Global Standards in National Contexts: The Role of Transnational Multi-Stakeholder Initiatives in Public Sector Governance Reform

Curated on Posted on by Stefaan Verhulst

Paper by Brandon Brockmyer: “Multi-stakeholder initiatives (i.e., partnerships between governments, civil society, and the private sector) are an increasingly prevalent strategy promoted by multilateral, bilateral, and nongovernmental development organizations for addressing weaknesses in public sector governance. Global public sector governance MSIs seek to make national governments more transparent and accountable by setting shared standards for information disclosure and multi- stakeholder collaboration. However, research on similar interventions implemented at the national or subnational level suggests that the effectiveness of these initiatives is likely to be mediated by a variety of socio-political factors.

This dissertation examines the transnational evidence base for three global public sector governance MSIs — the Extractive Industries Transparency Initiative, the Construction Sector Transparency Initiative, and the Open Government Partnership — and investigates their implementation within and across three shared national contexts — Guatemala, the Philippines, and Tanzania — in order to determine whether and how these initiatives lead to improvements in proactive transparency (i.e., discretionary release of government data), demand-driven transparency (i.e., reforms that increase access to government information upon request), and accountability (i.e., the extent to which government officials are compelled to publicly explain their actions and/or face penalties or sanction for them), as well as the extent to which they provide participating governments with an opportunity to project a public image of transparency and accountability, while maintaining questionable practices in these areas (i.e., openwashing).

The evidence suggests that global public sector governance MSIs often facilitate gains in proactive transparency by national governments, but that improvements in demand-driven transparency and accountability remain relatively rare. Qualitative comparative analysis reveals that a combination of multi-stakeholder power sharing and civil society capacity is sufficient to drive improvements in proactive transparency, while the absence of visible, high-level political support is sufficient to impede such reforms. The lack of demand-driven transparency or accountability gains suggests that national-level coalitions forged by global MSIs are often too narrow to successfully advocate for broader improvements to public sector governance. Moreover, evidence for openwashing was found in one-third of cases, suggesting that national governments sometimes use global MSIs to deliberately mislead international observers and domestic stakeholders about their commitment to reform….(More)”

The Algorithm as a Human Artifact: Implications for Legal {Re}Search

Curated on Posted on by Stefaan Verhulst

Paper by Susan Nevelow Mart: “When legal researchers search in online databases for the information they need to solve a legal problem, they need to remember that the algorithms that are returning results to them were designed by humans. The world of legal research is a human-constructed world, and the biases and assumptions the teams of humans that construct the online world bring to the task are imported into the systems we use for research. This article takes a look at what happens when six different teams of humans set out to solve the same problem: how to return results relevant to a searcher’s query in a case database. When comparing the top ten results for the same search entered into the same jurisdictional case database in Casetext, Fastcase, Google Scholar, Lexis Advance, Ravel, and Westlaw, the results are a remarkable testament to the variability of human problem solving. There is hardly any overlap in the cases that appear in the top ten results returned by each database. An average of forty percent of the cases were unique to one database, and only about 7% of the cases were returned in search results in all six databases. It is fair to say that each different set of engineers brought very different biases and assumptions to the creation of each search algorithm. One of the most surprising results was the clustering among the databases in terms of the percentage of relevant results. The oldest database providers, Westlaw and Lexis, had the highest percentages of relevant results, at 67% and 57%, respectively. The newer legal database providers, Fastcase, Google Scholar, Casetext, and Ravel, were also clustered together at a lower relevance rate, returning approximately 40% relevant results.

Legal research has always been an endeavor that required redundancy in searching; one resource does not usually provide a full answer, just as one search will not provide every necessary result. The study clearly demonstrates that the need for redundancy in searches and resources has not faded with the rise of the algorithm. From the law professor seeking to set up a corpus of cases to study, the trial lawyer seeking that one elusive case, the legal research professor showing students the limitations of algorithms, researchers who want full results will need to mine multiple resources with multiple searches. And more accountability about the nature of the algorithms being deployed would allow all researchers to craft searches that would be optimally successful….(More)”.

Discrimination by algorithm: scientists devise test to detect AI bias

Curated on Posted on by Stefaan Verhulst

 at the Guardian: “There was the voice recognition software that struggled to understand women, the crime prediction algorithm that targeted black neighbourhoods and the online ad platform which was more likely to show men highly paid executive jobs.

