Governance Level: national

Building Trust and Protecting Privacy: Progress on the President’s Precision Medicine Initiative

Curated on Posted on by Stefaan Verhulst

The White House: “Today, the White House is releasing the Privacy and Trust Principles for the President’s Precision Medicine Initiative (PMI). These principles are a foundation for protecting participant privacy and building trust in activities within PMI.

PMI is a bold new research effort to transform how we characterize health and treat disease. PMI will pioneer a new model of patient-powered research that promises to accelerate biomedical discoveries and provide clinicians with new tools, knowledge, and therapies to select which treatments will work best for which patients. The initiative includes development of a new voluntary research cohort by the National Institutes of Health (NIH), a novel regulatory approach to genomic technologies by the Food and Drug Administration, and new cancer clinical trials by the National Cancer Institute at NIH.  In addition, PMI includes aligned efforts by the Federal government and private sector collaborators to pioneer a new approach for health research and healthcare delivery that prioritizes patient empowerment through access to information and policies that enable safe, effective, and innovative technologies to be tested and made available to the public.

Following President Obama’s launch of PMI in January 2015, the White House Office of Science and Technology Policy worked with an interagency group to develop the Privacy and Trust Principles that will guide the Precision Medicine effort. The White House convened experts from within and outside of government over the course of many months to discuss their individual viewpoints on the unique privacy challenges associated with large-scale health data collection, analysis, and sharing. This group reviewed the bioethics literature, analyzed privacy policies for large biobanks and research cohorts, and released a draft set of Principles for public comment in July 2015…..

The Privacy and Trust Principles are organized into 6 broad categories:

  1. Governance that is inclusive, collaborative, and adaptable;
  2. Transparency to participants and the public;
  3. Respecting participant preferences;
  4. Empowering participants through access to information;
  5. Ensuring appropriate data sharing, access, and use;
  6. Maintaining data quality and integrity….(More)”

Does Open Data Need Journalism?

Curated on Posted on by Stefaan Verhulst

Paper by Jonathan Stoneman at Reuters Institute for Journalism: “The Open Data movement really came into being when President Obama issued his first policy paper, on his first day in office in January 2009. The US government opened up thousands of datasets to scrutiny by the public, by journalists, by policy-makers. Coders and developers were also invited to make the data useful to people and businesses in all manner of ways. Other governments across the globe followed suit, opening up data to their populations.

Opening data in this way has not resulted in genuine openness, save in a few isolated cases. In the USA and a few European countries, developers have created apps and websites which draw on Open Data, but these are not reaching a mass audience.

At the same time, journalists are not seen by government as the end users of these data. Data releases, even in the best cases, are uneven, and slow, and do not meet the needs of journalists. Although thousands of journalists have been learning and adopting the new skills of datajournalism they have tended to work with data obtained through Freedom of Information (FOI) legislation.

Stories which have resulted from datajournalists’ efforts have rarely been front page news; in many cases data-driven stories have ended up as lesser stories on inside pages, or as infographics, which relatively few people look at.

In this context, therefore, Open Data remains outside the mainstream of journalism, and out of the consciousness of the electorate, begging the question, “what are Open Data for?”, or as one developer put it – “if Open Data is the answer, what was the question?” Openness is seen as a badge of honour – scores of national governments have signed pledges to make data open, often repeating the same kind of idealistic official language as the previous announcement of a conversion to openness. But these acts are “top down”, and soon run out of momentum, becoming simply openness for its own sake. Looking at specific examples, the United States is the nearest to a success story: there is a rich ecosystem – made up of government departments, interest groups and NGOs, the media, civil society – which allows data driven projects the space to grow and the airtime to make an impact. (It probably helped that the media in the US were facing an existential challenge urgent enough to force them to embrace new, inexpensive, ways of carrying out investigative reporting).

Elsewhere data are making less impact on journalism. In the UK the new openness is being exploited by a small minority. Where data are made published on the data.gov.uk website they are frequently out of date, incomplete, or of limited new value, so where data do drive stories, these tend to be data released under FOI legislation, and the resulting stories take the form of statistics and/or infographics.

