Paper by Amanda Clarke: “From 2011 onwards, Digital Government Units (DGUs) have quickly emerged as a preferred solution for tackling the over-cost and under-performing digital services and lagging digital transformation agendas plaguing today’s governments. DGUs represent a common machinery of government phenomenon insofar as they all exist at the centre of the state, and adopt a shared orthodoxy, favouring agile, user-centric design, open-source technologies, pluralistic procurement, data-driven decision-making, horizontal ‘platform’ based solutions and a ‘delivery-first’ ethos. However, DGUs are differentiated in practice by their governance structures, resources and powers, adding notable complexity to this recent public management trend. Acknowledging the speedy policy transfer that has seen DGUs spread globally despite a lack of critical appraisal of their value and shortcomings, the paper highlights four critical considerations that governments and their observers should account for when assessing DGUs as a potential instrument of digital era public management renewal….(More)”.
Open data on universities – New fuel for transformation
François van Schalkwyk at University World News: “Accessible, usable and relevant open data on South African universities makes it possible for a wide range of stakeholders to monitor, advise and challenge the transformation of South Africa’s universities from an informed perspective.
Some describe data as the new oil while others suggest it is a new form of capital or compare it to electricity. Either way, there appears to be a groundswell of interest in the potential of data to fuel development.
Whether the proliferation of data is skewing development in favour of globally networked elites or disrupting existing asymmetries of information and power, is the subject of ongoing debate. Certainly, there are those who will claim that open data, from a development perspective, could catalyse disruption and redistribution.
Open data is data that is free to use without restriction. Governments and their agencies, universities and their researchers, non-governmental organisations and their donors, and even corporations, are all potential sources of open data.
Open government data, as a public rather than a private resource, embedded in principles of universal access, participation and transparency, is touted as being able to restore the deteriorating levels of trust between citizens and their governments.
Open data promises to do so by making the decisions and processes of the state more transparent and inclusive, empowering citizens to participate and to hold public institutions to account for the distribution of public services and resources.
Benefits of open data
Open data has other benefits over its more cloistered cousins (data in private networks, big data, etc). By democratising access, open data makes possible the use of data on, for example, health services, crime, the environment, procurement and education by a range of different users, each bringing their own perspective to bear on the data. This can expose bias in the data or may improve the quality of the data by surfacing data errors. Both are important when data is used to shape government policies.
By removing barriers to reusing data such as copyright or licence-fees, tech-savvy entrepreneurs can develop applications to assist the public to make more informed decisions by making available easy-to-understand information on medicine prices, crime hot-spots, air quality, beneficial ownership, school performance, etc. And access to open research data can improve quality and efficiency in science.
Scientists can check and confirm the data on which important discoveries are based if the data is open, and, in some cases, researchers can reuse open data from other studies, saving them the cost and effort of collecting the data themselves.
‘Open washing’
But access alone is not enough for open data to realise its potential. Open data must also be used. And data is used if it holds some value for the user. Governments have been known to publish server rooms full of data that no one is interested in to support claims of transparency and supporting the knowledge economy. That practice is called ‘open washing’. …(More)”
Features of Parliamentary Websites in Selected Jurisdictions
Report by The Law Library of Congress, Global Legal Research Center: “In recent years, parliaments around the world have enhanced their websites in order to improve access to legislative information and other parliamentary resources. Innovative features allow constituents and researchers to locate and utilize detailed information on laws and lawmaking in various ways. These include tracking tools and alerts, apps, the use of open data technology, and different search functions. In order to demonstrate some of the developments in this area, staff from the Global Legal Research Directorate of the Law Library of Congress surveyed the official parliamentary websites of fifty countries from all regions of the world, plus the website of the European Parliament. In some cases, information on more than one website is provided where separate sites have been established for different chambers of the national parliament, bringing the total number of individual websites surveyed to seventy.
While the information on the parliamentary websites is primarily in the national language of the particular country, around forty of the individual websites surveyed were found to provide at least limited information in one or more other languages. The European Parliament website can be translated into any of the twenty-four official languages of the members of the European Union.
