Rescue of soccer team in Thailand Cave

In July 2018, the world held its breath as an international team of cave divers endeavored to rescue 12 boys and their soccer coach stranded deep in a flooded cave in Thailand. Follow the harrowing operation and discover the scientific ingenuity that made the rescue possible. Hear how rescuers explored every option—from pumping water, to drilling a new exit, to ultimately cave diving with the children through the treacherous, flooded passages. (Premiered November 14, 2018)

Follow the harrowing operation to rescue 12 boys stranded in a flooded cave in Thailand.

Thai Cave Rescue

NOVA Wonders Can We Build a Brain? Hero

Artificially intelligent machines are taking over. They’re influencing our everyday lives in profound and often invisible ways. They can read handwriting, interpret emotions, play games, and even act as personal assistants. They are in our phones, our cars, our doctors’ offices, our banks, our web searches…the list goes on and is rapidly growing ever longer. But how does today’s A.I. actually work—and is it truly intelligent? And for that matter, what is intelligence? The world’s brightest computer programmers are trying to build brighter machines by reverse-engineering the brain and by inventing completely new kinds of computers, with exponentially greater speed and processing power. NOVA Wonders looks at how far we’ve come and where machines are headed as their software becomes ever more…cerebral. How close are we from a world in which computers take over—from diagnosing cancer to driving our cars to targeting weapons? If we place more and more of our lives under the control of these artificial brains, what are we putting at risk?

How does today’s artificial intelligence actually work—and is it truly intelligent?

Can We Build a Brain?

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A two-year analysis of nearly a quarter of a million Twitter users reveals how Americans’ social lives are taking a toll on their sleep schedules.

Scientists Track “Social Jet Lag” Sleeping Habits With Twitter Data

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Tech & Engineering

Koch Foundation

Draper

<div class="rich-text">When Keith Rose joined the Occupy St. Louis movement in 2011, he didn&#x27;t heed the advice of a fellow demonstrator to wear bandanas to cover his face. The police had cameras out, but Rose didn&#x27;t think they were doing much with the photos they were taking.When he joined the Ferguson protests a few years later, it seemed to him that law enforcement had indeed started to put together a file on him. He had not been arrested at that point, but after a few run-ins with police where they knew things like where he had been, who he had been with, or what he had said, he thought it seemed pretty clear that they were monitoring him.</div>
 

