Covid-19 Data Is a Mess. We Need a Way to Make Sense of It.

Curated on Posted on by Stefaan Verhulst

Beth Blauer and Jennifer Nuzzo in the New York Times: “The United States is more than eight months into the pandemic and people are back waiting in long lines to be tested as coronavirus infections surge again. And yet there is still no federal standard to ensure testing results are being uniformly reported. Without uniform results, it is impossible to track cases accurately or respond effectively.

We test to identify coronavirus infections in communities. We can tell if we are casting a wide enough net by looking at test positivity — the percentage of people whose results are positive for the virus. The metric tells us whether we are testing enough or if the transmission of the virus is outpacing our efforts to slow it.

If the percentage of tests coming back positive is low, it gives us more confidence that we are not missing a lot of infections. It can also tell us whether a recent surge in cases may be a result of increased testing, as President Trump has asserted, or that cases are rising faster than the rate at which communities are able to test.

But to interpret these results properly, we need a national standard for how these results are reported publicly by each state. And although the Centers for Disease Control and Prevention issue protocols for how to report new cases and deaths, there is no uniform guideline for states to report testing results, which would tell us about the universe of people tested so we know we are doing enough testing to track the disease. (Even the C.D.C. was found in May to be reporting states’ results in a way that presented a misleading picture of the pandemic.)

Without a standard, states are deciding how to calculate positivity rates on their own — and their approaches are very different.

Some states include results from positive antigen-based tests, some states don’t. Some report the number of people tested, while others report only the number of tests administered, which can skew the overall results when people are tested repeatedly (as, say, at colleges and nursing homes)….(More)”

tl;dr: this AI sums up research papers in a sentence

Curated on Posted on by Stefaan Verhulst

Jeffrey M. Perkel & Richard Van Noorden at Nature: “The creators of a scientific search engine have unveiled software that automatically generates one-sentence summaries of research papers, which they say could help scientists to skim-read papers faster.

The free tool, which creates what the team calls TLDRs (the common Internet acronym for ‘Too long, didn’t read’), was activated this week for search results at Semantic Scholar, a search engine created by the non-profit Allen Institute for Artificial Intelligence (AI2) in Seattle, Washington. For the moment, the software generates sentences only for the ten million computer-science papers covered by Semantic Scholar, but papers from other disciplines should be getting summaries in the next month or so, once the software has been fine-tuned, says Dan Weld, who manages the Semantic Scholar group at AI2…

Weld was inspired to create the TLDR software in part by the snappy sentences his colleagues share on Twitter to flag up articles. Like other language-generation software, the tool uses deep neural networks trained on vast amounts of text. The team included tens of thousands of research papers matched to their titles, so that the network could learn to generate concise sentences. The researchers then fine-tuned the software to summarize content by training it on a new data set of a few thousand computer-science papers with matching summaries, some written by the papers’ authors and some by a class of undergraduate students. The team has gathered training examples to improve the software’s performance in 16 other fields, with biomedicine likely to come first.

The TLDR software is not the only scientific summarizing tool: since 2018, the website Paper Digest has offered summaries of papers, but it seems to extract key sentences from text, rather than generate new ones, Weld notes. TLDR can generate a sentence from a paper’s abstract, introduction and conclusion. Its summaries tend to be built from key phrases in the article’s text, so are aimed squarely at experts who already understand a paper’s jargon. But Weld says the team is working on generating summaries for non-expert audiences….(More)”.

‘It gave me hope in democracy’: how French citizens are embracing people power

Curated on Posted on by Stefaan Verhulst

Peter Yeung at The Guardian: “Angela Brito was driving back to her home in the Parisian suburb of Seine-et-Marne one day in September 2019 when the phone rang. The 47-year-old caregiver, accustomed to emergency calls, pulled over in her old Renault Megane to answer. The voice on the other end of the line informed her she had been randomly selected to take part in a French citizens’ convention on climate. Would she, the caller asked, be interested?

“I thought it was a real prank,” says Brito, a single mother of four who was born in the south of Portugal. “I’d never heard anything about it before. But I said yes, without asking any details. I didn’t believe it.’”

Brito received a letter confirming her participation but she still didn’t really take it seriously. On 4 October, the official launch day, she got up at 7am as usual and, while driving to meet her first patient of the day, heard a radio news item on how 150 ordinary citizens had been randomly chosen for this new climate convention. “I said to myself, ah, maybe it was true,” she recalls.

