model's forecast were usually due to human augmentation, he adds. This is even more true for local, severe events like thunderstorms and tornadoes, which rely on split-second decision-making in order to save lives. As forecasters become more familiar with a particular model, they begin to notice its biases and failings, Novak adds. Just like the model learns from us, we learn from the model.

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The latest painting to receive the reconstructed-by-AI treatment is Gustav Klimt's 1900 painting "Philosophy". The Washington Post reports: For decades, only black-and-white photographs of "Philosophy" existed. Now, thanks to artificial intelligence, we can see the work in full color. But does the re-creation really look like the original? Does it even look like a Klimt? The new version, created by Google Arts and Culture using machine learning, shows a very different Klimt than you'd expect if you're familiar with "The Kiss" or "Portrait of Adele Bloch-Bauer I...." "I don't know any better than Google what those paintings really look like, but I don't think that they looked like that," says Jane Kallir, longtime director of the Galerie St. Etienne in New York, which gave Klimt his first shows in the United States. "These things look like cartoons. They don't look like Klimt paintings. "It's like people who try to clone their dogs. You can do it, but it's not the same dog." The paintings are one of several recent attempts to use artificial intelligence to re-create lost art. The Rijksmuseum in Amsterdam used AI to reconstruct missing panels from the edges of Rembrandt's famous "Night Watch" and, over the summer, temporarily installed them alongside the real thing. A pair of researchers in the United Kingdom, who call themselves Oxia Palus, say they've rebuilt a Picasso nude that was hidden beneath "The Blind Man's Meal," using 3-D printing and AI. In October, an orchestra in Bonn, Germany, "played" Beethoven's 10th and unfinished symphony in full. The version was written by an algorithm. George Cann, co-founder of Oxia Palus, posits that artificial intelligence "could give us this parallel alternative universe of art that we never really quite had." It's an alluring idea. Peek beneath a Picasso at an earlier painting under the surface layer and it's like you're peering into the artist's mind, eavesdropping on thoughts from a century ago. See a painting that was lost to catastrophe come back to life and it's like you've traveled back in time, reversed fate. But if any of this re-created universe of lost art, like "Philosophy," is inaccurate, the AI creators might not be resurrecting history but inadvertently rewriting it.... [F]or Kallir, there is little of Klimt in what she calls the "gaudy" re-creations, adding that the paintings would have been more subdued, with smoother transitions from one color to the next. "If you've got a decent eye, and you look at the black-and-white reproductions and compare them to other paintings that were done around the same time, you can probably get a better idea of what they really look like," she says.

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The Washington Post says that when it comes to making New Year's resolutions, the Smithsonian has a better idea. "What if instead of relying on our own resolutions we asked an AI what it thinks we should do?" Starting this weekend, the "Futures" exhibit both online and at its Arts and Industries Building offers a "Resolutions Generator," an AI that makes suggestions on what commitments we should undertake for 2022.... It sounds like a slightly weird idea, and I'd be lying if I said it didn't turn up some weird results. "Change my name to one of my favorite shapes," it suggests, or "Every Friday for a year I will wear a different hat." And, "Every time I hear bells for a month, I will paint a potato." Designed by AI researcher-writer Janelle Shane, the generator's odd results are deliberate; she purposely trained the AI (the powerful GPT-3) with some of the wackier resolutions humans have put online, then set its parameters wide. "We wanted the AI to come up with the kind of interesting resolutions we're not thinking of," Shane said. "We wanted whimsy," added Rachel Goslins, the director of the Arts and Industries Building, "with a little bit of real." Okay, so probably not many people will really "Go into a library, climb up onto a shelf, yell down 'I am a giant giraffe!'" But it's a lot easier than trying to lose those 15 pounds. And this way you end up in a library. Plus they have a point. The truth is by accessing the collective corpus of human resolutions, AI might conceive of ideas that our pale human pea brains cannot... [T]here are growing piles of evidence that deploying AI that can think faster and even differently will pay dividends in the real world. A Stanford study last month concluded that AI sped up discoveries on coronavirus antiviral drugs by as much as a month, potentially saving lives. Canadian researchers in September found that AI made consistently better choices than doctors in treating behavioral problems. Even a button-down institution like Deloitte has a staffer who has persuasively argued that we should use AI, not humans, to update government regulations. The exhibit's AI also generated these New Year's resolutions: "Treat every dog I meet like a celebrity." "Every time I see a mirror I will remember that it is the gateway to another dimension." The AI researcher behind the project also generated Slashdot headlines back in 2017, using 162,000 headlines from the site's first 20 years. Some of my favorites: More Pong Users for Kernel Project Red Hat Releases Linux Games And Moon Why Open Source Power Man Sues Java Microsoft Releases New Months Ask Slashdot: Do We Want To Be the Computers?