In developing countries where Open Data Portals have been launched with a fanfare – such as Kenya, and more recently Burkina Faso – there has been little uptake by coders, journalists, or citizens, and the number of fresh datasets being published drops to a trickle, and are soon well out of date. Small, apparently randomly selected datasets are soon outdated and inertia sets in.

The British Conservative Party, pledging greater openness in its 2010 manifesto, foresaw armies of “Armchair Auditors” who would comb through the data and present the government with ideas for greater efficiency in the use of public funds. Almost needless to say, these armies have never materialised, and thousands of datasets go unscrutinised by anybody. 2 In countries like Britain large amounts of data are being published but going (probably) unread and unscrutinised by anybody. At the same time, the journalists who want to make use of data are getting what they need through FOI, or even by gathering data themselves. Open Data is thus being bypassed, and could become an irrelevance. Yet, the media could be vital agents in the quest for the release of meaningful, relevant, timely data.

Governments seem in no hurry to expand the “comfort zone” from which they release the data which shows their policies at their most effective, and keeping to themselves data which paints a gloomier picture. Journalists seem likely to remain in their comfort zone, where they make use of FOI and traditional sources of information. For their part, journalists should push for better data and use it more, working in collaboration with open data activists. They need to change the habits of a lifetime and discuss their sources: revealing the source and quality of data used in a story would in itself be as much a part of the advocacy as of the actual reporting.

If Open Data are to be part of a new system of democratic accountability, they need to be more than a gesture of openness. Nor should Open Data remain largely the preserve of companies using them for commercial purposes. Governments should improve the quality and relevance of published data, making them genuinely useful for journalists and citizens alike….(More)”

Government as a Platform: a historical and architectural analysis

Curated on Posted on by Stefaan Verhulst

Paper by Bendik Bygstad and Francis D’Silva: “A national administration is dependent on its archives and registers, for many purposes, such as tax collection, enforcement of law, economic governance, and welfare services. Today, these services are based on large digital infrastructures, which grow organically in volume and scope. Building on a critical realist approach we investigate a particularly successful infrastructure in Norway called Altinn, and ask: what are the evolutionary mechanisms for a successful “government as a platform”? We frame our study with two perspectives; a historical institutional perspective that traces the roots of Altinn back to the Middle Ages, and an architectural perspective that allows for a more detailed analysis of the consequences of digitalization and the role of platforms. We offer two insights from our study: we identify three evolutionary mechanisms of national registers, and we discuss a future scenario of government platforms as “digital commons”…(More)”

Robots Will Make Leeds the First Self-Repairing City

Curated on Posted on by Stefaan Verhulst

Emiko Jozuka at Motherboard: “Researchers in Britain want to make the first “self-repairing” city by 2035. How will they do this? By creating autonomous repair robots that patrol the streets and drainage systems, making sure your car doesn’t dip into a pothole, and that you don’t experience any gas leaks.

“The idea is to create a city that behaves almost like a living organism,” said Raul Fuentes, a researcher at the School of Civil Engineering at Leeds University, who is working on the project. “The robots will act like white cells that are able to identify bacteria or viruses and attack them. It’s kind of like an immune system.”

The £4.2 million ($6.4 million) national infrastructure project is in collaboration with Leeds City Council and the UK Collaboration for Research in Infrastructures and Cities (UKCRIC). The aim is to create a fleet of robot repair workers who will live in Leeds city, spot problems, and sort them out before they become even bigger ones by 2035, said Fuentes. The project is set to launch officially in January 2016, he added.

For their five-year project—which has a vision that extends until 2050—the researchers will develop robot designs and technologies that focus on three main areas. The first is to create drones that can perch on high structures and repair things like street lamps; the second is to develop drones that can autonomously spot when a pothole is about to form and zone in and patch that up before it worsens; and the third is to develop robots that will live in utility pipes so they can inspect, repair, and report back to humans when they spot an issue.