At the home of her second patient, a good-humoured old man in a wheelchair, the TV news was on. Images of the grand Art Déco-style Palais d’Iéna, home of the citizens’ gathering, filled the screen. “I looked at him and said, ‘I’m supposed to be one of those 150,’” says Brito. “He told me, ‘What are you doing here then? Leave, get out, go there!’”

Brito had two hours to get to the Palais d’Iéna. “I arrived a little late, but I arrived!” she says.

Over the next nine months, Brito would take part in the French citizens’ convention for the climate, touted by Emmanuel Macron as an “unprecedented democratic experiment”, which would bring together 150 people aged 16 upwards, from all over France and all walks of French life – to learn, debate and then propose measures to reduce greenhouse gas emissions by at least 40% by 2030. By the end of the process, Brito and her fellow participants had convinced Macron to pledge an additional €15bn (£13.4bn) to the climate cause and to accept all but three of the group’s 149 recommendations….(More)”.

Facial-recognition research needs an ethical reckoning

Curated on Posted on by Stefaan Verhulst

Editorial in Nature: “…As Nature reports in a series of Features on facial recognition this week, many in the field are rightly worried about how the technology is being used. They know that their work enables people to be easily identified, and therefore targeted, on an unprecedented scale. Some scientists are analysing the inaccuracies and biases inherent in facial-recognition technology, warning of discrimination, and joining the campaigners calling for stronger regulation, greater transparency, consultation with the communities that are being monitored by cameras — and for use of the technology to be suspended while lawmakers reconsider where and how it should be used. The technology might well have benefits, but these need to be assessed against the risks, which is why it needs to be properly and carefully regulated.Is facial recognition too biased to be let loose?

Responsible studies

Some scientists are urging a rethink of ethics in the field of facial-recognition research, too. They are arguing, for example, that scientists should not be doing certain types of research. Many are angry about academic studies that sought to study the faces of people from vulnerable groups, such as the Uyghur population in China, whom the government has subjected to surveillance and detained on a mass scale.

Others have condemned papers that sought to classify faces by scientifically and ethically dubious measures such as criminality….One problem is that AI guidance tends to consist of principles that aren’t easily translated into practice. Last year, the philosopher Brent Mittelstadt at the University of Oxford, UK, noted that at least 84 AI ethics initiatives had produced high-level principles on both the ethical development and deployment of AI (B. Mittelstadt Nature Mach. Intell. 1, 501–507; 2019). These tended to converge around classical medical-ethics concepts, such as respect for human autonomy, the prevention of harm, fairness and explicability (or transparency). But Mittelstadt pointed out that different cultures disagree fundamentally on what principles such as ‘fairness’ or ‘respect for autonomy’ actually mean in practice. Medicine has internationally agreed norms for preventing harm to patients, and robust accountability mechanisms. AI lacks these, Mittelstadt noted. Specific case studies and worked examples would be much more helpful to prevent ethics guidance becoming little more than window-dressing….(More)”.

Digital Democracy’s Road Ahead

Curated on Posted on by Stefaan Verhulst

Richard Hughes Gibson at the Hedgehog Review: “In the last decade of the twentieth century, as we’ve seen, Howard Rheingold and William J. Mitchell imagined the Web as an “electronic agora” where netizens would roam freely, mixing business, pleasure, and politics. Al Gore envisioned it as an “information superhighway” system for which any computer could offer an onramp. Our current condition, by contrast, has been likened to shuffling between “walled gardens,” each platform—be it Facebook, Apple, Amazon, or Google—being its own tightly controlled ecosystem. Yet even this metaphor is perhaps too benign. As the cultural critic Alan Jacobs has observed, “they are not gardens; they are walled industrial sites, within which users, for no financial compensation, produce data which the owners of the factories sift and then sell.”

Harvard Business School professor Shoshanna Zuboff has dubbed the business model underlying these factories “surveillance capitalism.” Surveillance capitalism works by collecting information about you (your Internet activity, call history, app usage, your voice, your location, even your fitness level), which creates profiles of what you like, where you go, who you know, and who you are. That shadowy portrait makes a powerful tool for predicting what kinds of products and services you might like to purchase, and other companies are happy to pay for such finely-tuned targeted advertising. (Facebook alone generated $69 billion in ad revenue last year.)

The information-gathering can’t ever stop, however; the business model depends on a steady supply of new user data to inform the next round of predictions. This “extraction imperative,” as Zuboff calls it, is inherently monopolistic, rival companies being both a threat that must be eliminated and a potential gold mine from which more user data can be extracted (see Facebook’s acquisitions of competitors Whatsapp and Instagram). Equally worryingly, the big tech companies have begun moving into other sectors of the economy, as seen, for example, in Google’s quiet entry last year into the medical records business (unbeknownst to the patients and physicians whose data was mined).

