Paper by J. Ramon Gil‐Garcia, Paul Henman, and Martha Alicia Avila‐Maravilla: “In the last two decades, Internet portals have been used by governments around the world as part of very diverse strategies from service provision to citizen engagement. Several authors propose that there is an evolution of digital government reflected in the functionality and sophistication of these portals and other technologies. More recently, scholars and practitioners are proposing different conceptualizations of “government as a platform” and, for some, this could be the next stage of digital government. However, it is not clear what are the main differences between a sophisticated Internet portal and a platform. Therefore, based on an analysis of three of the most advanced national portals, this ongoing research paper explores to what extent these digital efforts clearly represent the basic characteristics of platforms. So, this paper explores questions such as: (1) to what extent current national portals reflect the characteristics of what has been called “government as a platform?; and (2) Are current national portals evolving towards “government as a platform”?…(More)”.
JPMorgan Creates ‘Volfefe’ Index to Track Trump Tweet Impact
Tracy Alloway at Bloomberg: “Two of the largest Wall Street banks are trying to measure the market impact of Donald Trump’s tweets.
Analysts at JPMorgan Chase & Co. have created an index to quantify what they say are the growing effects on U.S. bond yields. Citigroup Inc.’s foreign exchange team, meanwhile, report that these micro-blogging missives are also becoming “increasingly relevant” to foreign-exchange moves.
JPMorgan’s “Volfefe Index,” named after Trump’s mysterious covfefe tweet from May 2017, suggests that the president’s electronic musings are having a statistically significant impact on Treasury yields. The number of market-moving Trump tweets has ballooned in the past month, with those including words such as “China,” “billion,” “products,” “Democrats” and “great” most likely to affect prices, the analysts found….
JPMorgan’s analysis looked at Treasury yields in the five minutes after a Trump tweet, and the index shows the rolling one-month probability that each missive is market-moving.
They found that the Volfefe Index can account for a “measurable fraction” of moves in implied volatility, seen in interest rate derivatives known as swaptions. That’s particularly apparent at the shorter end of the curve, with two- and five-year rates more impacted than 10-year securities.
Meanwhile, Citi’s work shows that the president’s tweets are generally followed by a stretch of higher volatility across global currency markets. And there’s little sign traders are growing numb to these messages….(More)”
The Why of the World
Book review by Tim Maudlin of The Book of Why: The New Science of Cause and Effect by Judea Pearl and Dana Mackenzie: “Correlation is not causation.” Though true and important, the warning has hardened into the familiarity of a cliché. Stock examples of so-called spurious correlations are now a dime a dozen. As one example goes, a Pacific island tribe believed flea infestations to be good for one’s health because they observed that healthy people had fleas while sick people did not. The correlation is real and robust, but fleas do not cause health, of course: they merely indicate it. Fleas on a fevered body abandon ship and seek a healthier host. One should not seek out and encourage fleas in the quest to ward off sickness.
The rub lies in another observation: that the evidence for causation seems to lie entirely in correlations. But for seeing correlations, we would have no clue about causation. The only reason we discovered that smoking causes lung cancer, for example, is that we observed correlations in that particular circumstance. And thus a puzzle arises: if causation cannot be reduced to correlation, how can correlation serve as evidence of causation?
The Book of Why, co-authored by the computer scientist Judea Pearl and the science writer Dana Mackenzie, sets out to give a new answer to this old question, which has been around—in some form or another, posed by scientists and philosophers alike—at least since the Enlightenment. In 2011 Pearl won the Turing Award, computer science’s highest honor, for “fundamental contributions to artificial intelligence through the development of a calculus of probabilistic and causal reasoning,” and this book sets out to explain what all that means for a general audience, updating his more technical book on the same subject, Causality, published nearly two decades ago. Written in the first person, the new volume mixes theory, history, and memoir, detailing both the technical tools of causal reasoning Pearl has developed as well as the tortuous path by which he arrived at them—all along bucking a scientific establishment that, in his telling, had long ago contented itself with data-crunching analysis of correlations at the expense of investigation of causes. There are nuggets of wisdom and cautionary tales in both these aspects of the book, the scientific as well as the sociological…(More)”.
Is Privacy and Personal Data Set to Become the New Intellectual Property?
Paper by Leon Trakman, Robert Walters, and Bruno Zeller: “A pressing concern today is whether the rationale underlying the protection of personal data is itself a meaningful foundation for according intellectual property (IP) rights in personal data to data subjects. In particular, are there particular technological attributes about the collection, use and processing of personal data on the Internet, and global access to that data, that provide a strong justification to extend IP rights to data subjects? A central issue in so determining is whether data subjects need the protection of such rights in a technological revolution in which they are increasingly exposed to the use and abuse of their personal data. A further question is how IP law can provide them with the requisite protection of their private space, or whether other means of protecting personal data, such as through general contract rights, render IP protections redundant, or at least, less necessary. This paper maintains that lawmakers often fail to distinguish between general property and IP protection of personal data; that IP protection encompasses important attributes of both property and contract law; and that laws that implement IP protection in light of its sui generis attributes are more fitting means of protecting personal data than the alternatives. The paper demonstrates that one of the benefits of providing IP rights in personal data goes some way to strengthening data subjects’ control and protection over their personal data and strengthening data protection law more generally. It also argues for greater harmonization of IP law across jurisdictions to ensure that the protection of personal data becomes more coherent and internationally sustainable….(More)”.