Concerns have been growing about AI’s so-called “white guy problem” and now scientists have devised a way to test whether an algorithm is introducing gender or racial biases into decision-making.

Mortiz Hardt, a senior research scientist at Google and a co-author of the paper, said: “Decisions based on machine learning can be both incredibly useful and have a profound impact on our lives … Despite the need, a vetted methodology in machine learning for preventing this kind of discrimination based on sensitive attributes has been lacking.”

The paper was one of several on detecting discrimination by algorithms to be presented at the Neural Information Processing Systems (NIPS) conference in Barcelona this month, indicating a growing recognition of the problem.

Nathan Srebro, a computer scientist at the Toyota Technological Institute at Chicago and co-author, said: “We are trying to enforce that you will not have inappropriate bias in the statistical prediction.”

The test is aimed at machine learning programs, which learn to make predictions about the future by crunching through vast quantities of existing data. Since the decision-making criteria are essentially learnt by the computer, rather than being pre-programmed by humans, the exact logic behind decisions is often opaque, even to the scientists who wrote the software….“Our criteria does not look at the innards of the learning algorithm,” said Srebro. “It just looks at the predictions it makes.”

Their approach, called Equality of Opportunity in Supervised Learning, works on the basic principle that when an algorithm makes a decision about an individual – be it to show them an online ad or award them parole – the decision should not reveal anything about the individual’s race or gender beyond what might be gleaned from the data itself.

For instance, if men were on average twice as likely to default on bank loans than women, and if you knew that a particular individual in a dataset had defaulted on a loan, you could reasonably conclude they were more likely (but not certain) to be male.

However, if an algorithm calculated that the most profitable strategy for a lender was to reject all loan applications from men and accept all female applications, the decision would precisely confirm a person’s gender.

“This can be interpreted as inappropriate discrimination,” said Srebro….(More)”.

Science Can Restore America’s Faith in Democracy

Curated on Posted on by Stefaan Verhulst

Ariel Procaccia in Wired: “…Like most other countries, individual states in the US employ the antiquated plurality voting system, in which each voter casts a vote for a single candidate, and the person who amasses the largest number of votes is declared the winner. If there is one thing that voting experts unanimously agree on, it is that plurality voting is a bad idea, or at least a badly outdated one….. Maine recently became the first US state to adopt instant-runoff voting; the approach will be used for choosing the governor and members of Congress and the state legislature….

So why aren’t we already using cutting-edge voting systems in national elections? Perhaps because changing election systems usually itself requires an election, where short-term political considerations may trump long-term, scientifically grounded reasoning….Despite these difficulties, in the last few years state-of-the-art voting systems have made the transition from theory to practice, through not-for-profit online platforms that focus on facilitating elections in cities and organizations, or even just on helping a group of friends decide where to go to dinner. For example, the Stanford Crowdsourced Democracy Team has created an online tool whereby residents of a city can vote on how to allocate the city’s budget for public projects such as parks and roads. This tool has been used by New York City, Boston, Chicago, and Seattle to allocate millions of dollars. Building on this success, the Stanford team is experimenting with groundbreaking methods, inspired by computational thinking, to elicit and aggregate the preferences of residents.

The Princeton-based project All Our Ideas asks voters to compare pairs of ideas, and then aggregates these comparisons via statistical methods, ultimately providing a ranking of all the ideas. To date, roughly 14 million votes have been cast using this system, and it has been employed by major cities and organizations. Among its more whimsical use cases is the Washington Post’s 2010 holiday gift guide, where the question was “what gift would you like to receive this holiday season”; the disappointingly uncreative top idea, based on tens of thousands of votes, was “money”.

Finally, the recently launched website RoboVote (which I created with collaborators at Carnegie Mellon and Harvard) offers AI-driven voting methods to help groups of people make smart collective decisions. Applications range from selecting a spot for a family vacation or a class president, to potentially high-stakes choices such as which product prototype to develop or which movie script to produce.