There is growing consensus among legal scholars and social scientists that these practices are hazardous to democracy. Commentators worry over the consequences of putting so much wealth in so few hands so quickly (Zuboff calls it a “new Gilded Age”). They note the number of tech executives who’ve gone on to high-ranking government posts and vice versa. They point to the fact that—contrary to Mark Zuckerberg’s 2010 declaration that privacy is no longer a “social norm”—users are indeed worried about privacy. Scholars note, furthermore, that these platforms are not a genuine reflection of public opinion, though they are often treated as such. Social media can operate as echo chambers, only showing you what people like you read, think, do. Paradoxically, they can also become pressure cookers. As is now widely documented, many algorithms reward—and thereby amplify—the most divisive and thus most attention-grabbing content. Keeping us dialed in—whether for the next round of affirmation or outrage—is essential to their success….(More)”.

A nudge helps doctors bring up end-of-life issues with their dying cancer patients

Curated on Posted on by Stefaan Verhulst

Article by Ravi Parikh et al: “When conversations about goals and end-of-life wishes happen early, they can improve patients’ quality of life and decrease their chances of dying on a ventilator or in an intensive care unit. Yet doctors treating cancer focus so much of their attention on treating the disease that these conversations tend to get put off until it’s too late. This leads to costly and often unwanted care for the patient.Related: 

This can be fixed, but it requires addressing two key challenges. The first is that it is often difficult for doctors to know how long patients have left to live. Even among patients in hospice care, doctors get it wrong nearly 70% of the time. Hospitals and private companies have invested millions of dollars to try and identify these outcomes, often using artificial intelligence and machine learning, although most of these algorithms have not been vetted in real-world settings.

In a recent set of studies, our team used data from real-time electronic medical records to develop a machine learning algorithm that identified which cancer patients had a high risk of dying in the next six months. We then tested the algorithm on 25,000 patients who were seen at our health system’s cancer practices and found it performed better than relying only on doctors to identify high-risk patients.

But just because such a tool exists doesn’t mean doctors will use it to prompt more conversations. The second challenge — which is even harder to overcome — is using machine learning to motivate clinicians to have difficult conversations with patients about the end of life.

We wondered if implementing a timely “nudge” that doctors received before seeing their high-risk patients could help them start the conversation.

To test this idea, we used our prediction tool in a clinical trial involving nine cancer practices. Doctors in the nudge group received a weekly report on how many end-of-life conversations they had compared to their peers, along with a list of patients they were scheduled to see the following week who the algorithm deemed at high-risk of dying in the next six months. They could review the list and uncheck any patients they thought were not appropriate for end-of-life conversations. For the patients who remained checked, doctors received a text message on the day of the appointment reminding them to discuss the patient’s goals at the end of life. Doctors in the control group did not receive the email or text message intervention.

As we reported in JAMA Oncology, 15% of doctors who received the nudge text had end-of-life conversations with their patients, compared to just 4% of the control doctors….(More)”.

The Few, the Tired, the Open Source Coders

Curated on Posted on by Stefaan Verhulst

Article by Clive Thompson: “…When the open source concept emerged in the ’90s, it was conceived as a bold new form of communal labor: digital barn raisings. If you made your code open source, dozens or even hundreds of programmers would chip in to improve it. Many hands would make light work. Everyone would feel ownership.

Now, it’s true that open source has, overall, been a wild success. Every startup, when creating its own software services or products, relies on open source software from folks like Thornton: open source web-server code, open source neural-net code. But, with the exception of some big projects—like Linux—the labor involved isn’t particularly communal. Most are like Bootstrap, where the majority of the work landed on a tiny team of people.

Recently, Nadia Eghbal—the head of writer experience at the email newsletter platform Substack—published Working in Public, a fascinating book for which she spoke to hundreds of open source coders. She pinpointed the change I’m describing here. No matter how hard the programmers worked, most “still felt underwater in some shape or form,” Eghbal told me.

Why didn’t the barn-raising model pan out? As Eghbal notes, it’s partly that the random folks who pitch in make only very small contributions, like fixing a bug. Making and remaking code requires a lot of high-level synthesis—which, as it turns out, is hard to break into little pieces. It lives best in the heads of a small number of people.

Yet those poor top-level coders still need to respond to the smaller contributions (to say nothing of requests for help or reams of abuse). Their burdens, Eghbal realized, felt like those of YouTubers or Instagram influencers who feel overwhelmed by their ardent fan bases—but without the huge, ad-based remuneration.

