law – Page 27 – The Living Library

Is Open Data the Death of FOIA?

Curated on November 23, 2016August 3, 2018 by Stefaan Verhulst

Beth Noveck at the Yale Law Journal: “For fifty years, the Freedom of Information Act (FOIA) has been the platinum standard for open government in the United States. The statute is considered the legal bedrock of the public’s right to know about the workings of our government. More than one hundred countries and all fifty states have enacted their own freedom of information laws. At the same time, FOIA’s many limitations have also become evident: a cumbersome process, delays in responses, and redactions that frustrate journalists and other information seekers. Politically-motivated nuisance requests bedevil government agencies.With over 700,000 FOIA requests filed every year, the federal government faces the costs of a mounting backlog.

In recent years, however, an entirely different approach to government transparency in line with the era of big data has emerged: open government data. Open government data —generally shortened to open data—has many definitions but is generally considered to be publicly available information that can be universally and readily accessed, used, and redistributed free of charge in digital form. Open data is not limited to statistics, but also includes text such as the United States Federal Register, the daily newspaper of government, which was released as open data in bulk form in 2010.

To understand how significant the open data movement is for FOIA, this Essay discusses the impact of open data on the institutions and functions of government and the ways open data contrasts markedly with FOIA. Open data emphasizes the proactive publication of whole classes of information. Open data includes data about the workings of government but also data collected by the government about the economy and society posted online in a centralized repository for use by the wider public, including academic users seeking information as the basis for original research and commercial users looking to create new products and services. For example, Pixar used open data from the United States Geological Survey to create more realistic detail in scenes from its movie The Good Dinosaur.

By contrast, FOIA promotes ex post publication of information created by the government especially about its own workings in response to specific demands by individual requestors. I argue that open data’s more systematic and collaborative approach represents a radical and welcome departure from FOIA because open data concentrates on information as a means to solve problems to the end of improving government effectiveness. Open data is legitimated by the improved outcomes it yields and grounded in a theory of government effectiveness and, as a result, eschews the adversarial and ad hoc FOIA approach. Ultimately, however, each tactic offers important complementary benefits. The proactive information disclosure regime of open data is strengthened by FOIA’s rights of legal enforcement. Together, they stand to become the hallmark of government transparency in the fifty years ahead….(More)”.

New UN resolution on the right to privacy in the digital age: crucial and timely

Curated on November 23, 2016August 3, 2018 by Stefaan Verhulst

Deborah Brown at the Internet Policy Review: “The rapid pace of technological development enables individuals all over the world to use new information and communications technologies (ICTs) to improve their lives. At the same time, technology is enhancing the capacity of governments, companies and individuals to undertake surveillance, interception and data collection, which may violate or abuse human rights, in particular the right to privacy. In this context, the UN General Assembly’s Third Committee adoption on 21 November of a new resolution on the right to privacy in the digital age comes as timely and crucial for protecting the right to privacy in light of new challenges.

As with previous UN resolutions on this topic, the resolution adopted on 21 November 2016 recognises the importance of respecting international commitments in relation to the right to privacy. It underscores that any legitimate concerns states may have with regard to their security can and should be addressed in a manner consistent with obligations under international human rights law.

Recognising that more and more personal data is being collected, processed, and shared, this year’s resolution expresses concern about the sale or multiple re-sales of personal data, which often happens without the individual’s free, explicit and informed consent. It calls for the strengthening of prevention of and protection against such violations, and calls on states to develop preventative measures, sanctions, and remedies.

This year, the resolution more explicitly acknowledges the role of the private sector. It calls on states to put in place (or maintain) effective sanctions and remedies to prevent the private sector from committing violations and abuses of the right to privacy. This is in line with states’ obligations under the UN Guiding Principles on Business and Human Rights, which require states to protect against abuses by businesses within their territories or jurisdictions. The resolution specifically calls on states to refrain from requiring companies to take steps that interfere with the right to privacy in an arbitrary or unlawful way. With respect to companies, it recalls the responsibility of the private sector to respect human rights, and specifically calls on them to inform users about company policies that may impact their right to privacy….(More)”

What’s wrong with big data?