“The robots will be living permanently in the city, and they’ll be able to identify issues before they become real problems,” explained Fuentes. The researchers are working on making the robots autonomous, and want them to be living in swarms or packs where they can communicate with one another on how best they could get the repair job done….(More)

New flu tracker uses Google search data better than Google

Curated on Posted on by Stefaan Verhulst

 at ArsTechnica: “With big data comes big noise. Google learned this lesson the hard way with its now kaput Google Flu Trends. The online tracker, which used Internet search data to predict real-life flu outbreaks, emerged amid fanfare in 2008. Then it met a quiet death this August after repeatedly coughing up bad estimates.

But big Internet data isn’t out of the disease tracking scene yet.

With hubris firmly in check, a team of Harvard researchers have come up with a way to tame the unruly data, combine it with other data sets, and continually calibrate it to track flu outbreaks with less error. Their new model, published Monday in the Proceedings of the National Academy of Sciences, out-performs Google Flu Trends and other models with at least double the accuracy. If the model holds up in coming flu seasons, it could reinstate some optimism in using big data to monitor disease and herald a wave of more accurate second-generation models.

Big data has a lot of potential, Samuel Kou, a statistics professor at Harvard University and coauthor on the new study, told Ars. It’s just a question of using the right analytics, he said.

Kou and his colleagues built on Google’s flu tracking model for their new version, called ARGO (AutoRegression with GOogle search data). Google Flu Trends basically relied on trends in Internet search terms, such as headache and chills, to estimate the number of flu cases. Those search terms were correlated with flu outbreak data collected by the Centers for Disease Control and Prevention. The CDC’s data relies on clinical reports from around the country. But compiling and analyzing that data can be slow, leading to a lag time of one to three weeks. The Google data, on the other hand, offered near real-time tracking for health experts to manage and prepare for outbreaks.

At first Google’s tracker appeared to be pretty good, matching CDC data’s late-breaking data somewhat closely. But, two notable stumbles led to its ultimate downfall: an underestimate of the 2009 H1N1 swine flu outbreak and an alarming overestimate (almost double real numbers) of the 2012-2013 flu season’s cases…..For ARGO, he and colleagues took the trend data and then designed a model that could self-correct for changes in how people search. The model has a two-year sliding window in which it re-calibrates current search term trends with the CDC’s historical flu data (the gold standard for flu data). They also made sure to exclude winter search terms, such as March Madness and the Oscars, so they didn’t get accidentally correlated with seasonal flu trends. Last, they incorporated data on the historical seasonality of flu.

The result was a model that significantly out-competed the Google Flu Trends estimates for the period between March 29, 2009 to July 11, 2015. ARGO also beat out other models, including one based on current and historical CDC data….(More)”

See also Proceedings of the National Academy of Sciences, 2015. DOI: 10.1073/pnas.1515373112

Politics and the New Machine

Curated on Posted on by Stefaan Verhulst

Jill Lepore in the NewYorker on “What the turn from polls to data science means for democracy”: “…The modern public-opinion poll has been around since the Great Depression, when the response rate—the number of people who take a survey as a percentage of those who were asked—was more than ninety. The participation rate—the number of people who take a survey as a percentage of the population—is far lower. Election pollsters sample only a minuscule portion of the electorate, not uncommonly something on the order of a couple of thousand people out of the more than two hundred million Americans who are eligible to vote. The promise of this work is that the sample is exquisitely representative. But the lower the response rate the harder and more expensive it becomes to realize that promise, which requires both calling many more people and trying to correct for “non-response bias” by giving greater weight to the answers of people from demographic groups that are less likely to respond. Pollster.com’s Mark Blumenthal has recalled how, in the nineteen-eighties, when the response rate at the firm where he was working had fallen to about sixty per cent, people in his office said, “What will happen when it’s only twenty? We won’t be able to be in business!” A typical response rate is now in the single digits.

Meanwhile, polls are wielding greater influence over American elections than ever….

Still, data science can’t solve the biggest problem with polling, because that problem is neither methodological nor technological. It’s political. Pollsters rose to prominence by claiming that measuring public opinion is good for democracy. But what if it’s bad?