All of the parliamentary websites included in the survey have at least basic browse tools that allow users to view legislation in a list format, and that may allow for viewing in, for example, date or title order. All of the substantive websites also enable searching, often providing a general search box for the whole site at the top of each page as well as more advanced search options for different types of documents. Some sites provide various facets that can be used to further narrow searches.
Around thirty-nine of the individual websites surveyed provide users with some form of tracking or alert function to receive updates on certain documents (including proposed legislation), parliamentary news, committee activities, or other aspects of the website. This includes the ability to subscribe to different RSS feeds and/or email alerts.
The ability to watch live or recorded proceedings of different parliaments, including debates within the relevant chamber as well as committee hearings, is a common feature of the parliamentary websites surveyed. Fifty-eight of the websites surveyed featured some form of video, including links to dedicated YouTube channels, specific pages where users can browse and search for embedded videos, and separate video services or portals that are linked to or viewable from the main site. Some countries also make videos available on dedicated mobile-friendly sites or apps, including Denmark, Germany, Ireland, the Netherlands, and New Zealand. In total, apps containing parliamentary information are provided in just fourteen of the countries surveyed. In comparison, the parliamentary websites of thirty countries are available in mobile-friendly formats, enabling easy access to information and different functionalities using smartphones and tablets.
The table also provides information on some of the additional special features available on the surveyed websites. Examples include dedicated sites or pages that provide educational information about the parliament for children (Argentina, El Salvador, Germany, Israel, Netherlands, Spain, Taiwan, Turkey); calendar functions, including those that allow users to save information to their personal calendars or otherwise view information about different types of proceedings or events (available on at least twenty websites); and open data portals or other features that allow information to be downloaded in bulk for reuse or analysis, including through the use of APIs (application programming interfaces) (at least six countries)….(More)”.
Formalised data citation practices would encourage more authors to make their data available for reuse
Hyoungjoo Park and Dietmar Wolfram at the LSE Impact Blog: “Today’s researchers work in a heavily data-intensive and collaborative environment in order to further scientific discovery across and within fields. It is becoming routine for researchers (i.e. authors and data publishers) to submit their research data, such as datasets, biological samples in biomedical fields, and computer code, as supplementary information in order to comply with data sharing requirements of major funding agencies, high-profile journals, and data journals. This is part of open science, where data and any publication products are expected to be made available to anyone interested.
Given that researchers benefit from publicly shared data through data reuse in their own research, researchers who provide access to data should be acknowledged for their contributions, much in the same way that authors are recognised for their research publications through citation. Researchers who use shared data or other shared research products (e.g. open access software, tissue cultures) should also acknowledge the providers of these resources through formal citation. At present, data citation is not widely practised in most disciplines and as an object of study remains largely overlooked….
We found that data citations appear in the references section of an article less frequently than in the main text, making it difficult to identify the reward and credit for data authors (i.e. data sharers). Consistent data citation formats could not be found. Current data citation practices do not (yet) benefit data sharers. Also, data citation was sometimes located in the supplementary information, outside of the references. Data that had been reused was often not acknowledged in the reference lists, but was rather hidden in the representation of data (e.g. tables, figures, images, graphs, and other elements), which may be a consequence of the fact that data citation practices are not yet common in scholarly communications.
Ongoing challenges remain in identifying and documenting data citation. First, the practice of informal data citation presents a challenge for accurately documenting data citation. …
Second, data recitation by one or more co-authors of earlier studies (i.e. self-citation) is common, which reduces the broader impact of data sharing by limiting much of the reuse to the original authors..
Third, currently indexed data citations may not include rapidly advancing areas, such as in the hard sciences or computer engineering, because approximately 90% of indexed works were associated with journal articles…
Fourth, the number of authors associated with shared datasets raises questions of the ownership of and responsibility for a collective work, although some journals require one author to be responsible for the data used in the study…(More). (See also An examination of research data sharing and re-use: implications for data citation practice, published in Scientometrics)
Uber Releases Open Source Project for Differential Privacy
Katie Tezapsidis at Uber Security: “Data analysis helps Uber continuously improve the user experience by preventing fraud, increasing efficiency, and providing important safety features for riders and drivers. Data gives our teams timely feedback about what we’re doing right and what needs improvement.