 
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 <div class="rich-text">&quot;It really upset me and got me more concerned with surveillance,&quot; Rose says.During the protests, the St. Louis Metropolitan Police Department opened a surveillance hub called the Real Time Crime Center (RTCC). This data center aggregates information gathered through technologies like license plate readers, sensors that can detect and locate gunfire, and cameras spread across the city. Most of RTCC, as well as much of the technology that powers it, was not funded by the city, but through grants and public-private partnerships with companies like Motorola. That approach, according to the American Civil Liberties Union (ACLU), allowed the police department to <a href="https://www.aclu.org/blog/privacy-technology/surveillance-technologies/how-shift-power-police-people">sidestep going to the city&#x27;s elected Board of Aldermen</a> for approval and city funds.&quot;It&#x27;s really disturbing what this says about the future of big tech being able to expand its own power,&quot; Rose says.But with an effort called <a href="https://www.aclu.org/issues/privacy-technology/surveillance-technologies/community-control-over-police-surveillance?redirect=feature/community-control-over-police-surveillance">Community Control Over Police Surveillance</a> (CCOPS), the ACLU is attempting to give citizens more control over the technology that police could use to monitor them. Launched in 2016, CCOPS aims to help local communities draft and pass laws giving them oversight of any surveillance technology that law enforcement agencies or the government want to deploy.The ACLU works with local lawmakers to craft each law, changing it to meet the community&#x27;s specific needs. A dozen cities, plus San Francisco&#x27;s BART system, have passed CCOPS laws. In addition to St. Louis, at least another dozen cities, plus the state of Maine, are in the process of adopting their own versions of the law.St. Louis Alderman John Collins-Muhammad helped <a href="https://www.stlouis-mo.gov/government/city-laws/board-bills/boardbill.cfm?bbDetail=true&amp;BBId=10831&amp;fbclid=IwAR2dISUYbLnHGl-n7Tys97ijbIKg8rbMMWR9xdxZZI24kjj1o0bZZoFWTPU">introduce CCOPS legislation</a> in 2017 and 2018, and recently <a href="https://www.stlouis-mo.gov/government/city-laws/board-bills/boardbill.cfm?bbDetail=true&amp;BBId=13371">reintroduced</a> an updated version. Collins-Muhammad said that he and the rest of the Board got community input and updated each iteration. He thinks the latest version of the bill will pass this year. The law would create a process for board approval of any new surveillance technology, require annual reports for equipment purchases, strengthen sunshine laws around law enforcement data, and call for publishing crime reduction statistics. The bill tries to balance the concerns of both the public and the police, Collins-Muhammad says.&quot;We don&#x27;t want to invade privacy,&quot; he says. &quot;But we also don&#x27;t want to limit the police in doing their jobs.&quot;The St. Louis Metropolitan Police Department has also contributed comments on the bill and has been supportive of it in general, Collins-Muhammad says. In a statement, Keith Barrett, a sergeant with ‎the Department, said that civil liberties and surveillance technology can coexist if used in a responsible manner while still helping police do their job.“The advancement of technological resources continues to allow our department to deter, document, and reduce crime,” Barrett says. “The technology that is deployed continues to aid our efforts to prevent crime and apprehend criminals. We look forward to working with all our community partners in ensuring transparency and accountability.”Privacy Watch, a group of nonprofits in St. Louis, took part in the discussion around the bill. It wanted the law to address issues related to bias with the technology, privacy, freedom of speech, and the cost to taxpayers.&quot;There are so many layers of concern,&quot; says Kendra Tatum, a St. Louis resident and an organizer for the Organization for Black Struggle, a member group of Privacy Watch. &quot;People refer to the phrase ‘Big Brother’ jokingly a lot, but we all know that this type of stuff happens.&quot;Collins-Muhammad says he knows the threat personally, having experienced surveillance when he was an activist during the Ferguson protests. He said it was “scary” and got him interested in creating policy that could limit that experience for others in the future.&quot;This is something very important to me,&quot; he says.<h3>An AI Panopticon</h3>While the Real Time Crime Center doesn&#x27;t use AI-powered tools yet, it is technically easy to overlay face recognition software after cameras are in use. The proposed CCOPS legislation in St. Louis wouldn&#x27;t outlaw the technology, but would require law enforcement to go through the Board of Aldermen if they want to use it.AI-powered surveillance technology has <a href="https://carnegieendowment.org/publications/interactive/ai-surveillance">spread across the world quickly</a>. ACLU&#x27;S <a href="https://www.aclu.org/report/dawn-robot-surveillance">Dawn of Robot Surveillance</a> report points out that this is occurring as camera technology is improving with higher resolution and night-vision capabilities. At the same time, more cities are installing centralized surveillance systems, with about <a href="https://www.sdmmag.com/articles/92407-rise-of-surveillance-camera-installed-base-slows">350 million surveillance cameras</a> across the world in 2016.
 <figure class="richtext-image full-width"><img alt="surveillance2.jpg" class="richtext-image full-width" height="450" width="800"
 src="/wgbh/nova/media/images/surveillance2.width-800.jpg"/><figcaption>With artificial intelligence, a lot of surveillance work can be automated, making AI game-changing technology. Image Credit: iStock, andreusK<div class="credit"></div></figcaption>
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 The potential of facial recognition technology is perhaps most prominently on display in China. The government not only uses it to give out traffic tickets, and has passed laws mandating face scans for those signing up for internet or a new phone number, but has put it to work <a href="https://www.nytimes.com/2019/04/14/technology/china-surveillance-artificial-intelligence-racial-profiling.html">monitoring and imprisoning the Uighur minority</a>. By 2020, China plans to have more than <a href="https://www.cbsnews.com/news/china-social-credit-system-surveillance-cameras/">600 million cameras operating</a>.Beyond concerns over increased surveillance, facial recognition software has been shown to perform less well on dark skin. In a 2018 report called &quot;<a href="http://gendershades.org/">Gender Shades</a>,&quot; researchers Joy Buolamwini and Timnit Gebru found that three commercially available systems performed no better than chance at identifying women with darker skin tones.The ACLU did its own study showing that Rekognition, Amazon&#x27;s face recognition software, <a href="https://www.aclu.org/blog/privacy-technology/surveillance-technologies/amazons-face-recognition-falsely-matched-28">misidentified 28 members of Congress</a>. Forty percent of the algorithm’s mistakes were made in attempting to identify people of color, even though they make up only 20 percent of Congress. Amazon disputed the study and showed that with the recommended higher confidence threshold, which only lets the algorithm offer up matches if it is very sure it is right, the misidentifications disappear. However, one of the police departments<a href="https://gizmodo.com/defense-of-amazons-face-recognition-tool-undermined-by-1832238149">testing the software admitted</a> to not setting any confidence threshold.That isn&#x27;t the only case of police departments using software in a way that the developers didn&#x27;t intend. A study by Georgetown Law Center on Privacy and Technology <a href="https://www.flawedfacedata.com/">found that police fed celebrity photos</a> into face recognition software, along with sketches and photoshopped images.There is a long history of surveillance in the U.S. according to Matt Cagle, an attorney at the ACLU of Northern California. Public and civil rights groups like the ACLU have fought against much of it, with varying levels of success. With AI, however, a lot of surveillance work can be automated, making AI game-changing technology.&quot;If they can watch us, they can control us,&quot; Cagle says.In fact, the face recognition conversation can be part of the larger conversation about government surveillance according to Chad Marlow, a senior advocacy and policy counsel at the ACLU, who is also overseeing CCOPS. The proliferation of technology like surveillance cameras and Stingrays—which mimic cell phone towers and send out signals that trick nearby phones into transmitting identifying information and data about their locations—can be reevaluated. Also, anything new developed, with or without AI capabilities, can be integrated in a way that the community is aware of and has some control over.“In that way, we aren’t playing whack-a-mole,” Marlow says.</div>
 