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The Next Web's Tristan Greene combed through a recently published "position paper" detailing China's views on military AI regulation and found that it "makes absolutely no mention of restricting the use of machines capable of choosing and firing on targets autonomously." From the report: Per the paper: "In terms of law and ethics, countries need to uphold the common values of humanity, put people's well-being front and center, follow the principle of AI for good, and observe national or regional ethical norms in the development, deployment and use of relevant weapon systems." Neither the US or the PRC has any laws, rules, or regulations currently restricting the development or use of military LAWs. The paper's rhetoric may be empty, but there's still a lot we can glean from its contents. Research analyst Megha Pardhi, writing for the Asia Times, recently opined it was intended to signal that China's seeking to "be seen as a responsible state," and that it may be concerned over its progress in the field relative to other superpowers. According to Pardhi: "Beijing is likely talking about regulation out of fear either that it cannot catch up with others or that it is not confident of its capabilities. Meanwhile, formulating a few commonly agreeable rules on weaponization of AI would be prudent." "Despite the fact that neither the colonel's article nor the PRC's position paper mention LAWs directly, it's apparent that what they don't say is what's really at the heart of the issue," concludes Greene. "The global community has every reason to believe, and fear, that both China and the US are actively developing LAWS."

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Amazon has updated its Alexa voice assistant after it "challenged" a 10-year-old girl to touch a coin to the prongs of a half-inserted plug. From a report: The suggestion came after the girl asked Alexa for a "challenge to do". "Plug in a phone charger about halfway into a wall outlet, then touch a penny to the exposed prongs," the smart speaker said. Amazon said it fixed the error as soon as the company became aware of it. The girl's mother, Kristin Livdahl, described the incident on Twitter. She said: "We were doing some physical challenges, like laying down and rolling over holding a shoe on your foot, from a [physical education] teacher on YouTube earlier. Bad weather outside. She just wanted another one." That's when the Echo speaker suggested partaking in the challenge that it had "found on the web". The dangerous activity, known as "the penny challenge", began circulating on TikTok and other social media websites about a year ago.

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In a scenario that's part "Robocop" and part "Minority Report," researchers in China have created an AI that can reportedly identify crimes and file charges against criminals. Futurism reports: The AI was developed and tested by the Shanghai Pudong People's Procratorate, the country's largest district public prosecution office, South China Morning Post reports. It can file a charge with more than 97 percent accuracy based on a description of a suspected criminal case. "The system can replace prosecutors in the decision-making process to a certain extent," the researchers said in a paper published in Management Review seen by SCMP. The team built the machine off of an existing AI tool ominously called System 206. Prosecutors in China were already using the system to help assess evidence and determine whether or not a suspected criminal was dangerous to the public at large. However, it was fairly limited as it could not "participate in the decision-making process of filing charges and [suggesting] sentences," the team said in the paper. That would require the AI to be able to identify and remove irrelevant information in a case, and process human language in its neural network. The new AI developed in Shanghai is able to assess case files in such a manner. In fact, the machine can identify and charge criminals with the district's eight most common crimes: credit card fraud, gambling, reckless driving, intentional assault, obstructing an officer, theft, fraud, and even political dissent.

