Michael A. Johansson & Daniela Saderi in Nature: “The public call for rapid sharing of research data relevant to the COVID-19 outbreak (see go.nature.com/2t1lyp6) is driving an unprecedented surge in (unrefereed) preprints. To help pinpoint the most important research, we have launched Outbreak Science Rapid PREreview, with support from the London-based charity Wellcome. This is an open-source platform for rapid review of preprints related to emerging outbreaks (see https://outbreaksci.prereview.org).
These reviews comprise responses to short, yes-or-no questions, with optional commenting. The questions are designed to capture structured, high-level input on the importance and quality of the research, which can be aggregated across several reviews. Scientists who have ORCID IDs can submit their reviews as they read the preprints (currently limited to the medRxiv, bioRxiv and arXiv repositories). The reviews are open and can be submitted anonymously.
Outbreaks of pathogens such as the SARS-CoV-2 coronavirus that is responsible for COVID-19 move fast and can affect anyone. Research to support outbreak response needs to be fast and open, too, as do mechanisms to review outbreak-related research. Help other scientists, as well as the media, journals and public-health officials, to find the most important COVID-19 preprints now….(More)”.
Barend Mons at Nature: “It is irresponsible to support research but not data stewardship…
Many of the world’s hardest problems can be tackled only with data-intensive, computer-assisted research. And I’d speculate that the vast majority of research data are never published. Huge sums of taxpayer funds go to waste because such data cannot be reused. Policies for data reuse are falling into place, but fixing the situation will require more resources than the scientific community is willing to face.
In 2013, I was part of a group of Dutch experts from many disciplines that called on our national science funder to support data stewardship. Seven years later, policies that I helped to draft are starting to be put into practice. These require data created by machines and humans to meet the FAIR principles (that is, they are findable, accessible, interoperable and reusable). I now direct an international Global Open FAIR office tasked with helping communities to implement the guidelines, and I am convinced that doing so will require a large cadre of professionals, about one for every 20 researchers.
Even when data are shared, the metadata, expertise, technologies and infrastructure necessary for reuse are lacking. Most published data sets are scattered into ‘supplemental files’ that are often impossible for machines or even humans to find. These and other sloppy data practices keep researchers from building on each other’s work. In cases of disease outbreaks, for instance, this might even cost lives….(More)”.
David Eaves, and Naeha Rashid in Policy Options: “A few weeks ago, members of the Nexus traveller identification program were notified that Canadian Border Services is upgrading its automated system, from iris scanners to facial recognition technology. This is meant to simplify identification and increase efficiency without compromising security. But it also raises profound questions concerning how we discuss and develop public policies around such technology – questions that may not be receiving sufficiently open debate in the rush toward promised greater security.
Analogous to the U.S. Customs and Border Protection (CBP) program Global Entry, Nexus is a joint Canada-US border control system designed for low-risk, pre-approved travellers. Nexus does provide a public good, and there are valid reasons to improve surveillance at airports. Even before 9/11, border surveillance was an accepted annoyance and since then, checkpoint operations have become more vigilant and complex in response to the public demand for safety.
Nexus is one of the first North America government-sponsored services to adopt facial recognition, and as such it could be a pilot program that other services will follow. Left unchecked, the technology will likely become ubiquitous at North American border crossings within the next decade, and it will probably be adopted by governments to solve domestic policy challenges.
Facial recognition software is imperfect and has documented bias, but it will continue to improve and become superior to humans in identifying individuals. Given this, questions arise such as, what policies guide the use of this technology? What policies should inform future government use? In our headlong rush toward enhanced security, we risk replicating the justification the used by the private sector in an attempt to balance effectiveness, efficiency and privacy.
One key question involves citizens’ capacity to consent. Previously, Nexus members submitted to fingerprint and retinal scans – biometric markers that are relatively unique and enable government to verify identity at the border. Facial recognition technology uses visual data and seeks, analyzes, and stores identifying facial information in a database, which is then used to compare with new images and video….(More)”.
Article by Viola Zhou: “On Valentine’s Day, a 36-year-old lawyer Matt Ma in the eastern Chinese province of Zhejiang discovered he had been coded “red”.The colour, displayed in a payment app on his smartphone, indicated that he needed to be quarantined at home even though he had no symptoms of the dangerous coronavirus.
Without a green light from the system, Ma could not travel from his ancestral hometown of Lishui to his new home city of Hangzhou, which is now surrounded by checkpoints set up to contain the epidemic.
Ma is one of the millions of people whose movements are being choreographed by the government through software that feeds on troves of data and issues orders that effectively dictate whether they must stay in or can go to work.Their experience represents a slice of China’s desperate attempt to stop the coronavirus by using a mixed bag of cutting-edge technologies and old-fashioned surveillance. It was also a rare real-world test of the use of technology on a large scale to halt the spread of communicable diseases.
“This kind of massive use of technology is unprecedented,” said Christos Lynteris, a medical anthropologist at the University of St Andrews who has studied epidemics in China.
But Hangzhou’s experiment has also revealed the pitfalls of applying opaque formulas to a large population.