Next100
PressRelease: “Next100, a new “startup” think tank built for and by the next generation of policy leaders, officially launched today with the announcement of its inaugural class of eight “Policy Entrepreneurs,” selected from a highly competitive pool of more than 740 applicants. These eight rising leaders will spend the next two years researching and developing policy solutions to the issues that matter most to the next generation, focusing in particular on: education, immigration, criminal justice, climate change, economic opportunity, and the intersections between such issues.
Next100 was announced as an independent think tank earlier this year by The Century Foundation (TCF), in celebration of TCF’s 100th anniversary. It is built as a different type of “think and do” tank — both in terms of the people, perspectives, and policy areas represented, as well as its approach to advancing policy change. The organization’s mission is to change the face and future of progressive policy, through making the policymaking space more inclusive of diverse, next generation voices, and by helping emerging leaders translate their creative policy ideas into tangible policy change.
“The next generation is too often and too easily excluded from the policymaking table, despite having the most at stake in the decisions made at that table,” said Emma Vadehra, executive director of Next100. “As a result, we end up with the same people, with the same ideas, trying to solve the same problems, in the same ways. Next100 is trying to change that, and reimagine what a think tank can and should be. We’re giving diverse leaders of the next generation a chance to cut through the inertia and bring their unmatched creativity, knowledge, skills, and experiences to bear on the policymaking process. Policy by those with the most at stake, for those with the most at stake.”…(More)”.
How Should Scientists’ Access To Health Databanks Be Managed?
Richard Harris at NPR: “More than a million Americans have donated genetic information and medical data for research projects. But how that information gets used varies a lot, depending on the philosophy of the organizations that have gathered the data.
Some hold the data close, while others are working to make the data as widely available to as many researchers as possible — figuring science will progress faster that way. But scientific openness can be constrained b y both practical and commercial considerations.
Three major projects in the United States illustrate these differing philosophies.
VA scientists spearhead research on veterans database
The first project involves three-quarters of a million veterans, mostly men over age 60. Every day, 400 to 500 blood samples show up in a modern lab in the basement of the Veterans Affairs hospital in Boston. Luis Selva, the center’s associate director, explains that robots extract DNA from the samples and then the genetic material is sent out for analysis….
Intermountain Healthcare teams with deCODE genetics
Our second example involves what is largely an extended family: descendants of settlers in Utah, primarily from the Church of Jesus Christ of Latter-day Saints. This year, Intermountain Healthcare in Utah announced that it was going to sequence the complete DNA of half a million of its patients, resulting in what the health system says will be the world’s largest collection of complete genomes….
NIH’s All of Us aims to diversify and democratize research
Our third and final example is an effort by the National Institutes of Health to recruit a million Americans for a long-term study of health, behavior and genetics. Its philosophy sharply contrasts with that of Intermountain Health.
“We do have a very strong goal around diversity, in making sure that the participants in the All of Us research program reflect the vast diversity of the United States,” says Stephanie Devaney, the program’s deputy director….(More)”.
Raw data won’t solve our problems — asking the right questions will
Stefaan G. Verhulst in apolitical: “If I had only one hour to save the world, I would spend fifty-five minutes defining the questions, and only five minutes finding the answers,” is a famous aphorism attributed to Albert Einstein.
Behind this quote is an important insight about human nature: Too often, we leap to answers without first pausing to examine our questions. We tout solutions without considering whether we are addressing real or relevant challenges or priorities. We advocate fixes for problems, or for aspects of society, that may not be broken at all.
This misordering of priorities is especially acute — and represents a missed opportunity — in our era of big data. Today’s data has enormous potential to solve important public challenges.
However, policymakers often fail to invest in defining the questions that matter, focusing mainly on the supply side of the data equation (“What data do we have or must have access to?”) rather than the demand side (“What is the core question and what data do we really need to answer it?” or “What data can or should we actually use to solve those problems that matter?”).
As such, data initiatives often provide marginal insights while at the same time generating unnecessary privacy risks by accessing and exploring data that may not in fact be needed at all in order to address the root of our most important societal problems.
A new science of questions
So what are the truly vexing questions that deserve attention and investment today? Toward what end should we strategically seek to leverage data and AI?
The truth is that policymakers and other stakeholders currently don’t have a good way of defining questions or identifying priorities, nor a clear framework to help us leverage the potential of data and data science toward the public good.