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Through public records requests, The Markup found more than 20 public agencies using the sometimes-controversial software. From the report: In 2020, the FDA's Center for Drug Evaluation and Research (CDER) faced a daunting task: It needed to fill more than 900 job vacancies -- and fast. The center, which does things like inspect pharmaceutical manufacturing facilities, was in the process of modernizing the FDA's New Drugs Regulatory Program just as the pandemic started. It faced "a surge in work," along with new constraints that have affected everyone during the pandemic, including travel limitations and lockdowns. So they decided to turn to an artificial intelligence tool to speed up the hiring, according to records obtained by The Markup. The center, along with the Office of Management and the Division of Management Services, the background section of a statement of work said, were developing a "recruitment plan to leverage artificial intelligence (AI) to assist in the time to hire process." The agency ultimately chose to use HireVue, an online platform that allows employers to review asynchronously recorded video interviews and have recruits play video games as part of their application process. Over the years the platform has also offered a variety of AI features to automatically score candidates. HireVue, controversially, used to offer facial analysis to predict whether an applicant would be a good fit for an open job. In recent years, research has shown that facial recognition software is racially biased. In 2019, the company's continued use of the technique led one member of its scientific advisory board to resign. It has since stopped using facial recognition. The Markup used GovSpend, a database of procurement records for U.S. agencies at the state, local, and federal levels, to identify agencies that use HireVue. We also searched for agencies using Teramind and ActivTrak, both another kind of controversial software that allows employers to remotely monitor their workers' browsing activities through screenshots and logs. The Markup contacted and filed public records requests with those 24 agencies to understand how they were using the software. Eleven public agencies, including the FDA, replied to The Markup with documents or confirmations that they had bought HireVue at some point since 2017. Of the six public agencies that replied to The Markup's questions confirming that they actually used the software, all but one -- Lake Travis Independent School District in Texas -- confirmed they did not make use of the AI scoring features of the software. Documents and responses from 13 agencies confirmed that they purchased Teramind or ActivTrak at some point during the same time frame.

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Bloomberg Businessweek: Each holiday season since 2015, Amazon.com has counted on selling a lot of its Alexa voice-controlled smart speakers. For almost as long, it's known that the devices have had trouble holding customers' attention even into January. According to internal data, there have been years when 15% to 25% of new Alexa users were no longer active in their second week with the device. Concern about user retention and engagement comes up repeatedly in internal planning documents that Bloomberg Businessweek viewed. The documents, which covered 2018 to 2021, detail Amazon's continued ambitions for Alexa, including plans to add more cameras and sensors that would allow devices to recognize different voices or determine which rooms users are in during each interaction. They also reveal the roadblocks the company sees to realizing these goals. Last year, Amazon's internal analysis of the smart speaker market determined it had "passed its growth phase" and estimated it would expand only 1.2% annually for the next several years.

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U.S. military commanders in the Pacific have built a software tool to predict how the Chinese government will react to U.S. actions in the region like military sales, U.S.-backed military activity and even congressional visits to hotspots like Taiwan. From a report: Deputy Secretary of Defense Kathleen Hicks was briefed on the new tool during a visit to the United States Indo-Pacific Command in Hawaii on Tuesday. "With the spectrum of conflict and the challenge sets spanning down into the grey zone. What you see is the need to be looking at a far broader set of indicators, weaving that together and then understanding the threat interaction," Hicks said in an interview aboard a military jet en route to California. The tool calculates "strategic friction," a defense official said. It looks at data since early 2020 and evaluates significant activities that had impacted U.S.-Sino relations. The computer-based system will help the Pentagon predict whether certain actions will provoke an outsized Chinese reaction. In October, the Chinese military condemned the United States and Canada for each sending a warship through the Taiwan Strait, saying they were threatening peace and stability in the region. The incident and others like it have fueled demand for the tool, the U.S. official said, to ensure the United States does not inadvertently upset China with its actions.

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South Korea will soon roll out a pilot project to use artificial intelligence, facial recognition and thousands of CCTV cameras to track the movement of people infected with the coronavirus, despite concerns about the invasion of privacy. Reuters reports: The nationally funded project in Bucheon, one of the country's most densely populated cities on the outskirts of Seoul, is due to become operational in January, a city official told Reuters. The system uses an AI algorithms and facial recognition technology to analyze footage gathered by more than 10,820 CCTV cameras and track an infected person's movements, anyone they had close contact with, and whether they were wearing a mask, according to a 110-page business plan from the city submitted to the Ministry of Science and ICT (Information and Communications Technology), and provided to Reuters by a parliamentary lawmaker critical of the project. The Bucheon official said the system should reduce the strain on overworked tracing teams in a city with a population of more than 800,000 people, and help use the teams more efficiently and accurately. [...] The Ministry of Science and ICT said it has no current plans to expand the project to the national level. It said the purpose of the system was to digitize some of the manual labour that contact tracers currently have to carry out. The Bucheon system can simultaneously track up to ten people in five to ten minutes, cutting the time spent on manual work that takes around half an hour to one hour to trace one person, the plan said.