These examples show that centuries of research on voting can, at long last, make a societal impact in the internet age. They demonstrate what science can do for democracy, albeit on a relatively small scale, for now….(More)’

Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability

Curated on Posted on by Stefaan Verhulst

Mike Ananny and Kate Crawford in New Media and Society: “Models for understanding and holding systems accountable have long rested upon ideals and logics of transparency. Being able to see a system is sometimes equated with being able to know how it works and govern it—a pattern that recurs in recent work about transparency and computational systems. But can “black boxes’ ever be opened, and if so, would that ever be sufficient? In this article, we critically interrogate the ideal of transparency, trace some of its roots in scientific and sociotechnical epistemological cultures, and present 10 limitations to its application. We specifically focus on the inadequacy of transparency for understanding and governing algorithmic systems and sketch an alternative typology of algorithmic accountability grounded in constructive engagements with the limitations of transparency ideals….(More)”

New Tech Helps Tenants Make Their Case in Court

Curated on Posted on by Stefaan Verhulst

Corinne Ramey at the Wall Street Journal: “Tenants and their advocates are using new technology to document a lack of heat in apartment buildings, a condition they say has been difficult to prove in housing-court cases.

Small sensors provided by the New York City-based nonprofit Heat Seek, now installed in some city apartments, measure temperatures and transmit the data to a server. Tenant advocates say the data buttress their contention that some landlords withhold heat as a way to oust rent-regulated tenants.

“It’s really exciting to be able to track this information and hopefully get the courts to accept it, so we can show what everybody knows to be true,” said Sunny Noh, a supervising attorney at the Legal Aid Society’s Tenant Rights Coalition, which filed a civil suit using Heat Seek’s data.

…The smaller device, called a cell, transmits hourly temperature readings to the larger device, called a hub, through a radio signal. The hub is equipped with a small modem, which it uses to send data to a web server. The nonprofit typically places a hub in each building and the cells inside individual apartments.

Last winter, Heat Seek’s data were used in eight court cases representing a total of about 20 buildings, all of which were settled, Ms. Francois said. Currently, the sensors are in about six buildings and she anticipates about 50 buildings will have them by the end of the winter.

Data from Heat Seek haven’t been admitted at a trial yet, said Ms. Noh, the Legal Aid lawyer.

The nonprofit, with help from housing lawyers, has chosen to focus on gentrifying neighborhoods because they expect landlords there have more incentives to force out rent-regulated tenants….(More)

Privacy of Public Data

Curated on Posted on by Stefaan Verhulst

Paper by Kirsten E. Martin and Helen Nissenbaum: “The construct of an information dichotomy has played a defining role in regulating privacy: information deemed private or sensitive typically earns high levels of protection, while lower levels of protection are accorded to information deemed public or non-sensitive. Challenging this dichotomy, the theory of contextual integrity associates privacy with complex typologies of information, each connected with respective social contexts. Moreover, it contends that information type is merely one among several variables that shape people’s privacy expectations and underpin privacy’s normative foundations. Other contextual variables include key actors – information subjects, senders, and recipients – as well as the principles under which information is transmitted, such as whether with subjects’ consent, as bought and sold, as required by law, and so forth. Prior work revealed the systematic impact of these other variables on privacy assessments, thereby debunking the defining effects of so-called private information.

In this paper, we shine a light on the opposite effect, challenging conventional assumptions about public information. The paper reports on a series of studies, which probe attitudes and expectations regarding information that has been deemed public. Public records established through the historical practice of federal, state, and local agencies, as a case in point, are afforded little privacy protection, or possibly none at all. Motivated by progressive digitization and creation of online portals through which these records have been made publicly accessible our work underscores the need for more concentrated and nuanced privacy assessments, even more urgent in the face of vigorous open data initiatives, which call on federal, state, and local agencies to provide access to government records in both human and machine readable forms. Within a stream of research suggesting possible guard rails for open data initiatives, our work, guided by the theory of contextual integrity, provides insight into the factors systematically shaping individuals’ expectations and normative judgments concerning appropriate uses of and terms of access to information.

Using a factorial vignette survey, we asked respondents to rate the appropriateness of a series of scenarios in which contextual elements were systematically varied; these elements included the data recipient (e.g. bank, employer, friend,.), the data subject, and the source, or sender, of the information (e.g. individual, government, data broker). Because the object of this study was to highlight the complexity of people’s privacy expectations regarding so-called public information, information types were drawn from data fields frequently held in public government records (e.g. voter registration, marital status, criminal standing, and real property ownership).