Curated on November 16, 2016August 3, 2018 by Stefaan Verhulst

James Bridle in the New Humanist: “In a 2008 article in Wired magazine entitled “The End of Theory”, Chris Anderson argued that the vast amounts of data now available to researchers made the traditional scientific process obsolete. No longer would they need to build models of the world and test them against sampled data. Instead, the complexities of huge and totalising datasets would be processed by immense computing clusters to produce truth itself: “With enough data, the numbers speak for themselves.” As an example, Anderson cited Google’s translation algorithms which, with no knowledge of the underlying structures of languages, were capable of inferring the relationship between them using extensive corpora of translated texts. He extended this approach to genomics, neurology and physics, where scientists are increasingly turning to massive computation to make sense of the volumes of information they have gathered about complex systems. In the age of big data, he argued, “Correlation is enough. We can stop looking for models.”

This belief in the power of data, of technology untrammelled by petty human worldviews, is the practical cousin of more metaphysical assertions. A belief in the unquestionability of data leads directly to a belief in the truth of data-derived assertions. And if data contains truth, then it will, without moral intervention, produce better outcomes. Speaking at Google’s private London Zeitgeist conference in 2013, Eric Schmidt, Google Chairman, asserted that “if they had had cellphones in Rwanda in 1994, the genocide would not have happened.” Schmidt’s claim was that technological visibility – the rendering of events and actions legible to everyone – would change the character of those actions. Not only is this statement historically inaccurate (there was plenty of evidence available of what was occurring during the genocide from UN officials, US satellite photographs and other sources), it’s also demonstrably untrue. Analysis of unrest in Kenya in 2007, when over 1,000 people were killed in ethnic conflicts, showed that mobile phones not only spread but accelerated the violence. But you don’t need to look to such extreme examples to see how a belief in technological determinism underlies much of our thinking and reasoning about the world.

“Big data” is not merely a business buzzword, but a way of seeing the world. Driven by technology, markets and politics, it has come to determine much of our thinking, but it is flawed and dangerous. It runs counter to our actual findings when we employ such technologies honestly and with the full understanding of their workings and capabilities. This over-reliance on data, which I call “quantified thinking”, has come to undermine our ability to reason meaningfully about the world, and its effects can be seen across multiple domains.

The assertion is hardly new. Writing in the Dialectic of Enlightenment in 1947, Theodor Adorno and Max Horkheimer decried “the present triumph of the factual mentality” – the predecessor to quantified thinking – and succinctly analysed the big data fallacy, set out by Anderson above. “It does not work by images or concepts, by the fortunate insights, but refers to method, the exploitation of others’ work, and capital … What men want to learn from nature is how to use it in order wholly to dominate it and other men. That is the only aim.” What is different in our own time is that we have built a world-spanning network of communication and computation to test this assertion. While it occasionally engenders entirely new forms of behaviour and interaction, the network most often shows to us with startling clarity the relationships and tendencies which have been latent or occluded until now. In the face of the increased standardisation of knowledge, it becomes harder and harder to argue against quantified thinking, because the advances of technology have been conjoined with the scientific method and social progress. But as I hope to show, technology ultimately reveals its limitations….

“Eroom’s law” – Moore’s law backwards – was recently formulated to describe a problem in pharmacology. Drug discovery has been getting more expensive. Since the 1950s the number of drugs approved for use in human patients per billion US dollars spent on research and development has halved every nine years. This problem has long perplexed researchers. According to the principles of technological growth, the trend should be in the opposite direction. In a 2012 paper in Nature entitled “Diagnosing the decline in pharmaceutical R&D efficiency” the authors propose and investigate several possible causes for this. They begin with social and physical influences, such as increased regulation, increased expectations and the exhaustion of easy targets (the “low hanging fruit” problem). Each of these are – with qualifications – disposed of, leaving open the question of the discovery process itself….(More)

Federal Privacy Council’s Law Library

Curated on November 15, 2016August 20, 2018 by Stefaan Verhulst

Federal Privacy Council: “The Law Library is a compilation of information about and links to select Federal laws related to the creation, collection, use, processing, storage, maintenance, dissemination, disclosure, and disposal of personally identifiable information (PII) by departments and agencies within the Federal Government. The Law Library does not include all laws that are relevant to privacy or the management of PII in the Federal Government.