Uber is committed to protecting user privacy and we apply this principle throughout our business, including our internal data analytics. While Uber already has technical and administrative controls in place to limit who can access specific databases, we are adding additional protections governing how that data is used — even in authorized cases.
We are excited to give a first glimpse of our recent work on these additional protections with the release of a new open source tool, which we’ll introduce below.
Background: Differential Privacy
Differential privacy is a formal definition of privacy and is widely recognized by industry experts as providing strong and robust privacy assurances for individuals. In short, differential privacy allows general statistical analysis without revealing information about a particular individual in the data. Results do not even reveal whether any individual appears in the data. For this reason, differential privacy provides an extra layer of protection against re-identification attacks as well as attacks using auxiliary data.
Differential privacy can provide high accuracy results for the class of queries Uber commonly uses to identify statistical trends. Consequently, differential privacy allows us to calculate aggregations (averages, sums, counts, etc.) of elements like groups of users or trips on the platform without exposing information that could be used to infer details about a specific user or trip.
Differential privacy is enforced by adding noise to a query’s result, but some queries are more sensitive to the data of a single individual than others. To account for this, the amount of noise added must be tuned to the sensitivity of the query, which is defined as the maximum change in the query’s output when an individual’s data is added to or removed from the database.
As part of their job, a data analyst at Uber might need to know the average trip distance in a particular city. A large city, like San Francisco, might have hundreds of thousands of trips with an average distance of 3.5 miles. If any individual trip is removed from the data, the average remains close to 3.5 miles. This query therefore has low sensitivity, and thus requires less noise to enable each individual to remain anonymous within the crowd.
Conversely, the average trip distance in a smaller city with far fewer trips is more influenced by a single trip and may require more noise to provide the same degree of privacy. Differential privacy defines the precise amount of noise required given the sensitivity.
A major challenge for practical differential privacy is how to efficiently compute the sensitivity of a query. Existing methods lack sufficient support for the features used in Uber’s queries and many approaches require replacing the database with a custom runtime engine. Uber uses many different database engines and replacing these databases is infeasible. Moreover, custom runtimes cannot meet Uber’s demanding scalability and performance requirements.
Introducing Elastic Sensitivity
To address these challenges we adopted Elastic Sensitivity, a technique developed by security researchers at the University of California, Berkeley for efficiently calculating the sensitivity of a query without requiring changes to the database. The full technical details of Elastic Sensitivity are described here.
Today, we are excited to share a tool developed in collaboration with these researchers to calculate Elastic Sensitivity for SQL queries. The tool is available now on GitHub. It is designed to integrate easily with existing data environments and support additional state-of-the-art differential privacy mechanisms, which we plan to share in the coming months….(More)”.
Government at a Glance 2017
OECD: “Government at a Glance 2017 provides the latest available data on public administrations in OECD countries. Where possible, it also reports data for Brazil, China, Colombia, Costa Rica, India, Indonesia, Lithuania, the Russian Federation, and South Africa. This edition contains new indicators on public sector emploympent, institutions, budgeting practices and procedures, regulatory governance, risk management and communication, open government data and public sector innovation. This edition also includes for the first time a number of scorecards comparing the level of access, responsiveness and quality of services in three key areas: health care, education and justice.
Each indicator in the publication is presented in a user-friendly format, consisting of graphs and/or charts illustrating variations across countries and over time, brief descriptive analyses highlighting the major findings conveyed by the data, and a methodological section on the definition of the indicator and any limitations in data comparability. A database containing qualitative and quantitative indicators on government is available on line. It is updated twice a year as new data are released. The database, countries fact sheets and other online supplements can be found at www.oecd.org/gov/govataglance.htm.”
AI, people, and society
Eric Horvitz at Science: “In an essay about his science fiction, Isaac Asimov reflected that “it became very common…to picture robots as dangerous devices that invariably destroyed their creators.” He rejected this view and formulated the “laws of robotics,” aimed at ensuring the safety and benevolence of robotic systems. Asimov’s stories about the relationship between people and robots were only a few years old when the phrase “artificial intelligence” (AI) was used for the first time in a 1955 proposal for a study on using computers to “…solve kinds of problems now reserved for humans.” Over the half-century since that study, AI has matured into subdisciplines that have yielded a constellation of methods that enable perception, learning, reasoning, and natural language understanding.