A “poll” used to mean the top of your head. Ophelia says of Polonius, “His beard as white as snow: All flaxen was his poll.” When voting involved assembling (all in favor of Smith stand here, all in favor of Jones over there), counting votes required counting heads; that is, counting polls. Eventually, a “poll” came to mean the count itself. By the nineteenth century, to vote was to go “to the polls,” where, more and more, voting was done on paper. Ballots were often printed in newspapers: you’d cut one out and bring it with you. With the turn to the secret ballot, beginning in the eighteen-eighties, the government began supplying the ballots, but newspapers kept printing them; they’d use them to conduct their own polls, called “straw polls.” Before the election, you’d cut out your ballot and mail it to the newspaper, which would make a prediction. Political parties conducted straw polls, too. That’s one of the ways the political machine worked….

Ever since Gallup, two things have been called polls: surveys of opinions and forecasts of election results. (Plenty of other surveys, of course, don’t measure opinions but instead concern status and behavior: Do you own a house? Have you seen a doctor in the past month?) It’s not a bad idea to reserve the term “polls” for the kind meant to produce election forecasts. When Gallup started out, he was skeptical about using a survey to forecast an election: “Such a test is by no means perfect, because a preelection survey must not only measure public opinion in respect to candidates but must also predict just what groups of people will actually take the trouble to cast their ballots.” Also, he didn’t think that predicting elections constituted a public good: “While such forecasts provide an interesting and legitimate activity, they probably serve no great social purpose.” Then why do it? Gallup conducted polls only to prove the accuracy of his surveys, there being no other way to demonstrate it. The polls themselves, he thought, were pointless…

If public-opinion polling is the child of a strained marriage between the press and the academy, data science is the child of a rocky marriage between the academy and Silicon Valley. The term “data science” was coined in 1960, one year after the Democratic National Committee hired Simulmatics Corporation, a company founded by Ithiel de Sola Pool, a political scientist from M.I.T., to provide strategic analysis in advance of the upcoming Presidential election. Pool and his team collected punch cards from pollsters who had archived more than sixty polls from the elections of 1952, 1954, 1956, 1958, and 1960, representing more than a hundred thousand interviews, and fed them into a UNIVAC. They then sorted voters into four hundred and eighty possible types (for example, “Eastern, metropolitan, lower-income, white, Catholic, female Democrat”) and sorted issues into fifty-two clusters (for example, foreign aid). Simulmatics’ first task, completed just before the Democratic National Convention, was a study of “the Negro vote in the North.” Its report, which is thought to have influenced the civil-rights paragraphs added to the Party’s platform, concluded that between 1954 and 1956 “a small but significant shift to the Republicans occurred among Northern Negroes, which cost the Democrats about 1 per cent of the total votes in 8 key states.” After the nominating convention, the D.N.C. commissioned Simulmatics to prepare three more reports, including one that involved running simulations about different ways in which Kennedy might discuss his Catholicism….

Data science may well turn out to be as flawed as public-opinion polling. But a stage in the development of any new tool is to imagine that you’ve perfected it, in order to ponder its consequences. I asked Hilton to suppose that there existed a flawless tool for measuring public opinion, accurately and instantly, a tool available to voters and politicians alike. Imagine that you’re a member of Congress, I said, and you’re about to head into the House to vote on an act—let’s call it the Smeadwell-Nutley Act. As you do, you use an app called iThePublic to learn the opinions of your constituents. You oppose Smeadwell-Nutley; your constituents are seventy-nine per cent in favor of it. Your constituents will instantly know how you’ve voted, and many have set up an account with Crowdpac to make automatic campaign donations. If you vote against the proposed legislation, your constituents will stop giving money to your reëlection campaign. If, contrary to your convictions but in line with your iThePublic, you vote for Smeadwell-Nutley, would that be democracy? …(More)”

 

Using Crowdsourcing to Track the Next Viral Disease Outbreak

Curated on Posted on by Stefaan Verhulst

The TakeAway: “Last year’s Ebola outbreak in West Africa killed more than 11,000 people. The pandemic may be diminished, but public health officials think that another major outbreak of infectious disease is fast-approaching, and they’re busy preparing for it.

Boston public radio station WGBH recently partnered with The GroundTruth Project and NOVA Next on a series called “Next Outbreak.” As part of the series, they reported on an innovative global online monitoring system called HealthMap, which uses the power of the internet and crowdsourcing to detect and track emerging infectious diseases, and also more common ailments like the flu.