 
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 <div class="rich-text"><h3>Drawbacks of Bans</h3>Four cities have gone further than community oversight with their CCOPS laws: Berkeley, Oakland, San Francisco, and Somerville, Massachusetts have outright banned face recognition by the government. Earlier this year, the ACLU, along with another 60 groups, <a href="https://www.aclu.org/letter/coalition-letter-calling-federal-moratorium-face-recognition">sent a letter to Congress</a> asking for a federal moratorium on the use of facial recognition for law enforcement and immigration enforcement until Congress fully debates which practices should be allowed.“This capability threatens to create a world where people are watched and identified as they attend a protest, congregate outside a place of worship, visit a medical provider, or simply go about their daily lives,” the letter said.But Daniel Castro, vice president of the Information Technology and Innovation Foundation and director of the Center for Data Innovation, says cities could also end up blocking better potential uses of face recognition tools out of fear of a surveillance state.&quot;We&#x27;re not there yet,&quot; he says. &quot;A lot of the concerns there are premature.&quot;Other law enforcement applications of the technology, like finding suspects or missing children, can be made safer with laws that create guardrails, like establishing accuracy rates for any software used. The areas that do matter get conflated with broader issues, like whether the police should be surveilling protestors at all, Castro said.&quot;We can separate some of these issues without banning the technology,&quot; he says.CCOPS laws only regulate government use of technology, and don&#x27;t stop private companies from using it. St. Louis has a handful of businesses, like corner stores, using cameras run by a local company called Blue Line Technology that says it <a href="https://www.kmov.com/news/local-former-cops-create-facial-recognition-technology-to-fight-crime/article_e7694d3a-7689-11e9-ab41-939fb5bbfa86.html">uses face recognition</a>. Another company called Persistent Surveillance Systems (PSS) <a href="https://fox2now.com/2019/07/31/surveillance-company-offers-st-louis-6-6-million-crimefighting-tools-for-free/">offered millions of dollars in crime-fighting technology</a> to St. Louis for free.But if the police wanted to plug AI-powered cameras from private property into their Real Time Crime Center or take PSS up on their offer, CCOPS would kick in, forcing the police to bring the project to the Board of Aldermen, points out Marlow. Then the community and its elected officials would decide the future they want to see in their city.&quot;CCOPS sets up the conversation; it isn&#x27;t the conversation itself,&quot; he says.</div>
 