Growing exuberance about AI has come in the wake of surprising jumps in the accuracy of machine pattern recognition using methods referred to as “deep learning.” The advances have put new capabilities in the hands of consumers, including speech-to-speech translation and semi-autonomous driving. Yet, many hard challenges persist—and AI scientists remain mystified by numerous capabilities of human intellect.
Excitement about AI has been tempered by concerns about potential downsides. Some fear the rise of superintelligences and the loss of control of AI systems, echoing themes from age-old stories. Others have focused on nearer-term issues, highlighting potential adverse outcomes. For example, data-fueled classifiers used to guide high-stakes decisions in health care and criminal justice may be influenced by biases buried deep in data sets, leading to unfair and inaccurate inferences. Other imminent concerns include legal and ethical issues regarding decisions made by autonomous systems, difficulties with explaining inferences, threats to civil liberties through new forms of surveillance, precision manipulation aimed at persuasion, criminal uses of AI, destabilizing influences in military applications, and the potential to displace workers from jobs and to amplify inequities in wealth.
As we push AI science forward, it will be critical to address the influences of AI on people and society, on short- and long-term scales. Valuable assessments and guidance can be developed through focused studies, monitoring, and analysis. The broad reach of AI’s influences requires engagement with interdisciplinary groups, including computer scientists, social scientists, psychologists, economists, and lawyers. On longer-term issues, conversations are needed to bridge differences of opinion about the possibilities of superintelligence and malevolent AI. Promising directions include working to specify trajectories and outcomes, and engaging computer scientists and engineers with expertise in software verification, security, and principles of failsafe design….Asimov concludes in his essay, “I could not bring myself to believe that if knowledge presented danger, the solution was ignorance. To me, it always seemed that the solution had to be wisdom. You did not refuse to look at danger, rather you learned how to handle it safely.” Indeed, the path forward for AI should be guided by intellectual curiosity, care, and collaboration….(More)”
Bangalore Taps Tech Crowdsourcing to Fix ‘Unruly’ Gridlock
Saritha Rai at Bloomberg Technology: “In Bangalore, tech giants and startups typically spend their days fiercely battling each other for customers. Now they are turning their attention to a common enemy: the Indian city’s infernal traffic congestion.
Cross-town commutes that can take hours has inspired Gridlock Hackathon, a contest initiated by Flipkart Online Services Pvt. for technology workers to find solutions to the snarled roads that cost the economy billions of dollars. While the prize totals a mere $5,500, it’s attracting teams from global giants Microsoft Corp., Google and Amazon.com. Inc. to local startups including Ola.
The online contest is crowdsourcing solutions for Bangalore, a city of more than 10 million, as it grapples with inadequate roads, unprecedented growth and overpopulation. The technology industry began booming decades ago and with its base of talent, it continues to attract companies. Just last month, Intel Corp. said it would invest $178 million and add more workers to expand its R&D operations.
The ideas put forward at the hackathon range from using artificial intelligence and big data on traffic flows to true moonshots, such as flying cars.
The gridlock remains a problem for a city dependent on its technology industry and seeking to attract new investment…(More)”.
Political Inequality in Affluent Democracies
Larry M. Bartels for the SSRC: “A key characteristic of a democracy,” according to Robert Dahl, is “the continuing responsiveness of the government to the preferences of its citizens, considered as political equals.” Much empirical research over the past half century, most of it focusing on the United States, has examined the relationship between citizens’ policy preferences and the policy choices of elected officials. According to Robert Shapiro, this research has generated “evidence for strong effects of public opinion on government policies,” providing “a sanguine picture of democracy at work.”
In recent years, however, scholars of American politics have produced striking evidence that the apparent “strong effects” of aggregate public opinion in these studies mask severe inequalities in responsiveness. As Martin Gilens put it, “The American government does respond to the public’s preferences, but that responsiveness is strongly tilted toward the most affluent citizens. Indeed, under most circumstances, the preferences of the vast majority of Americans appear to have essentially no impact on which policies the government does or doesn’t adopt.”