In the city’s case, there are reports of people being marked incorrectly, falling victim to an algorithm that is, by the government’s own admission, not perfect….(More)”.
Special Report by The Economist: “The data economy is a work in progress. Its economics still have to be worked out; its infrastructure and its businesses need to be fully built; geopolitical arrangements must be found. But there is one final major tension: between the wealth the data economy will create and how it will be distributed. The data economy—or the “second economy”, as Brian Arthur of the Santa Fe Institute terms it—will make the world a more productive place no matter what, he predicts. But who gets what and how is less clear. “We will move from an economy where the main challenge is to produce more and more efficiently,” says Mr Arthur, “to one where distribution of the wealth produced becomes the biggest issue.”
The data economy as it exists today is already very unequal. It is dominated by a few big platforms. In the most recent quarter, Amazon, Apple, Alphabet, Microsoft and Facebook made a combined profit of $55bn, more than the next five most valuable American tech firms over the past 12 months. This corporate inequality is largely the result of network effects—economic forces that mean size begets size. A firm that can collect a lot of data, for instance, can make better use of artificial intelligence and attract more users, who in turn supply more data. Such firms can also recruit the best data scientists and have the cash to buy the best ai startups.
It is also becoming clear that, as the data economy expands, these sorts of dynamics will increasingly apply to non-tech companies and even countries. In many sectors, the race to become a dominant data platform is on. This is the mission of Compass, a startup, in residential property. It is one goal of Tesla in self-driving cars. And Apple and Google hope to repeat the trick in health care. As for countries, America and China account for 90% of the market capitalisation of the world’s 70 largest platforms (see chart), Africa and Latin America for just 1%. Economies on both continents risk “becoming mere providers of raw data…while having to pay for the digital intelligence produced,” the United Nations Conference on Trade and Development recently warned.
Yet it is the skewed distribution of income between capital and labour that may turn out to be the most pressing problem of the data economy. As it grows, more labour will migrate into the mirror worlds, just as other economic activity will. It is not only that people will do more digitally, but they will perform actual “data work”: generating the digital information needed to train and improve ai services. This can mean simply moving about online and providing feedback, as most people already do. But it will increasingly include more active tasks, such as labelling pictures, driving data-gathering vehicles and perhaps, one day, putting one’s digital twin through its paces. This is the reason why some say ai should actually be called “collective intelligence”: it takes in a lot of human input—something big tech firms hate to admit….(More)”.
Article by Geoff Shullenberger on “How fears of mind control went from paranoid delusion to conventional wisdom”: “In early 2017, after the double shock of Brexit and the election of Donald Trump, the British data-mining firm Cambridge Analytica gained sudden notoriety. The previously little-known company, reporters claimed, had used behavioral influencing techniques to turn out social media users to vote in both elections. By its own account, Cambridge Analytica had worked with both campaigns to produce customized propaganda for targeting individuals on Facebook likely to be swept up in the tide of anti-immigrant populism. Its methods, some news sources suggested, might have sent enough previously disengaged voters to the polls to have tipped the scales in favor of the surprise victors. To a certain segment of the public, this story seemed to answer the question raised by both upsets: How was it possible that the seemingly solid establishment consensus had been rejected? What’s more, the explanation confirmed everything that seemed creepy about the Internet, evoking a sci-fi vision of social media users turned into an army of political zombies, mobilized through subliminal manipulation.
Cambridge Analytica’s violations of Facebook users’ privacy have made it an enduring symbol of the dark side of social media. However, the more dramatic claims about the extent of the company’s political impact collapse under closer scrutiny, mainly because its much-hyped “psychographic targeting” methods probably don’t work. As former Facebook product manager Antonio García Martínez noted in a 2018 Wired article, “the public, with no small help from the media sniffing a great story, is ready to believe in the supernatural powers of a mostly unproven targeting strategy,” but “most ad insiders express skepticism about Cambridge Analytica’s claims of having influenced the election, and stress the real-world difficulty of changing anyone’s mind about anything with mere Facebook ads, least of all deeply ingrained political views.” According to García, the entire affair merely confirms a well-established truth: “In the ads world, just because a product doesn’t work doesn’t mean you can’t sell it….(More)”.
Editorial at Nature: “Everyone’s talking about reproducibility — or at least they are in the biomedical and social sciences. The past decade has seen a growing recognition that results must be independently replicated before they can be accepted as true.
A focus on reproducibility is necessary in the physical sciences, too — an issue explored in this month’s Nature Physics, in which two metrologists argue that reproducibility should be viewed through a different lens. When results in the science of measurement cannot be reproduced, argue Martin Milton and Antonio Possolo, it’s a sign of the scientific method at work — and an opportunity to promote public awareness of the research process (M. J. T. Milton and A. Possolo Nature Phys. 26, 117–119; 2020)….
However, despite numerous experiments spanning three centuries, the precise value of G remains uncertain. The root of the uncertainty is not fully understood: it could be due to undiscovered errors in how the value is being measured; or it could indicate the need for new physics. One scenario being explored is that G could even vary over time, in which case scientists might have to revise their view that it has a fixed value.
If that were to happen — although physicists think it unlikely — it would be a good example of non-reproduced data being subjected to the scientific process: experimental results questioning a long-held theory, or pointing to the existence of another theory altogether.