 
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As law enforcement agencies deploy AI-powered facial recognition systems, some communities are pushing back, insisting on having a say in how they’re used.

Communities come face-to-face with the growing power of facial recognition technology

See the “Mona Lisa” as da Vinci once did.
An engineer used powerful lights, cameras, and computer software to digitally remove the varnish from the &quot;Mona Lisa,&quot; allowing people today to see the painting as Leonardo da Vinci created it.
Giving the &quot;Mona Lisa&quot; a Digital MakeoverPublished: November 22, 2019Walter Isaacson: If we could just take that varnish off, we could see it the way Leonardo really did it. But I think French governments have fallen for less cause than trying to take the Mona Lisa out of circulation and clean it.Narrator: Perhaps there is another way. Could she be given a digital makeover?That is what Pascal Cotte is trying to do. He has analyzed the &quot;Mona Lisa&quot; with a remarkably powerful camera and lights, which he demonstrates using a replica.Pascal Cotte: We make the measurements in the basement of the Louvre, inside the laboratory. It’s very emotional to have the painting in your hands without the frame. You can look at this painting under this very intense light, that reveal everything that you cannot see, usually.Narrator: Cotte’s extremely detailed scan of the &quot;Mona Lisa&quot; and his analysis of the optics and chemistry of paint reveal how the colors may have changed over time. It’s not just the varnish that yellows and darkens, but the pigments and oil in the paint itself.Cotte’s challenge is to reverse engineer the effects of that aging.Martin Kemp: This is not just photoshopping it and messing around with the colors, which you and I could do and get tolerable results, but this is based on pigment analysis.Narrator: First, Cotte determines how much the varnish has darkened and with his computer, peels it away. Next, he identifies what the color of each pigment would have looked like 500 years ago and recreates them to see the colors just as Leonardo did. Cotte: For example, we know that Leonardo make the sky with white lead and lapis lazuli. So, we have a software that removes the wrong colors to obtain the genuine color.Narrator: Then, pixel by pixel, Cotte restores those colors.Suddenly, a greenish sky becomes brilliantly blue, and a bit of flush comes back to Lisa’s cheeks. Finally, as the French say, “voila!”Kemp: Suddenly she doesn’t look like a submarine goddess. She looks as if she’s in the fresh air, which is just terrific.Narrator: Cotte’s restoration has brought Lisa back to life, at least digitally closer to the state that Leonardo saw her.

See the “Mona Lisa” as da Vinci once did.