Our findings are noteworthy on both theoretical and practical grounds. In the first place, they reinforce key assertions of contextual integrity about the simultaneous relevance to privacy of other factors beyond information types. In the second place, they reveal discordance between truisms that have frequently shaped public policy relevant to privacy. …(More)”

 

The Econocracy: The perils of leaving economics to the experts

Curated on Posted on by Stefaan Verhulst

Cover

Book by Joe Earle, Cahal Moran, Zach Ward-Perkins, and Series edited by Mick Moran: “One hundred years ago the idea of ‘the economy’ didn’t exist. Now, improving the economy has come to be seen as perhaps the most important task facing modern societies. Politics and policymaking are conducted in the language of economics and economic logic shapes how political issues are thought about and addressed. The result is that the majority of citizens, who cannot speak this language, are locked out of politics while political decisions are increasingly devolved to experts. The econocracy explains how economics came to be seen this way – and the damaging consequences. It opens up the discipline and demonstrates its inner workings to the wider public so that the task of reclaiming democracy can begin….(More)”

How Artificial Intelligence Will Usher in the Next Stage of E-Government

Curated on Posted on by Stefaan Verhulst

Daniel Castro at GovTech: “Since the earliest days of the Internet, most government agencies have eagerly explored how to use technology to better deliver services to citizens, businesses and other public-sector organizations. Early on, observers recognized that these efforts often varied widely in their implementation, and so researchers developed various frameworks to describe the different stages of growth and development of e-government. While each model is different, they all identify the same general progression from the informational, for example websites that make government facts available online, to the interactive, such as two-way communication between government officials and users, to the transactional, like applications that allow users to access government services completely online.

However, we will soon see a new stage of e-government: the perceptive.

The defining feature of the perceptive stage will be that the work involved in interacting with government will be significantly reduced and automated for all parties involved. This will come about principally from the integration of artificial intelligence (AI) — computer systems that can learn, reason and decide at levels similar to that of a human — into government services to make it more insightful and intelligent.

Consider the evolution of the Department of Motor Vehicles. The informational stage made it possible for users to find the hours for the local office; the interactive stage made it possible to ask the agency a question by email; and the transactional stage made it possible to renew a driver’s license online.

In the perceptive stage, the user will simply say, “Siri, I need a driver’s license,” and the individual’s virtual assistant will take over — collecting any additional information from the user, coordinating with the government’s system and scheduling any in-person meetings automatically. That’s right: AI might finally end your wait at the DMV.

In general, there are at least three ways that AI will impact government agencies. First, it will enable government workers to be more productive since the technology can be used to automate many tasks. …

Second, AI will create a faster, more responsive government. AI enables the creation of autonomous, intelligent agents — think online chatbots that answer citizens’ questions, real-time fraud detection systems that constantly monitor government expenditures and virtual legislative assistants that quickly synthesize feedback from citizens to lawmakers.

Third, AI will allow people to interact more naturally with digital government services…(More)”

Artificial Intelligence Could Help Colleges Better Plan What Courses They Should Offer

Curated on Posted on by Stefaan Verhulst

Jeffrey R. Young at EdSsurge: Big data could help community colleges better predict how industries are changing so they can tailor their IT courses and other programs. After all, if Amazon can forecast what consumers will buy and prestock items in their warehouses to meet the expected demand, why can’t colleges do the same thing when planning their curricula, using predictive analytics to make sure new degree or certificates programs are started just in time for expanding job opportunities?

That’s the argument made by Gordon Freedman, president of the nonprofit National Laboratory for Education Transformation. He’s part of a new center that will do just that, by building a data warehouse that brings together up-to-date information on what skills employers need and what colleges currently offer—and then applying artificial intelligence to attempt to predict when sectors or certain employment needs might be expanding.

He calls the approach “opportunity engineering,” and the center boasts some heavy-hitting players to assist in the efforts, including the University of Chicago, the San Diego Supercomputing Center and Argonne National Laboratory. It’s called the National Center for Opportunity Engineering & Analysis.

Ian Roark, vice president of workforce development at Pima Community College in Arizona, is among those eager for this kind of “opportunity engineering” to emerge.

He explains when colleges want to start new programs, they face a long haul—it takes time to develop a new curriculum, put it through an internal review, and then send it through an accreditor….

Other players are already trying to translate the job market into a giant data set to spot trends. LinkedIn sits on one of the biggest troves of data, with hundreds of millions of job profiles, and ambitions to create what it calls the “economic graph” of the economy. But not everyone is on LinkedIn, which attracts mainly those in white-collar jobs. And companies such as Burning Glass Technologies have scanned hundreds of thousands of job listings and attempt to provide real-time intelligence on what employers say they’re looking for. Those still don’t paint the full picture, Freedman argues, such as what jobs are forming at companies.

“We need better information from the employer, better information from the job seeker and better information from the college, and that’s what we’re going after,” Freedman says…(More)”.