Questions in biomedicine and in the social sciences do not reduce so cleanly to the determination of a fundamental constant of nature. Compared with metrology, experiments to reproduce results in fields such as cancer biology are likely to include many more sources of variability, which are fiendishly hard to control for.
But metrology reminds us that when researchers attempt to reproduce the results of experiments, they do so using a set of agreed — and highly precise — experimental standards, known in the measurement field as metrological traceability. It is this aspect, the authors contend, that helps to build trust and confidence in the research process….(More)”.
Rana Foroohar at the Financial Times: “…A report by a Swedish research group called V-Dem found Taiwan was subject to more disinformation than nearly any other country, much of it coming from mainland China. Yet the popularity of pro-independence politicians is growing there, something Ms Tang views as a circular phenomenon.
When politicians enable more direct participation, the public begins to have more trust in government. Rather than social media creating “a false sense of us versus them,” she notes, decentralised technologies have “enabled a sense of shared reality” in Taiwan.
The same seems to be true in a number of other countries, including Israel, where Green party leader and former Occupy activist Stav Shaffir crowdsourced technology expertise to develop a bespoke data analysis app that allowed her to make previously opaque Treasury data transparent. She’s now heading an OECD transparency group to teach other politicians how to do the same. Part of the power of decentralised technologies is that they allow, at scale, the sort of public input on a wide range of complex issues that would have been impossible in the analogue era.
Consider “quadratic voting”, a concept that has been popularised by economist Glen Weyl, co-author of Radical Markets: Uprooting Capitalism and Democracy for a Just Society. Mr Weyl is the founder of the RadicalxChange movement, which aimsto empower a more participatory democracy. Unlike a binary “yes” or “no” vote for or against one thing, quadratic voting allows a large group of people to use a digital platform to express the strength of their desire on a variety of issues.
For example, when he headed the appropriations committee in the Colorado House of Representatives, Chris Hansen used quadratic voting to help his party quickly sort through how much of their $40m budget should be allocated to more than 100 proposals….(More)”.
Essay by Syon P. Bhanot and Elizabeth Linos: “The last decade has seen remarkable growth in the field of behavioral public administration, both in practice and in academia. In both domains, applications of behavioral science to policy problems have moved forward at breakneck speed; researchers are increasingly pursuing randomized behavioral interventions in public administration contexts, editors of peer‐reviewed academic journals are showing greater interest in publishing this work, and policy makers at all levels are creating new initiatives to bring behavioral science into the public sector.
However, because the expansion of the field has been so rapid, there has been relatively little time to step back and reflect on the work that has been done and to assess where the field is going in the future. It is high time for such reflection: where is the field currently on track, and where might it need course correction?…(More)”.
Louise Lief at Stanford Social Innovation Review: “Today, data governs almost every aspect of our lives, shaping the opportunities we have, how we perceive reality and understand problems, and even what we believe to be possible. Philanthropy is particularly data driven, relying on it to inform decision-making, define problems, and measure impact. But what happens when data design and collection methods are flawed, lack context, or contain critical omissions and misdirected questions? With bad data, data-driven strategies can misdiagnose problems and worsen inequities with interventions that don’t reflect what is needed.
Data justice begins by asking who controls the narrative. Who decides what data is collected and for which purpose? Who interprets what it means for a community? Who governs it? In recent years, affected communities, social justice philanthropists, and academics have all begun looking deeper into the relationship between data and social justice in our increasingly data-driven world. But philanthropy can play a game-changing role in developing practices of data justice to more accurately reflect the lived experience of communities being studied. Simply incorporating data justice principles into everyday foundation practice—and requiring it of grantees—would be transformative: It would not only revitalize research, strengthen communities, influence policy, and accelerate social change, it would also help address deficiencies in current government data sets.
When Data Is Flawed
Some of the most pioneering work on data justice has been done by Native American communities, who have suffered more than most from problems with bad data. A 2017 analysis of American Indian data challenges—funded by the W.K. Kellogg Foundation and the Morris K. Udall and Stewart L. Udall Foundation—documented how much data on Native American communities is of poor quality, inaccurate, inadequate, inconsistent, irrelevant, and/or inaccessible. The National Congress of American Indians even described American Native communities as “The Asterisk Nation,” because in many government data sets they are represented only by an asterisk denoting sampling errors instead of data points.
Where it concerns Native Americans, data is often not standardized and different government databases identify tribal members at least seven different ways using different criteria; federal and state statistics often misclassify race and ethnicity; and some data collection methods don’t allow tribes to count tribal citizens living off the reservation. For over a decade the Department of the Interior’s Bureau of Indian Affairs has struggled to capture the data it needs for a crucial labor force report it is legally required to produce; methodology errors and reporting problems have been so extensive that at times it prevented the report from even being published. But when the Department of the Interior changed several reporting requirements in 2014 and combined data submitted by tribes with US Census data, it only compounded the problem, making historical comparisons more difficult. Moreover, Native Americans have charged that the Census Bureau significantly undercounts both the American Indian population and key indicators like joblessness….(More)”.