Giving the "Mona Lisa" a Digital Makeover

An algorithm helps find buried lead, but can residents trust it?
Researchers are turning to AI to give workers the best chance of finding dangerous lead in Flint.
Artificial Intelligence is Helping Get the Lead Out of FlintPublished: November 19, 2019Gina Luster: We could be getting water from heaven but our infrastructure is so destroyed, by the time it makes it to me and my family, it’s no good. Onscreen: In 2014, Flint, Michigan changed water sources but didn’t properly treat the new water to prevent corroding water pipes resulting in eroded lead pipes and contaminated water across the cityLuster: I will never trust what anyone says as far as the condition and quality of the water. In the back of my head, I&#x27;ll always be wondering.Onscreen: Officials denied that the water was unsafe for more than a year until activism by Flint residents brought national attention and pressure.In 2016 the city began to replace the lead pipes. But there was a huge problem. They didn’t know where the thousands of lead pipes were.Jacob Abernethy: The city had essentially no records of what these lines were. The vast majority of the lead pipes were in the public portion of the line.The problem is that the line is buried entirely underground. There is no way to find out what the material is unless you dig six feet underground and that’s very expensiveOnscreen: To give workers the best chance of finding the lead, U. of Michigan researchers turned to a form of artificial intelligence (AI), that searches large collections of data looking for patterns. It’s called “machine learning” Abernethy: It first would check, is the age of the property above this value? Then, is the home located in this neighborhood? Then, has this home had a water test?Onscreen: Using census data and city records, the algorithm makes conclusions based on relationships it finds in the data.Abernethy: It takes all these little pieces of information and combines it into a single number which is a probability of a home having a dangerous pipe. Onscreen: The more information the ai model got about pipes in Flint, the more accurate the predictions became.Abernethy: When this project started, we had a total of 36 homes where we actually knew the service line material. Now we have over 20,000 homes. Once you go from 36 to 20,000, the accuracy of a machine learning algorithm increases dramatically.Onscreen: The city of Flint started using the AI model to prioritize digging at homes that most likely had lead pipes. In 2016-2017, the algorithm successfully predicted which homes had lead about 81% of the time. Abernethy: That was a decent hit rate, it wasn&#x27;t perfect.Onscreen: But many residents objected to prioritizing some homes over others. Maurice Davis: When it&#x27;s something as devastating as the lead in this water, we don’t need predictions, we need 100% accuracy to right this wrong.We need to actually dig holes, excavate the holes, look down in that hole and see copper-to-copper or  lead-to-copper. You come and replace my neighbor’s but you don’t replace mine according to a prediction? That don’t help nobody’s confidence.Onscreen: In 2018, the City hired a California-based contractor to accelerate replacement of lead pipes.They stopped using AI to target specific homes and took a more blanket approach, inspecting neighborhoods across the city.Abernethy: They did a less targeted, less focused approach Onscreen: But only found lead in about 15 percent of the homes inspected Abernethy: When it became clear that the hit rate of this new contracting firm had gone down so dramatically, people were concerned about that and were trying to figure out why finite government resources were being used to try to replace pipes in homes that actually didn&#x27;t require replacement.Onscreen: A coalition of activists sought a court order demanding that the city first inspect homes expected to have lead.  A settlement required the city to use the AI model. Now the hit rate has climbed back up, to over 70%. Still, many residents want physical confirmation that their pipes are safe.Luster: Once all of these agencies that are supposed to be put in place to protect you deceive you and lie to you, how do you ever gain that trust back? The damage is permanently done to our bodies, our infrastructure. You can never forget that. Our pipes haven’t been changed yet. I don’t really know what our service line pipes are. I want a person down there with a shovel, literally down there touching that pipe.Eric Schwartz: There’s really no substitute for seeing someone dig in your front lawn, removing that lead pipe, and replacing it with a fresh copper pipe.The goal is really just take advantage of all the information we have and remove the lead from the homes that actually have lead as quickly as possible so that no residents in Flint have to continue living with lead more than five years after the water has been switched.Onscreen: More than 8,000 lead pipes have been replaced so far. No one knows how many remain. Researchers estimate there could be about 1,921 lead pipes remainingCrowd: Five years later, Flint is not fixedE. Yvonne Coleman-Lewis: There are things that will never change in my life or my family’s life as a result of this. Until the water system is completely replaced, there will be little, if any, true trust in the water that comes from the tap.Onscreen: At least 12 residents died from illnesses related to the water crisis in Flint Crowd: Rest in peace. All of you fallen soldiers. That’s what you are.Onscreen: It’s estimated that in 2014-2015 alone, 140,000 people were exposed to dangerous levels of lead in Flint’s water including around 9,000 children.