One possible interpretation of these findings is that the American political system is anomalous in its apparent disregard for the preferences of middle-class and poor people. In that case, the severe political inequality documented there would presumably be accounted for by distinctive features of the United States, such as its system of private campaign finance, its weak labor unions, or its individualistic political culture. But, what if severe political inequality is endemic in affluent democracies? That would suggest that fiddling with the political institutions of the United States to make them more like Denmark’s (or vice versa) would be unlikely to bring us significantly closer to satisfying Dahl’s standard of democratic equality. We would be forced to conclude either that Dahl’s standard is fundamentally misguided or that none of the political systems commonly identified as democratic comes anywhere close to meriting that designation.
Analyzing policy responsiveness
“I have attempted to test the extent to which policymakers in a variety of affluent democracies respond to the preferences of their citizens considered as political equals.”
To address this question, I have attempted to test the extent to which policymakers in a variety of affluent democracies respond to the preferences of their citizens considered as political equals. My analyses focus on the relationship between public opinion and government spending on social welfare programs, including pensions, health, education, and unemployment benefits. These programs represent a major share of government spending in every affluent democracy and, arguably, an important source of public well-being. Moreover, social spending figures prominently in the comparative literature on the political impact of public opinion in affluent democracies, with major scholarly works suggesting that it is significantly influenced by citizens’ preferences.
My analyses employ data on citizens’ views about social spending and the welfare state from three major cross-national survey projects—the International Social Survey Programme (ISSP), the World Values Survey (WVS), and the European Values Survey (EVS). In combination, these three sources provide relevant opinion data from 160 surveys conducted between 1985 and 2012 in 30 countries, including most of the established democracies of Western Europe and the English-speaking world and some newer democracies in Eastern Europe, Latin America, and Asia. I examine shifts in (real per capita) social spending in the two years following each survey. Does greater public enthusiasm for the welfare state lead to increases in social spending, other things being equal? And, more importantly here, do the views of low-income people have the same apparent influence on policy as the views of affluent people?…(More)”.
Intelligent sharing: unleashing the potential of health and care data in the UK to transform outcomes
Report by Future Care Capital: “….Data is often referred to as the ‘new oil’ – the 21st century raw material which, when hitched to algorithmic refinement, may be mined for insight and value – and ‘data flows’ are said to have exerted a greater impact upon global growth than traditional goods flows in recent years (Manyika et al, 2016). Small wonder, then, that governments around the world are endeavouring to strike a balance between individual privacy rights and protections on the one hand, and organisational permissions to facilitate the creation of social, economic and environmental value from broad-ranging data on the other: ‘data rights’ are now of critical importance courtesy of technological advancements. The tension between the two is particularly evident where health and care data in the UK is concerned. Individuals are broadly content with anonymised data from their medical records being used for public benefit but are, understandably, anxious about the implications of the most intimate aspects of their lives being hacked or, else, shared without their knowledge or consent….
The potential for health and care data to be transformative remains, and there is growing concern that opportunities to improve the use of health and care data in peoples’ interests are being missed….
we recommend additional support for digitisation efforts in social care settings. We call upon the Government to streamline processes associated with Information Governance (IG) modelling to help data sharing initiatives that traverse organisational boundaries. We also advocate for investment and additional legal safeguards to make more anonymised data sets available for research and innovation. Crucially, we recommend expediting the scope for individuals to contribute health and care data to sharing initiatives led by the public sector through promotion, education and pilot activities – so that data is deployed to transform public health and support the ‘pivot to prevention’.
In Chapter Two, we explore the rationale and scope for the UK to build upon emergent practice from around the world and become a global leader in ‘data philanthropy’ – to push at the boundaries of existing plans and programmes, and support the development of and access to unrivalled health and care data sets. We look at member-controlled ‘data cooperatives’ and what we’ve termed ‘data communities’ operated by trusted intermediaries. We also explore ‘data collaboratives’ which involve the private sector engaging in data philanthropy for public benefit. Here, we make recommendations about promoting a culture of data philanthropy through the demonstration of tangible benefits to participants and the wider public, and we call upon Government to assess the appetite and feasibility of establishing the world’s first National Health and Care Data Donor Bank….(More)”