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The American military news site Defense One shares a cautionary tale from top U.S. Air Force Major General Daniel Simpson (assistant deputy chief of staff for intelligence, surveillance, and reconnaissance). Simpson describes their experience with an experimental AI-based target recognition program that had seemed to be performing well: Initially, the AI was fed data from a sensor that looked for a single surface-to-surface missile at an oblique angle, Simpson said. Then it was fed data from another sensor that looked for multiple missiles at a near-vertical angle. "What a surprise: the algorithm did not perform well. It actually was accurate maybe about 25 percent of the time," he said. That's an example of what's sometimes called brittle AI, which "occurs when any algorithm cannot generalize or adapt to conditions outside a narrow set of assumptions," according to a 2020 report by researcher and former Navy aviator Missy Cummings. When the data used to train the algorithm consists of too much of one type of image or sensor data from a unique vantage point, and not enough from other vantages, distances, or conditions, you get brittleness, Cummings said. In settings like driverless-car experiments, researchers will just collect more data for training. But that can be very difficult in military settings where there might be a whole lot of data of one type — say overhead satellite or drone imagery — but very little of any other type because it wasn't useful on the battlefield... Simpson said the low accuracy rate of the algorithm wasn't the most worrying part of the exercise. While the algorithm was only right 25 percent of the time, he said, "It was confident that it was right 90 percent of the time, so it was confidently wrong. And that's not the algorithm's fault. It's because we fed it the wrong training data."

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An anonymous reader quotes a report from Politico: Clearview AI has gotten the green light on a federal patent for its facial recognition technology -- an award that the company says is the first to cover a so-called "search engine for faces" that crawls the internet to find matches. Clearview's software -- which scrapes public images from social media to help law enforcement match images in government databases or surveillance footage -- has long faced fire from privacy advocates who say it uses people's faces without their knowledge or consent. Civil rights groups also argue that facial recognition technology is generally error-prone, misidentifying women and minorities at higher rates than it does white men and sometimes leading to false arrests. (A recent audit of Clearview's tech by the Commerce Department's National Institute of Standards and Technology found its results to be highly accurate (PDF), and the company said it knows of no instances to date where the technology has led to a wrongful arrest.) Now, some of those critics fear that codifying Clearview's work with a patent will accelerate the growth of these technologies before legislators or regulators have fully addressed the potential dangers. The U.S. Patent and Trademark Office sent Clearview a "notice of allowance" on Wednesday, meaning the patent will be approved once the company pays certain administrative fees. The patent covers Clearview's "methods of providing information about a person based on facial recognition," including its "automated web crawler" that scans social networking sites and the internet and its algorithms that analyze and match facial images obtained online. "There are other facial recognition patents out there -- that are methods of doing it -- but this is the first one around the use of large-scale internet data," Clearview CEO and co-founder Hoan Ton-That told POLITICO in an exclusive interview. The product uses a database of more than 10 billion photos, Ton-That said, and he has emphasized that "as a person of mixed race, having non-biased technology is important to me." Clearview argues that there is a First Amendment right to make use of public material. "All information in our datasets are all publicly available info that people voluntarily posted online -- it's not anything on your private camera roll," Ton-That said. "If it was all private data, that would be a completely different story." Ton-That said Clearview serves government users only and that "we don't intend to ever make a consumer version of Clearview AI." Yet Clearview says in its patent application that the invention could be useful for other purposes. The company argues that "it may be desirable for an individual to know more about a person that they meet, such as through business, dating, or other relationship." Common ways of learning about new people, like asking them questions or checking out their business cards, may be unreliable because the information they choose to share could be false, the application says. "The part that they're looking to protect is exactly the part that's the most problematic," said Matt Mahmoudi, an Amnesty International researcher who is leading the group's work to ban facial recognition. "They are patenting the very part of it that's in violation of international human rights law." Mahmoudi of Amnesty International said that language in the patent leaves the door open to a cascade of new uses in the future. "It shows a willingness to go down a slippery slope of basically being available in any context," he said.