Sometimes open source coders simply walk away: Let someone else deal with this crap. Studies suggest that about 9.5 percent of all open source code is abandoned, and a quarter is probably close to being so. This can be dangerous: If code isn’t regularly updated, it risks causing havoc if someone later relies on it. Worse, abandoned code can be hijacked for ill use. Two years ago, the pseudonymous coder right9ctrl took over a piece of open source code that was used by bitcoin firms—and then rewrote it to try to steal cryptocurrency….(More)”.

Google launches new tool to help cities stay cool

Curated on Posted on by Stefaan Verhulst

Article by Justine Calma: “Google unveiled a tool today that could help cities keep their residents cool by mapping out where trees are needed most. Cities tend to be warmer than surrounding areas because buildings and asphalt trap heat. An easy way to cool metropolitan areas down is to plant more trees in neighborhoods where they’re sparse.

Google’s new Tree Canopy Lab uses aerial imagery and Google’s AI to figure out where every tree is in a city. Tree Canopy Lab puts that information on an interactive map along with additional data on which neighborhoods are more densely populated and are more vulnerable to high temperatures. The hope is that planting new trees in these areas could help cities adapt to a warming world and save lives during heat waves.

Google piloted Tree Canopy Lab in Los Angeles. Data on hundreds more cities is on the way, the company says. City planners interested in using the tool in the future can reach out to Google through a form it posted along with today’s announcement.

“We’ll be able to really home in on where the best strategic investment will be in terms of addressing that urban heat,” says Rachel Malarich, Los Angeles’ first city forest officer.

Google claims that its new tool can save cities like Los Angeles time when it comes to taking inventory of their trees. That’s often done by sending people to survey each block. Los Angeles has also used LIDAR technology to map their urban forest in the past, which uses a laser sensor to detect the trees — but that process was expensive and slow, according to Malarich. Google’s new service, on the other hand, is free to use and will be updated regularly using images the company already takes by plane for Google Maps….(More)”.

How the U.S. Military Buys Location Data from Ordinary Apps

Curated on Posted on by Stefaan Verhulst

Joseph Cox at Vice: “The U.S. military is buying the granular movement data of people around the world, harvested from innocuous-seeming apps, Motherboard has learned. The most popular app among a group Motherboard analyzed connected to this sort of data sale is a Muslim prayer and Quran app that has more than 98 million downloads worldwide. Others include a Muslim dating app, a popular Craigslist app, an app for following storms, and a “level” app that can be used to help, for example, install shelves in a bedroom.

Through public records, interviews with developers, and technical analysis, Motherboard uncovered two separate, parallel data streams that the U.S. military uses, or has used, to obtain location data. One relies on a company called Babel Street, which creates a product called Locate X. U.S. Special Operations Command (USSOCOM), a branch of the military tasked with counterterrorism, counterinsurgency, and special reconnaissance, bought access to Locate X to assist on overseas special forces operations. The other stream is through a company called X-Mode, which obtains location data directly from apps, then sells that data to contractors, and by extension, the military.

The news highlights the opaque location data industry and the fact that the U.S. military, which has infamously used other location data to target drone strikes, is purchasing access to sensitive data. Many of the users of apps involved in the data supply chain are Muslim, which is notable considering that the United States has waged a decades-long war on predominantly Muslim terror groups in the Middle East, and has killed hundreds of thousands of civilians during its military operations in Pakistan, Afghanistan, and Iraq. Motherboard does not know of any specific operations in which this type of app-based location data has been used by the U.S. military.

The apps sending data to X-Mode include Muslim Pro, an app that reminds users when to pray and what direction Mecca is in relation to the user’s current location. The app has been downloaded over 50 million times on Android, according to the Google Play Store, and over 98 million in total across other platforms including iOS, according to Muslim Pro’s website….(More)”.

CrowdHeritage: Improving the quality of Cultural Heritage through crowdsourcing methods

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Paper by Maria Ralli et al: “The lack of granular and rich descriptive metadata highly affects the discoverability and usability of the digital content stored in museums, libraries and archives, aggregated and served through Europeana, thus often frustrating the user experience offered by these institutions’ portals. In this context, metadata enrichment services through automated analysis and feature extraction along with crowdsourcing annotation services can offer a great opportunity for improving the metadata quality of digital cultural content in a scalable way, while at the same time engaging different user communities and raising awareness about cultural heritage assets. Such an effort is Crowdheritage, an open crowdsourcing platform that aims to employ machine and human intelligence in order to improve the digital cultural content metadata quality….(More)”.