The Law Library only includes laws applicable to the Federal Government. Although some of the laws included may also be applicable to entities outside of the Federal Government, the information provided on the Law Library pages is strictly limited to the application of those laws to the Federal Government; the information provided does not in any way address the application of any law to the private sector or other non-Federal entities.

The Law Library pages have been prepared by members of the Federal Privacy Council and consist of information from and links to other Federal Government websites. The Federal Privacy Council is not responsible for the content of any third-party website, and links to other websites do not constitute or imply endorsement or recommendation of those sites or the information they provide.

The material in the Law Library is provided for informational purposes only. The information provided may not reflect current legal developments or agency-specific requirements, and it may not be correct or complete. The Federal Privacy Council does not have authority to provide legal advice, to set policies for the Federal Government, or to represent the views of the Federal Government or the views of any agency within the Federal Government; accordingly, the information on this website in no way constitutes policy or legal advice, nor does it in any way reflect Federal Government views or opinions. Agencies shall consult law, regulation, and policy, including OMB guidance, to understand applicable requirements….(More)”

Transforming government through digitization

Curated on November 14, 2016October 24, 2018 by Stefaan Verhulst

Bjarne Corydon, Vidhya Ganesan, and Martin Lundqvist at McKinsey: “By digitizing processes and making organizational changes, governments can enhance services, save money, and improve citizens’ quality of life.

As companies have transformed themselves with digital technologies, people are calling on governments to follow suit. By digitizing, governments can provide services that meet the evolving expectations of citizens and businesses, even in a period of tight budgets and increasingly complex challenges. Our estimates suggest that government digitization, using current technology, could generate over $1 trillion annually worldwide.

Digitizing a government requires attention to two major considerations: the core capabilities for engaging citizens and businesses, and the organizational enablers that support those capabilities (exhibit). These make up a framework for setting digital priorities. In this article, we look at the capabilities and enablers in this framework, along with guidelines and real-world examples to help governments seize the opportunities that digitization offers.

A digital government has core capabilities supported by organizational enablers.

Governments typically center their digitization efforts on four capabilities: services, processes, decisions, and data sharing. For each, we believe there is a natural progression from quick wins to transformative efforts….(More)”

See also: Digital by default: A guide to transforming government (PDF–474KB) and “Never underestimate the importance of good government,” a New at McKinsey blog post with coauthor Bjarne Corydon, director of the McKinsey Center for Government.

Environmental Law, Big Data, and the Torrent of Singularities

Curated on November 9, 2016August 3, 2018 by Stefaan Verhulst

Essay by William Boyd: “How will big data impact environmental law in the near future? This Essay imagines one possible future for environmental law in 2030 that focuses on the implications of big data for the protection of public health from risks associated with pollution and industrial chemicals. It assumes the perspective of an historian looking back from the end of the twenty-first century at the evolution of environmental law during the late twentieth and early twenty-first centuries.

The premise of the Essay is that big data will drive a major shift in the underlying knowledge practices of environmental law (along with other areas of law focused on health and safety). This change in the epistemic foundations of environmental law, it is argued, will in turn have important, far-reaching implications for environmental law’s normative commitments and for its ability to discharge its statutory responsibilities. In particular, by significantly enhancing the ability of environmental regulators to make harm more visible and more traceable, big data will put considerable pressure on previous understandings of acceptable risk across populations, pushing toward a more singular and more individualized understanding of harm. This will raise new and difficult questions regarding environmental law’s capacity to confront and take responsibility for the actual lives caught up in the tragic choices it is called upon to make. In imagining this near future, the Essay takes a somewhat exaggerated and, some might argue, overly pessimistic view of the implications of big data for environmental law’s efforts to protect public health. This is done not out of a conviction that such a future is likely, but rather to highlight some of the potential problems that may arise as big data becomes a more prominent part of environmental protection. In an age of data triumphalism, such a perspective, it is hoped, may provide grounds for a more critical engagement with the tools and knowledge practices that inform environmental law and the implications of those tools for environmental law’s ability to meet its obligations. Of course, there are other possible futures, and big data surely has the potential to make many positive contributions to environmental protection in the coming decades. Whether it will do so will depend in no small part on the collective choices we make to manage these new capabilities in the years ahead….(More)”

The Risk to Civil Liberties of Fighting Crime With Big Data

Curated on November 7, 2016August 3, 2018 by Stefaan Verhulst

Quentin Hardy in the New York Times: “…Sharing data, both among the parts of a big police department and between the police and the private sector, “is a force multiplier,” he said.

