Politicizing Digital Space: Theory, the Internet, and Renewing Democracy

Curated on Posted on by Stefaan Verhulst

Book by Trevor Garrison Smith: “The objective of this book is to outline how a radically democratic politics can be reinvigorated in theory and practice through the use of the internet. The author argues that politics in its proper sense can be distinguished from anti-politics by analyzing the configuration of public space, subjectivity, participation, and conflict. Each of these terrains can be configured in a more or less political manner, though the contemporary status quo heavily skews them towards anti-political configuration.

Using this understanding of what exactly politics entails, this book considers how the internet can both help and hinder efforts to move each area in a more political direction. By explicitly interpreting contemporary theories of the political in terms of the internet, this analysis avoids the twin traps of both technological determinism and technological cynicism.

Raising awareness of what the word ‘politics’ means, the author develops theoretical work by Arendt, Rancière, Žižek and Mouffe to present a clear and coherent view of how in theory, politics can be digitized and alternatively how the internet can be deployed in the service of trulydemocratic politics…(More)”.

Uber Releases Open Source Project for Differential Privacy

Curated on Posted on by Stefaan Verhulst

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)”.

Political Inequality in Affluent Democracies

Curated on Posted on by Stefaan Verhulst

 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)”.

Open Data Blueprint

Curated on Posted on by Stefaan Verhulst

ODX Canada: “In Canada, the open data environment should be viewed as a supply chain. The movement of open data from producers to consumers involves many different organizations, people, activities, projects and initiatives, all of which work together to push out a final product. Naturally, if there is a break or hurdle in this supply chain, it doesn’t work efficiently. A fundamental hurdle highlighted by companies across the country was the inability to scale their business at the provincial, national and international levels.

This blueprint aims to address the challenges Canadian entrepreneurs are facing by encouraging municipalities to launch open data initiatives. By sharing best practices, we hope to encourage the accessibility of datasets within existing jurisdictions. The structured recommendations in this Open Data Blueprint are based on feedback and best practices seen in major cities across Canada collected through ODX’s primary research….(More)”

(Read more about the OD150 initiative here)

Principles and Practices for a Federal Statistical Agency

Curated on Posted on by Stefaan Verhulst

National Academies of Sciences Report: “Publicly available statistics from government agencies that are credible, relevant, accurate, and timely are essential for policy makers, individuals, households, businesses, academic institutions, and other organizations to make informed decisions. Even more, the effective operation of a democratic system of government depends on the unhindered flow of statistical information to its citizens.

In the United States, federal statistical agencies in cabinet departments and independent agencies are the governmental units whose principal function is to compile, analyze, and disseminate information for such statistical purposes as describing population characteristics and trends, planning and monitoring programs, and conducting research and evaluation. The work of these agencies is coordinated by the U.S. Office of Management and Budget. Statistical agencies may acquire information not only from surveys or censuses of people and organizations, but also from such sources as government administrative records, private-sector datasets, and Internet sources that are judged of suitable quality and relevance for statistical use. They may conduct analyses, but they do not advocate policies or take partisan positions. Statistical purposes for which they provide information relate to descriptions of groups and exclude any interest in or identification of an individual person, institution, or economic unit.

Four principles are fundamental for a federal statistical agency: relevance to policy issues, credibility among data users, trust among data providers, and independence from political and other undue external influence.� Principles and Practices for a Federal Statistical Agency: Sixth Edition presents and comments on these principles as they’ve been impacted by changes in laws, regulations, and other aspects of the environment of federal statistical agencies over the past 4 years….(More)”.

Lessons from Airbnb and Uber to Open Government as a Platform

Curated on Posted on by Stefaan Verhulst

Interview by Marquis Cabrera with Sangeet Paul Choudary: “…Platform companies have a very strong core built around data, machine learning, and a central infrastructure. But they rapidly innovate around it to try and test new things in the market and that helps them open themselves for further innovation in the ecosystem. Governments can learn to become more modular and more agile, the way platform companies are. Modularity in architecture is a very fundamental part of being a platform company; both in terms of your organizational architecture, as well as your business model architecture.

The second thing that governments can learn from a platform company is that successful platform companies are created with intent. They are not created by just opening out what you have available. If you look at the current approach of applying platform thinking in government, a common approach is just to take data and open it out to the world. However, successful platform companies first create a shaping strategy to shape-out and craft a direction of vision for the ecosystem in terms of what they can achieve by being on the platform. They then provision the right tools and services that serve the vision to enable success for the ecosystem[1] . And only then do they open up their infrastructure. It’s really important that you craft the right shaping strategy and use that to define the rights tools and services before you start pursuing a platform implementation.

In my work with governments, I regularly find myself stressing the importance of thinking as a market maker rather than as a service provider. Governments have always been market makers but when it comes to technology, they often take the service provider approach.

In your book, you used San Francisco City Government and Data.gov as examples of infusing platform thinking in government. But what are some global examples of governments, countries infusing platform thinking around the world?