Researchers at Boston Children’s Hospital are the ones behind HealthMap (see below), and they use it to tap into tens of thousands of sources of online data, including social media, news reports, and blogs to curate information about outbreaks. Dr. John Brownstein, chief innovation officer at Boston Children’s Hospital and co-founder of HealthMap, says that smarter data collection can help to quickly detect and track emerging infectious diseases, fatal or not.

“Traditional public health is really slowed down by the communication process: People get sick, they’re seen by healthcare providers, they get laboratory confirmed, information flows up the channels to state and local health [agencies], national governments, and then to places like the WHO,” says Dr. Brownstein. “Each one of those stages can take days, weeks, or even months, and that’s the problem if you’re thinking about a virus that can spread around the world in a matter of days.”

The HealthMap team looks at a variety of communication channels to undo the existing hierarchy of health information.

“We make everyone a stakeholder when it comes to data about outbreaks, including consumers,” says Dr. Brownstein. “There are a suite of different tools that public health officials have at their disposal. What we’re trying to do is think about how to communicate and empower individuals to really understand what the risks are, what the true information is about a disease event, and what they can do to protect themselves and their families. It’s all about trying to demystify outbreaks.”

In addition to the map itself, the HealthMap team has a number of interactive tools that individuals can both use and contribute to. Dr. Brownstein hopes these resources will enable the public to care more about disease outbreaks that may be happening around them—it’s a way to put the “public” back in “public health,” he says.

“We have a app called Outbreaks Near Me that allows people to know about what disease outbreaks are happening in their neighborhood,” Dr. Brownstein says. “Flu Near You is a an app that people use to self report on symptoms; Vaccine Finder is a tool that allows people to know what vaccines are available to them and their community.”

In addition to developing their own app, the HealthMap has partnered with existing tech firms like Uber to spread the word about public health.

“We worked closely with Uber last year and actually put nurses in Uber cars and delivered vaccines to people,” Dr. Brownstein says. “The closest vaccine location might still be only a block away for people, but people are still hesitant to get it done.”…(More)”

How smartphones are solving one of China’s biggest mysteries

Curated on Posted on by Stefaan Verhulst

Ana Swanson at the Washington Post: “For decades, China has been engaged in a building boom of a scale that is hard to wrap your mind around. In the last three decades, 260 million people have moved from the countryside to Chinese cities — equivalent to around 80 percent of the population of the U.S. To make room for all of those people, the size of China’s built-up urban areas nearly quintupled between 1984 and 2010.

Much of that development has benefited people’s lives, but some has not. In a breathless rush to boost growth and development, some urban areas have built vast, unused real estate projects — China’s infamous “ghost cities.” These eerie, shining developments are complete except for one thing: people to live in them.

China’s ghost cities have sparked a lot of debate over the last few years. Some argue that the developments are evidence of the waste in top-down planning, or the result of too much cheap funding for businesses. Some blame the lack of other good places for average people to invest their money, or the desire of local officials to make a quick buck — land sales generate a lot of revenue for China’s local governments.

Others say the idea of ghost cities has been overblown. They espouse a “build it and they will come” philosophy, pointing out that, with time, some ghost cities fill up and turn into vibrant communities.

It’s been hard to evaluate these claims, since most of the research on ghost cities has been anecdotal. Even the most rigorous research methods leave a lot to be desired — for example, investment research firms sending poor junior employees out to remote locations to count how many lights are turned on in buildings at night.

Now new research from Baidu, one of China’s biggest technology companies, provides one of the first systematic looks at Chinese ghost cities. Researchers from Baidu’s Big Data Lab and Peking University in Beijing used the kind of location data gathered by mobile phones and GPS receivers to track how people moved in and out suspected ghost cities, in real time and on a national scale, over a period of six months. You can see the interactive project here.

Google has been blocked in China for years, and Baidu dominates the market in terms of search, mobile maps and other offerings. That gave the researchers a huge data base to work with —  770 million users, a hefty chunk of China’s 1.36 billion people.

To identify potential ghost cities, the researchers created an algorithm that identifies urban areas with a relatively spare population. They define a ghost city as an urban region with a population of fewer than 5,000 people per square kilometer – about half the density recommended by the Chinese Ministry of Housing and Urban-Rural Development….(More)”

Open government: a new paradigm in social change?