Companies working with the military and intelligence agencies have long practiced these kinds of techniques, which the companies are bringing to domestic policing, in much the way surplus military gear has beefed upAmerican SWAT teams.

Palantir first built up its business by offering products like maps of social networks of extremist bombers and terrorist money launderers, and figuring out efficient driving routes to avoid improvised explosive devices.

Palantir used similar data-sifting techniques in New Orleans to spot individuals most associated with murders. Law enforcement departments around Salt Lake City used Palantir to allow common access to 40,000 arrest photos, 520,000 case reports and information like highway and airport data — building human maps of suspected criminal networks.

People in the predictive business sometimes compare what they do to controlling the other side’s “OODA loop,” a term first developed by a fighter pilot and military strategist named John Boyd.

OODA stands for “observe, orient, decide, act” and is a means of managing information in battle.

“Whether it’s war or crime, you have to get inside the other side’s decision cycle and control their environment,” said Robert Stasio, a project manager for cyberanalysis at IBM, and a former United States government intelligence official. “Criminals can learn to anticipate what you’re going to do and shift where they’re working, employ more lookouts.”

IBM sells tools that also enable police to become less predictable, for example, by taking different routes into an area identified as a crime hotspot. It has also conducted studies that show changing tastes among online criminals — for example, a move from hacking retailers’ computers to stealing health care data, which can be used to file for federal tax refunds.

But there are worries about what military-type data analysis means for civil liberties, even among the companies that get rich on it.

“It definitely presents challenges to the less sophisticated type of criminal,but it’s creating a lot of what is called ‘Big Brother’s little helpers,’” Mr.Bowman said. For now, he added, much of the data abundance problem is that “most police aren’t very good at this.”…(More)’

The case against democracy

Curated on November 2, 2016August 3, 2018 by Stefaan Verhulst

Caleb Crain in the New Yorker: “Roughly a third of American voters think that the Marxist slogan “From each according to his ability to each according to his need” appears in the Constitution. About as many are incapable of naming even one of the three branches of the United States government. Fewer than a quarter know who their senators are, and only half are aware that their state has two of them.

Democracy is other people, and the ignorance of the many has long galled the few, especially the few who consider themselves intellectuals. Plato, one of the earliest to see democracy as a problem, saw its typical citizen as shiftless and flighty:

Sometimes he drinks heavily while listening to the flute; at other times, he drinks only water and is on a diet; sometimes he goes in for physical training; at other times, he’s idle and neglects everything; and sometimes he even occupies himself with what he takes to be philosophy.

It would be much safer, Plato thought, to entrust power to carefully educated guardians. To keep their minds pure of distractions—such as family, money, and the inherent pleasures of naughtiness—he proposed housing them in a eugenically supervised free-love compound where they could be taught to fear the touch of gold and prevented from reading any literature in which the characters have speaking parts, which might lead them to forget themselves. The scheme was so byzantine and cockamamie that many suspect Plato couldn’t have been serious; Hobbes, for one, called the idea “useless.”

A more practical suggestion came from J. S. Mill, in the nineteenth century: give extra votes to citizens with university degrees or intellectually demanding jobs. (In fact, in Mill’s day, select universities had had their own constituencies for centuries, allowing someone with a degree from, say, Oxford to vote both in his university constituency and wherever he lived. The system wasn’t abolished until 1950.) Mill’s larger project—at a time when no more than nine per cent of British adults could vote—was for the franchise to expand and to include women. But he worried that new voters would lack knowledge and judgment, and fixed on supplementary votes as a defense against ignorance.