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The artificial intelligence (AI) program DeepMind has gotten closer to proving a math conjecture that's bedeviled mathematicians for decades and revealed another new conjecture that may unravel how mathematicians understand knots. Live Science reports: The two pure math conjectures are the first-ever important advances in pure mathematics (or math not directly linked to any non-math application) generated by artificial intelligence, the researchers reported Dec. 1 in the journal Nature. [...] The first challenge was setting DeepMind onto a useful path. [...] They focused on two fields: knot theory, which is the mathematical study of knots; and representation theory, which is a field that focuses on abstract algebraic structures, such as rings and lattices, and relates those abstract structures to linear algebraic equations, or the familiar equations with Xs, Ys, pluses and minuses that might be found in a high-school math class. In understanding knots, mathematicians rely on something called invariants, which are algebraic, geometric or numerical quantities that are the same. In this case, they looked at invariants that were the same in equivalent knots; equivalence can be defined in several ways, but knots can be considered equivalent if you can distort one into another without breaking the knot. Geometric invariants are essentially measurements of a knot's overall shape, whereas algebraic invariants describe how the knots twist in and around each other. "Up until now, there was no proven connection between those two things," [said Alex Davies, a machine-learning specialist at DeepMind and one of the authors of the new paper], referring to geometric and algebraic invariants. But mathematicians thought there might be some kind of relationship between the two, so the researchers decided to use DeepMind to find it. With the help of the AI program, they were able to identify a new geometric measurement, which they dubbed the "natural slope" of a knot. This measurement was mathematically related to a known algebraic invariant called the signature, which describes certain surfaces on knots. In the second case, DeepMind took a conjecture generated by mathematicians in the late 1970s and helped reveal why that conjecture works. For 40 years, mathematicians have conjectured that it's possible to look at a specific kind of very complex, multidimensional graph and figure out a particular kind of equation to represent it. But they haven't quite worked out how to do it. Now, DeepMind has come closer by linking specific features of the graphs to predictions about these equations, which are called Kazhdan-Lusztig (KL) polynomials, named after the mathematicians who first proposed them. "What we were able to do is train some machine-learning models that were able to predict what the polynomial was, very accurately, from the graph," Davies said. The team also analyzed what features of the graph DeepMind was using to make those predictions, which got them closer to a general rule about how the two map to each other. This means DeepMind has made significant progress on solving this conjecture, known as the combinatorial invariance conjecture.

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FIFA will trial its new automatic offside technology next week at the Arab Cup as a test to potentially use it at the World Cup in Qatar next year. From a report: As per the Times, the technology relies on an artificial intelligence (AI) system sending an instant message to VAR when a player is offside, with the official then left to determine if a player has interfered with the passage of play or not. The technology will be used at all six stadiums used in the Arab Cup -- which also takes place in Qatar -- and comes after several trials behind closed doors took place at the likes of the Etihad Stadium and the Allianz Arena. Despite being able to relay an instant message to VAR, the technology will only be classed as semi-automated as the verdict will be sent to VAR and not the referee himself.

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The artificial intelligence research company OpenAI will eliminate the waiting list for access to the API of its natural language processing program (NLP) GPT-3. From a report: The move will accelerate access to the world's best-known reading and writing AI model, and is a sign that OpenAI believes the program is safe enough -- and can be monitored sufficiently -- to be disseminated more widely. Developers from supported countries will be able to sign up to access GPT-3's API and begin experimenting immediately, OpenAI said in an announcement Thursday morning. Previously developers had to sit on a waiting list as OpenAI reviewed them before they could even get experimental access. "We've added a lot of improvements across our API and added a number of safety features," says Peter Welinder, VP of products and partnerships at OpenAI. "We think a lot of value can come from getting more developers to build solutions to problems that they see in their environments."

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A year since it was introduced, New York City Council passed a bill earlier this week requiring companies that sell AI technologies for hiring to obtain audits assessing the potential of those products to discriminate against job candidates. The bill requiring "bias audits" passed with overwhelming support in a 38-4 vote. Protocol reports: The bill is intended to weed out the use of tools that enable already unlawful employment discrimination in New York City. If signed into law, it will require providers of automated employment decision tools to have those systems evaluated each year by an audit service and provide the results to companies using those systems. AI for recruitment can include software that uses machine learning to sift through resumes and help make hiring decisions, systems that attempt to decipher the sentiments of a job candidate, or even tech involving games to pick up on subtle clues about someone's hiring worthiness. The NYC bill attempts to encompass the full gamut of AI by covering everything from old-school decision trees to more complex systems operating through neural networks. The legislation calls on companies using automated decision tools for recruitment not only to tell job candidates when they're being used, but to tell them what information the technology used to evaluate their suitability for a job. If signed, the law goes into effect January 2023. Violators could be subject to civil penalties. Notably, the bill "fails to go into detail on what constitutes a bias audit other than to define one as 'an impartial evaluation' that involves testing," reports Protocol. It also doesn't address how well automatic hiring technologies work to remove phony applicants.