This is a situation we seek to remedy at The GovLab, an action research center based at New York University.
Our most recent project, the 100 Questions Initiative, seeks to begin developing a new science and practice of questions — one that identifies the most urgent questions in a participatory manner. Launched last month, the goal of this project is to develop a process that takes advantage of distributed and diverse expertise on a range of given topics or domains so as to identify and prioritize those questions that are high impact, novel and feasible.
Because we live in an age of data and much of our work focuses on the promises and perils of data, we seek to identify the 100 most pressing problems confronting the world that could be addressed by greater use of existing, often inaccessible, datasets through data collaboratives – new forms of cross-disciplinary collaboration beyond public-private partnerships focused on leveraging data for good….(More)”.
Could footnotes be the key to winning the disinformation wars?
Karin Wulf at the Washington Post: “We are at a distinctive point in the relationship between information and democracy: As the volume of information dissemination has grown, so too have attempts by individuals and groups to weaponize disinformation for commercial and political purposes. This has contributed to fragmentation, political polarization, cynicism, and distrust in institutions and expertise, as a recent Pew Research Center report found. So what is the solution?
Footnotes.
Outside of academics and lawyers, few people may think about footnotes once they leave school. Indeed, there is a hackneyed caricature about footnotes as pedantry, the purview of tweedy scholars blinking as we emerge from fluorescent-lit libraries into the sun — not the concern of regular folks. A recent essay in the Economist even laid some of Britain’s recent woes at the feet of historians who spend too much time “fiddling with footnotes.”
But nothing could be further from the truth. More than ever, we need what this tool provides: accountability and transparency. “Fiddling with footnotes” is the kind of hygienic practice that our era of information pollution needs — and needs to be shared as widely as possible. Footnotes are for everyone.
Though they began as an elite practice, footnotes became aligned historically with modern democracy itself. Citation is rooted in the 17th-century emergence of enlightenment science, which asked for evidence rather than faith as key to supporting a conclusion. It was an era when scientific empiricism threatened the authority of government and religious institutions and newly developing institutional science publications, the Philosophical Transactions of the Royal Society, for example, began to use citations for evidence and reference. In one of Isaac Newton’s contributions to the journal in 1673, a reply to queries about his work on light and the color spectrum, he used citations to his initial publication on the subject (“see no. 80. Page 3075”).
By the 18th century, and with more agile printing, the majority of scientific publications included citations, and the bottom of the page was emerging as the preferred placement. Where scientific scholarship traveled, humanists were not far behind. The disdain of French philosopher and mathematician René Descartes for any discipline without rigorous methods was part of the prompt for historians to embrace citations….(More)”.
The hidden assumptions in public engagement: A case study of engaging on ethics in government data analysis
Paper by Emily S. Rempel, Julie Barnett and Hannah Durrant: “This study examines the hidden assumptions around running public-engagement exercises in government. We study an example of public engagement on the ethics of combining and analysing data in national government – often called data science ethics. We study hidden assumptions, drawing on hidden curriculum theories in education research, as it allows us to identify conscious and unconscious underlying processes related to conducting public engagement that may impact results. Through participation in the 2016 Public Dialogue for Data Science Ethics in the UK, four key themes were identified that exposed underlying public engagement norms. First, that organizers had constructed a strong imagined public as neither overly critical nor supportive, which they used to find and engage participants. Second, that official aims of the engagement, such as including publics in developing ethical data regulations, were overshadowed by underlying meta-objectives, such as counteracting public fears. Third, that advisory group members, organizers and publics understood the term ‘engagement’ in varying ways, from creating interest to public inclusion. And finally, that stakeholder interests, particularly government hopes for a positive report, influenced what was written in the final report. Reflection on these underlying mechanisms, such as the development of meta-objectives that seek to benefit government and technical stakeholders rather than publics, suggests that the practice of public engagement can, in fact, shut down opportunities for meaningful public dialogue….(More)”.
The 9 Pitfalls of Data Science
Book by Gary Smith and Jay Cordes: “Data science has never had more influence on the world. Large companies are now seeing the benefit of employing data scientists to interpret the vast amounts of data that now exists. However, the field is so new and is evolving so rapidly that the analysis produced can be haphazard at best.
The 9 Pitfalls of Data Science shows us real-world examples of what can go wrong. Written to be an entertaining read, this invaluable guide investigates the all too common mistakes of data scientists – who can be plagued by lazy thinking, whims, hunches, and prejudices – and indicates how they have been at the root of many disasters, including the Great Recession.
Gary Smith and Jay Cordes emphasise how scientific rigor and critical thinking skills are indispensable in this age of Big Data, as machines often find meaningless patterns that can lead to dangerous false conclusions. The 9 Pitfalls of Data Science is loaded with entertaining tales of both successful and misguided approaches to interpreting data, both grand successes and epic failures. These cautionary tales will not only help data scientists be more effective, but also help the public distinguish between good and bad data science….(More)”.