In the United States, élites who feared the ignorance of poor immigrants tried to restrict ballots. In 1855, Connecticut introduced the first literacy test for American voters. Although a New York Democrat protested, in 1868, that “if a man is ignorant, he needs the ballot for his protection all the more,” in the next half century the tests spread to almost all parts of the country. They helped racists in the South circumvent the Fifteenth Amendment and disenfranchise blacks, and even in immigrant-rich New York a 1921 law required new voters to take a test if they couldn’t prove that they had an eighth-grade education. About fifteen per cent flunked. Voter literacy tests weren’t permanently outlawed by Congress until 1975, years after the civil-rights movement had discredited them.

Worry about voters’ intelligence lingers, however. …In a new book, “Against Democracy” (Princeton), Jason Brennan, a political philosopher at Georgetown, has turned Estlund’s hedging inside out to create an uninhibited argument for epistocracy. Against Estlund’s claim that universal suffrage is the default, Brennan argues that it’s entirely justifiable to limit the political power that the irrational, the ignorant, and the incompetent have over others. To counter Estlund’s concern for fairness, Brennan asserts that the public’s welfare is more important than anyone’s hurt feelings; after all, he writes, few would consider it unfair to disqualify jurors who are morally or cognitively incompetent. As for Estlund’s worry about demographic bias, Brennan waves it off. Empirical research shows that people rarely vote for their narrow self-interest; seniors favor Social Security no more strongly than the young do. Brennan suggests that since voters in an epistocracy would be more enlightened about crime and policing, “excluding the bottom 80 percent of white voters from voting might be just what poor blacks need.”…(More)”

Learning Privacy Expectations by Crowdsourcing Contextual Informational Norms

Curated on November 1, 2016August 20, 2018 by Stefaan Verhulst

Yan Shvartzshnaider at Freedom to Tinker: “The advent of social apps, smart phones and ubiquitous computing has brought a great transformation to our day-to-day life. The incredible pace with which the new and disruptive services continue to emerge challenges our perception of privacy. To keep apace with this rapidly evolving cyber reality, we need to devise agile methods and frameworks for developing privacy-preserving systems that align with evolving user’s privacy expectations.

Previous efforts have tackled this with the assumption that privacy norms are provided through existing sources such law, privacy regulations and legal precedents. They have focused on formally expressing privacy norms and devising a corresponding logic to enable automatic inconsistency checks and efficient enforcement of the logic.

However, because many of the existing regulations and privacy handbooks were enacted well before the Internet revolution took place, they often lag behind and do not adequately reflect the application of logic in modern systems. For example, the Family Rights and Privacy Act (FERPA) was enacted in 1974, long before Facebook, Google and many other online applications were used in an educational context. More recent legislation faces similar challenges as novel services introduce new ways to exchange information, and consequently shape new, unconsidered information flows that can change our collective perception of privacy.

Crowdsourcing Contextual Privacy Norms

Armed with the theory of Contextual Integrity (CI) in our work, we are exploring ways to uncover societal norms by leveraging the advances in crowdsourcing technology.

In our recent paper, we present the methodology that we believe can be used to extract a societal notion of privacy expectations. The results can be used to fine tune the existing privacy guidelines as well as get a better perspective on the users’ expectations of privacy.

CI defines privacy as collection of norms (privacy rules) that reflect appropriate information flows between different actors. Norms capture who shares what, with whom, in what role, and under which conditions. For example, while you are comfortable sharing your medical information with your doctor, you might be less inclined to do so with your colleagues.

We use CI as a proxy to reason about privacy in the digital world and a gateway to understanding how people perceive privacy in a systematic way. Crowdsourcing is a great tool for this method. We are able to ask hundreds of people how they feel about a particular information flow, and then we can capture their input and map it directly onto the CI parameters. We used a simple template to write Yes-or-No questions to ask our crowdsourcing participants:

“Is it acceptable for the [sender] to share the [subject’s] [attribute] with [recipient] [transmission principle]?”

For example:

“Is it acceptable for the student’s professor to share the student’s record of attendance with the department chair if the student is performing poorly? ”

In our experiments, we leveraged Amazon’s Mechanical Turk (AMT) to ask 450 turkers over 1400 such questions. Each question represents a specific contextual information flow that users can approve, disapprove or mark under the Doesn’t Make Sense category; the last category could be used when 1) the sender is unlikely to have the information, 2) the receiver would already have the information, or 3) the question is ambiguous….(More)”