Who will benefit most from the data economy?

Curated on Posted on by Stefaan Verhulst

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)”.

We All Wear Tinfoil Hats Now

Curated on Posted on by Stefaan Verhulst

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)”.

Irreproducibility is not a sign of failure, but an inspiration for fresh ideas

Curated on Posted on by Stefaan Verhulst

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)”.

Digital tools can be a useful bolster to democracy

Curated on Posted on by Stefaan Verhulst

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)”.

Behavioral Public Administration: : Past, Present, and Future

Curated on Posted on by Stefaan Verhulst

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)”.

How Philanthropy Can Help Lead on Data Justice

Curated on Posted on by Stefaan Verhulst

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)”.

This emoji could mean your suicide risk is high, according to AI

Curated on Posted on by Stefaan Verhulst

Rebecca Ruiz at Mashable: “Since its founding in 2013, the free mental health support service Crisis Text Line has focused on using data and technology to better aid those who reach out for help. 

Unlike helplines that offer assistance based on the order in which users dialed, texted, or messaged, Crisis Text Line has an algorithm that determines who is in most urgent need of counseling. The nonprofit is particularly interested in learning which emoji and words texters use when their suicide risk is high, so as to quickly connect them with a counselor. Crisis Text Line just released new insights about those patterns. 

Based on its analysis of 129 million messages processed between 2013 and the end of 2019, the nonprofit found that the pill emoji, or 💊, was 4.4 times more likely to end in a life-threatening situation than the word suicide. 

Other words that indicate imminent danger include 800mg, acetaminophen, excedrin, and antifreeze; those are two to three times more likely than the word suicide to involve an active rescue of the texter. The loudly crying emoji face, or 😭, is similarly high-risk. In general, the words that trigger the greatest alarm suggest the texter has a method or plan to attempt suicide or may be in the process of taking their own life. …(More)”.

Our personal health history is too valuable to be harvested by the tech giants

Curated on Posted on by Stefaan Verhulst

Eerke Boiten at The Guardian: “…It is clear that the black box society does not only feed on internet surveillance information. Databases collected by public bodies are becoming more and more part of the dark data economy. Last month, it emerged that a data broker in receipt of the UK’s national pupil database had shared its access with gambling companies. This is likely to be the tip of the iceberg; even where initial recipients of shared data might be checked and vetted, it is much harder to oversee who the data is passed on to from there.

Health data, the rich population-wide information held within the NHS, is another such example. Pharmaceutical companies and internet giants have been eyeing the NHS’s extensive databases for commercial exploitation for many years. Google infamously claimed it could save 100,000 lives if only it had free rein with all our health data. If there really is such value hidden in NHS data, do we really want Google to extract it to sell it to us? Google still holds health data that its subsidiary DeepMind Health obtained illegally from the NHS in 2016.

Although many health data-sharing schemes, such as in the NHS’s register of approved data releases], are said to be “anonymised”, this offers a limited guarantee against abuse.

There is just too much information included in health data that points to other aspects of patients’ lives and existence. If recipients of anonymised health data want to use it to re-identify individuals, they will often be able to do so by combining it, for example, with publicly available information. That this would be illegal under UK data protection law is a small consolation as it would be extremely hard to detect.

It is clear that providing access to public organisations’ data for research purposes can serve the greater good and it is unrealistic to expect bodies such as the NHS to keep this all in-house.

However, there are other methods by which to do this, beyond the sharing of anonymised databases. CeLSIUS, for example, a physical facility where researchers can interrogate data under tightly controlled conditions for specific registered purposes, holds UK census information over many years.

These arrangements prevent abuse, such as through deanonymisation, do not have the problem of shared data being passed on to third parties and ensure complete transparency of the use of the data. Online analogues of such set-ups do not yet exist, but that is where the future of safe and transparent access to sensitive data lies….(More)”.

Google redraws the borders on maps depending on who’s looking

Curated on Posted on by Stefaan Verhulst

Greg Bensinger in the Washington Post: “For more than 70 years, India and Pakistan have waged sporadic and deadly skirmishes over control of the mountainous region of Kashmir. Tens of thousands have died in the conflict, including three just this month.

Both sides claim the Himalayan outpost as their own, but Web surfers in India could be forgiven for thinking the dispute is all but settled: The borders on Google’s online maps there display Kashmir as fully under Indian control. Elsewhere, users see the region’s snaking outlines as a dotted line, acknowledging the dispute.

Google’s corporate mission is “to organize the world’s information,” but it also bends it to its will. From Argentina to the United Kingdom to Iran, the world’s borders look different depending on where you’re viewing them from. That’s because Google — and other online mapmakers — simply change them.

With some 80 percent market share in mobile maps and over a billion users, Google Maps has an outsize impact on people’s perception of the world — from driving directions to restaurant reviews to naming attractions to adjudicating historical border wars.

And while maps are meant to bring order to the world, the Silicon Valley firm’s decision-making on maps is often shrouded in secrecy, even to some of those who work to shape its digital atlases every day. It is influenced not just by history and local laws, but also the shifting whims of diplomats, policymakers and its own executives, say people familiar with the matter, who asked not to be identified because they weren’t authorized to discuss internal processes….(More)”.

Realizing the Potential of AI Localism

Curated on Posted on by Stefaan Verhulst

Stefaan G. Verhulst and Mona Sloane at Project Syndicate: “Every new technology rides a wave from hype to dismay. But even by the usual standards, artificial intelligence has had a turbulent run. Is AI a society-renewing hero or a jobs-destroying villain? As always, the truth is not so categorical.

As a general-purpose technology, AI will be what we make of it, with its ultimate impact determined by the governance frameworks we build. As calls for new AI policies grow louder, there is an opportunity to shape the legal and regulatory infrastructure in ways that maximize AI’s benefits and limit its potential harms.

Until recently, AI governance has been discussed primarily at the national level. But most national AI strategies – particularly China’s – are focused on gaining or maintaining a competitive advantage globally. They are essentially business plans designed to attract investment and boost corporate competitiveness, usually with an added emphasis on enhancing national security.

This singular focus on competition has meant that framing rules and regulations for AI has been ignored. But cities are increasingly stepping into the void, with New York, Toronto, Dubai, Yokohama, and others serving as “laboratories” for governance innovation. Cities are experimenting with a range of policies, from bans on facial-recognition technology and certain other AI applications to the creation of data collaboratives. They are also making major investments in responsible AI research, localized high-potential tech ecosystems, and citizen-led initiatives.

This “AI localism” is in keeping with the broader trend in “New Localism,” as described by public-policy scholars Bruce Katz and the late Jeremy Nowak. Municipal and other local jurisdictions are increasingly taking it upon themselves to address a broad range of environmental, economic, and social challenges, and the domain of technology is no exception.

For example, New York, Seattle, and other cities have embraced what Ira Rubinstein of New York University calls “privacy localism,” by filling significant gaps in federal and state legislation, particularly when it comes to surveillance. Similarly, in the absence of a national or global broadband strategy, many cities have pursued “broadband localism,” by taking steps to bridge the service gap left by private-sector operators.

As a general approach to problem solving, localism offers both immediacy and proximity. Because it is managed within tightly defined geographic regions, it affords policymakers a better understanding of the tradeoffs involved. By calibrating algorithms and AI policies for local conditions, policymakers have a better chance of creating positive feedback loops that will result in greater effectiveness and accountability….(More)”.