Curated on Posted on by Stefaan Verhulst

Rosie Williams: In a recent speech to the Australian and New Zealand School of Government (ANSOG) annual conference, technology journalist and academic Suelette Drefyus explained the growing ‘information asymmetry’ that characterises the current-day relationship between government and citizenry.

According to Dreyfus:

‘Big Data makes government very powerful in its relationship with the citizen. This is even more so with the rise of intelligent systems, software that increasingly trawls, matches and analyses that Big Data. And it is moving toward making more decisions once made by human beings.’

The role of technology in the delivery of government services gives much food for thought in terms of both its implications for potential good and the potential dangers it may pose. The concept of open government is an important one for the future of policy and democracy in Australia. Open government has at its core a recognition that the world has changed, that the ways people engage and who they engage with has transformed in ways that governments around the world must respond to in both technological and policy terms.

As described in the ANSOG speech, the change within government in how it uses technology is well underway, however in many regards we are at the very beginning of understanding and implementing the potential of data and technology in providing solutions to many of our shared problems. Australia’s pending membership of the Open Government Partnership is integral to how Australia responds to these challenges. Membership of the multi-lateral partnership requires the Australian government to create a National Action Plan based on consultation and demonstrate our credentials in the areas of Fiscal Transparency, Access to Information, Income and Asset Disclosure, and Citizen Engagement.

What are the implications of the National Action Plan for policy consultation formulation, implementation and evaluation? In relative terms, Australia’s history with open government is fairly recent. Policies on open data have seen the roll out of data.gov.au – a repository of data published by government agencies and made available for re-use in efforts such as the author’s own financial transparency site OpenAus.

In this way citizen activity and government come together for the purposes of achieving open government. These efforts express a new paradigm in government and activism where the responsibility for solving the problems of democracy are shared between government and the people as opposed to the government ‘solving’ the problems of a passive, receptive citizenry.

As the famous whistle-blowers have shown, citizens are no longer passive but this new capability also requires a consciousness of the responsibilities and accountability that go along with the powers newly developed by citizen activists through technological change.

The opening of data and communication channels in the formulation of public policy provides a way forward to create both a better informed citizenry and also better informed policy evaluation. When new standards of transparency are applied to wicked problems what shortcomings does this highlight?

This question was tested with my recent request for a basic fact missing from relevant government research and reviews but key to social issues of homelessness and domestic violence….(More)”

New traffic app and disaster prevention technology road tested

Curated on Posted on by Stefaan Verhulst

Psych.org: “A new smartphone traffic app tested by citizens in Dublin, Ireland allows users to give feedback on traffic incidents, enabling traffic management centres to respond quicker when collisions and other incidents happen around the city. The ‘CrowdAlert’ app, which is now available for download, is one of the key components utilised in the EU-funded INSIGHT project and a good example of how smartphones and social networks can be harnessed to improve public services and safety.

‘We are witnessing an explosion in the quantity, quality, and variety of available information, fuelled in large part by advances in sensor networking, the availability of low-cost sensor-enabled devices and by the widespread adoption of powerful smart-phones,’ explains  coordinator professor Dimitrios Gunopulos from the National and Kapodistrian University of Athens. ‘These revolutionary technologies are driving the development and adoption of applications where mobile devices are used for continuous data sensing and analysis.’

The project also developed a novel citywide real-time traffic monitoring tool, the ‘INSIGHT System’, which was tested in real conditions in the Dublin City control room, along with nationwide disaster monitoring technologies. The INSIGHT system was shown to provide early warnings to experts at situation centres, enabling them to monitor situations in real-time, including disasters with potentially nation-wide impacts such as severe weather conditions, floods and subsequent knock-on events such as fires and power outages.

The project’s results will be of interest to public services, which have until now lacked the necessary infrastructure for handling and integrating miscellaneous data streams, including data from static and mobile sensors as well as information coming from social network sources, in real-time. Providing cities with the ability to manage emergency situations with enhanced capabilities will also open up new markets for network technologies….(More)”

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