An algorithm helps find buried lead, but can residents trust it?

Artificial Intelligence is Helping Get the Lead Out of Flint

Scientists are using artificial cervices and 3D models of the uterus to better understand pregnancy and childbirth.

How mechanical engineering could revolutionize the study of preterm birth

Learn what inspired da Vinci’s &quot;Mona Lisa.&quot;
To master Mona Lisa&#x27;s form, da Vinci carefully studied the human eye and mouth—and he dissected at least 30 human bodies.
How did Leonardo da Vinci Paint the &quot;Mona Lisa&quot;?Published: November 11, 2019Onscreen: How did Leonardo da Vinci paint the &quot;Mona Lisa&quot;?Walter Isaacson: In order to paint the &quot;Mona Lisa&quot;, Leonardo dissected at least 30 human bodies. He probably did not need to do all of those dissections and all of those studies of the eye and mouth to get the smile right. But he was Leonardo. He just wanted to know everything he could.It’s that ability to see patterns across nature that makes smart people into creative people.In the &quot;Mona Lisa&quot;, you see every pattern he loves—including how water swirls. So you see in that picture the curve of the river, connecting to a curving road, connecting almost literally to the Mona Lisa. As he’s saying that the veins of the Earth connect to the veins of the human.And that’s the cool thing about Leonardo. He’s always blending this notion of pure science but also mystery, and even miracles.In order to be a genius, it’s not enough to be really intelligent. In fact, there are a lot of really intelligent people who don’t have creativity. They don’t “think different.” They don’t have an innovative way or inventive way of looking at things.I think in order to be a genius, you have to think out of the box at times. You have to make leaps that others wouldn’t see. That’s what a lifetime spent connecting arts and sciences and humanities and everything you could know that leads up to being able to paint the &quot;Mona Lisa&quot;.

Learn what inspired da Vinci’s &quot;Mona Lisa.&quot;

How did Leonardo da Vinci Paint the "Mona Lisa"?

<div class="rich-text">In the last decade or so, artificial intelligence has found its way into just about every technology-heavy sector of society. From music recommendation services to targeted advertising, machines around the world have learned how to pore through gobs of data, identify the patterns within, and spit out predictions—a strategy that, in theory, streamlines an otherwise arduous process.There’s just one problem: Across a bevy of applications, artificial intelligence <a href="https://www.washingtonpost.com/technology/2019/10/22/ai-hiring-face-scanning-algorithm-increasingly-decides-whether-you-deserve-job/?tid=lk_inline_manual_9">has a serious</a> <a href="https://www.vice.com/en_us/article/8xzwgx/racial-bias-in-ai-isnt-getting-better-and-neither-are-researchers-excuses">racial bias problem</a>.Today, researchers announce the latest example in a <a href="https://science.sciencemag.org/content/366/6464/447">study</a> published in the journal Science. Their findings show a widely used medical algorithm that predicts who might benefit from follow-up care drastically underestimates the health needs of black patients—even when they’re sicker than their white counterparts.Removing the racial bias in the algorithm, which is one of several used in hospitals and by insurance companies around the country, could more than double the number of black patients who are deemed eligible for additional medical support, the researchers found.</div>
 