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SiliconANGLE reports on why investors poured another $250 million into Cerebras Systems Inc: Enterprises typically use graphics processing units in their AI projects. The fastest GPU on the market today features about 54 billion transistors. Cerebras Systems' chip, the WSE-2, includes 2.6 trillion transistors that the startup says make it the "fastest AI processor on Earth." WSE-2 stands for Wafer Scale Engine 2, a nod to the unique architecture on which the startup has based the processor. The typical approach to chip production is carving as many as several dozen processors into a silicon wafer and then separating them. Cerebras Systems is using a vastly different method: The startup carves a single large processor into the silicon wafer that isn't broken up into smaller units. The 2.6 trillion transistors in the WSE-2 are organized into 850,000 cores... Cerebras Systems says that the WSE-2 has 123 times more cores and 1,000 times more on-chip memory than the closest GPU. The chip's impressive specifications translate into several benefits for customers, according to the startup, most notably increased processing efficiency. To match the performance provided by a WSE-2 chip, a company would have to deploy dozens or hundreds of traditional GPU servers... With the WSE-2, data doesn't have to travel between two different servers but only from one section of the chip to another, which represents a much shorter distance. The shorter distance reduces processing delays. Cerebras Systems says that the result is an increase in the speed at which neural networks can run.

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AI systems being developed to diagnose skin cancer run the risk of being less accurate for people with dark skin, research suggests. From a report: The potential of AI has led to developments in healthcare, with some studies suggesting image recognition technology based on machine learning algorithms can classify skin cancers as successfully as human experts. NHS trusts have begun exploring AI to help dermatologists triage patients with skin lesions. But researchers say more needs to be done to ensure the technology benefits all patients, after finding that few freely available image databases that could be used to develop or "train" AI systems for skin cancer diagnosis contain information on ethnicity or skin type. Those that do have very few images of people with dark skin. Dr David Wen, first author of the study from the University of Oxford, said: "You could have a situation where the regulatory authorities say that because this algorithm has only been trained on images in fair-skinned people, you're only allowed to use it for fair-skinned individuals, and therefore that could lead to certain populations being excluded from algorithms that are approved for clinical use. Alternatively, if the regulators are a bit more relaxed and say: 'OK, you can use it [on all patients]', the algorithms may not perform as accurately on populations who don't have that many images involved in training." That could bring other problems including risking avoidable surgery, missing treatable cancers and causing unnecessary anxiety, the team said.

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A bipartisan group of House lawmakers has introduced a companion to a Senate bill that would let people use algorithm-free versions of tech platforms, according to a copy of the text shared exclusively with Axios. From the report: Recent revelations about Facebook's internal research findings have renewed lawmaker interest in bills that seek to give people more of a say in how algorithms shape their online experiences. The bill shows that anger over how platforms use their algorithms to target users with specialized content is a bipartisan issue with momentum on Capitol Hill. The algorithms that personalize content on social networks and other apps can make services addictive, violate users' privacy and promote extremism, critics and many lawmakers argue. Conservatives have also claimed that services deliberately censor their speech. The Filter Bubble Transparency Act would require internet platforms to let people use a version of their services where content selections are not driven by algorithms. It's sponsored by Reps. Ken Buck (R-Colo.), David Cicilline (D-R.I.), Lori Trahan (D-Mass.) and Burgess Owens (R-Utah). The Senate version of the bill, also bipartisan, is sponsored by Sen. John Thune (R-S.D.), an influential member of Republican leadership. Buck and Cicilline are the bipartisan duo responsible for passing six antitrust bills out of the House Judiciary committee in June. Buck and Thune plan to work together on tech and antitrust issues going forward, a Republican aide told Axios. That could boost the chances of such bills passing muster with Senate Republicans in the future.

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