 
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 <div class="rich-text">Optum, the health services company that sells the algorithm, is now working with the team behind the study to rectify the issue. But the problem is probably widespread among the many organizations that provide health care in the United States, where programs like these affect more than 200 million people, the researchers write.“It’s truly inconceivable to me that anyone else’s algorithm doesn’t suffer from this,” study author Sendhil Mullainathan, a computational scientist at the University of Chicago Booth School of Business, <a href="https://www.washingtonpost.com/health/2019/10/24/racial-bias-medical-algorithm-favors-white-patients-over-sicker-black-patients/">told Carolyn Y. Johnson at The Washington Post</a>. “I’m hopeful that this causes the entire industry to say, ‘Oh, my, we’ve got to fix this.’”The algorithm in question, Impact Pro, is estimated to affect <a href="https://www.statnews.com/2019/10/24/widely-used-algorithm-hospitals-racial-bias/">100 million Americans</a>. It’s designed to scan patients’ bills and insurance payouts, then assign “risk scores” to patients, tabulating the urgency of their need for additional or specialized treatment. A big part of the algorithm’s strategy, the researchers found, relies on the assumption that people who spend less on health care are more well.But many other studies show that this is simply untrue, study author Ziad Obermeyer, a health policy researcher at the University of California, Berkeley, <a href="https://www.sciencemag.org/news/2019/10/hospital-risk-scores-prioritize-white-patients">told Michael Price at Science</a>. Black patients, he explains, are <a href="https://www.washingtonpost.com/news/wonk/wp/2015/10/05/medicines-racial-divide-measured-in-minutes-spent-waiting-to-see-a-doctor/?tid=lk_inline_manual_12">less likely</a> than white patients to purchase medical services for the same conditions, due in part to unequal access to care and historical distrust in health providers.None of this was accounted for in the algorithm. As a result, people with similar scores weren’t on level medical ground. Compared to white patients with similar “risk,” black patients suffered from more illnesses and conditions like cancer, diabetes, and high blood pressure. All told, the algorithm had missed nearly 50,000 chronic conditions in black patients—simply because they spent less on treatment.The system then helped perpetuate the cycle that had compromised its performance. By flagging patients who were already more likely to spend on health care—typically, wealthy white people—as candidates for additional treatment, it likely widened an existing racial disparity, the researchers say.In their paper, the researchers also report a straightforward fix for the issue. They trained a new algorithm that scoured biological, rather than financial, data, and predicted future health conditions instead of spending. This reduced the number of missed chronic conditions in black patients to 8,000—an 84% drop.</div>
 

 
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 <div class="rich-text">“Predictive algorithms that power these tools should be continually reviewed and refined, and supplemented by information such as socioeconomic data, to help clinicians make the best-informed care decisions for each patient,” Optum spokesman Tyler Mason told Johnson. “As we advise our customers, these tools should never be viewed as a substitute for a doctor’s expertise and knowledge of their patients’ individual needs.”The trouble may be identifying these issues in the first place. Prediction algorithms can be both complex and obscure—and across fields, researchers caution that these programs are <a href="https://www.pbs.org/wgbh/nova/article/infectious-disease-zoonotic-model/">only as good</a> as the data they’re fed.“It’s important that we understand the data the algorithms are trained on,” Milena Gianfrancesco, an epidemiologist at the University of California, San Francisco who wasn’t involved in the study, told Price. “An algorithm built and used blindly on [racial] disparities certainly has the potential to further racial biases in health care.”But, as experts have <a href="https://www.pbs.org/wgbh/nova/article/radical-ideas-social-media-algorithms/">pointed out before</a>, the problem perhaps goes back to us, the humans who built machines and asked them to learn in our stead. We have a shot at catching our own biases before they’re passed on; the same isn’t (yet) true of algorithms. As Mullainathan <a href="https://www.statnews.com/2019/10/24/widely-used-algorithm-hospitals-racial-bias/">told Shraddha Chakradhar at STAT News</a>, “Concepts that we as humans tend to take synonymously—like care in dollars and care in biological terms—algorithms take them literally....But there’s a difference between them, especially for black patients versus white patients.”</div>
 

 
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The algorithm was based on the faulty assumption that health care spending is a good proxy for wellbeing. But there seems to be a quick fix.

Racially-biased medical algorithm prioritizes white patients over black patients

Discover the science behind Leonardo da Vinci&#x27;s masterpieces—and Mona Lisa&#x27;s iconic smile.

Topic: Tech & Engineering

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