One of the best examples is from my home country Singapore, which has been at the forefront of converting the nation into a platform. It has now been pursuing platform strategy both overall as a nation by building a smart nation platform, and also within verticals. If you look particularly at mobility and transportation, it has worked to create a central core platform and then build greater autonomy around how mobility and transportation works in the country. Other good examples of governments applying this are Dubai, South Korea, Barcelona; they are all countries and cities that have applied the concept of platforms very well to create a smart nation platform. India is another example that is applying platform thinking with the creation of the India stack, though the implementation could benefit from better platform governance structures and a more open regulation around participation….(More)”.

The Prospects & Limits of Deliberative Democracy

Curated on Posted on by Stefaan Verhulst

Introduction by James S. Fishkin and Jane Mansbridge of Special Issue of Daedalus: “Democracy is under siege. Approval ratings for democratic institutions in most countries around the world are at near-record lows. The number of recognized democratic countries in the world is no longer expanding after the so-called Third Wave of democratic transitions. Indeed, there is something of a “democratic recession.” Further, some apparently democratic countries with competitive elections are undermining elements of liberal democracy: the rights and liberties that ensure freedom of thought and expression, protection of the rule of law, and all the protections for the substructure of civil society that may be as important for making democracy work as the electoral process itself. The model of party competition-based democracy – the principal model of democracy in the modern era – seems under threat.

That model also has competition. What might be called “meritocratic authoritarianism,” a model in which regimes with flawed democratic processes nevertheless provide good governance, is attracting attention and some support. Singapore is the only successful extant example, although some suggest China as another nation moving in this direction. Singapore is not a Western-style party- and competition-based democracy, but it is well-known for its competent civil servants schooled in making decisions on a cost-benefit basis to solve public problems, with the goals set by elite consultation with input from elections rather than by party competition.

Public discontent makes further difficulties for the competitive model. Democracies around the world struggle with the apparent gulf between political elites who are widely distrusted and mobilized citizens who fuel populism with the energy of angry voices. Disillusioned citizens turning against elites have produced unexpected election results, including the Brexit decision and the 2016 U.S. presidential election.

The competitive elections and referenda of most current democracies depend on mobilizing millions of voters within a context of advertising, social media, and efforts to manipulate as well as inform public opinion. Competing teams want to win and, in most cases, are interested in informing voters only when it is to their advantage. The rationale for competitive democracy, most influentially developed by the late economist Joseph Schumpeter, held that the same techniques of advertising used in the commercial sphere to get people to buy products can be expected in the political sphere. On this view, we should not expect a “genuine” public will, but rather “a manufactured will” that is just a by-product of political competition.

Yet the ideal of democracy as the rule of “the people” is deeply undermined when the will of the people is in large part manufactured. The legitimacy of democracy depends on some real link between the public will and the public policies and office-holders who are selected. Although some have criticized this “folk theory of democracy” as empirically naive, its very status as a folk theory reflects how widespread this normative expectation is.5 To the extent that leaders manufacture the public will, the normative causal arrow goes in the wrong direction. If current democracies cannot produce meaningful processes of public will formation, the legitimacy claims of meritocratic autocracies or even more fully autocratic systems become comparatively stronger.

Over the last two decades, another approach to democracy has become increasingly prominent. Based on greater deliberation among the public and its representatives, deliberative democracy has the potential, at least in theory, to respond to today’s current challenges. If the many versions of a more deliberative democracy live up to their aspirations, they could help revive democratic legitimacy, provide for more authentic public will formation, provide a middle ground between widely mistrusted elites and the angry voices of populism, and help fulfill some of our common normative expectations about democracy.

Can this potential be realized? In what ways and to what extent? Deliberative democracy has created a rich literature in both theory and practice. This issue of Dædalus assesses both its prospects and limits. We include advocates as well as critics. As deliberative democrats, our aim is to stimulate public deliberation about deliberative democracy, weighing arguments for and against its application in different contexts and for different purposes.

How can deliberative democracy, if it were to work as envisaged by its supporters, respond to the challenges just sketched? First, if the more-deliberative institutions that many advocate can be applied to real decisions in actual ongoing democracies, arguably they could have a positive effect on legitimacy and lead to better governance. They could make a better connection between the public’s real concerns and how they are governed. Second, these institutions could help fill the gap between distrusted elites and angry populists. Elites are distrusted in part because they seem and often are unresponsive to the public’s concerns, hopes, and values. Perhaps, the suspicion arises, the elites are really out for themselves. On the other hand, populism stirs up angry, mostly nondeliberative voices that can be mobilized in plebescitary campaigns, whether for Brexit or for elected office. In their contributions to this issue, both Claus Offe and Hélène Landemore explore the crisis of legitimacy in representative government, including the clash between status quo – oriented elites and populism. Deliberative democratic methods open up the prospect of prescriptions that are both representative of the entire population and based on sober, evidence-based analysis of the merits of competing arguments. Popular deliberative institutions are grounded in the public’s values and concerns, so the voice they magnify is not the voice of the elites. But that voice is usually also, after deliberation, more evidence-based and reflective of the merits of the major policy arguments. Hence these institutions fill an important gap.

How might popular deliberative democracy, if it were to work as envisaged by its supporters, fulfill normative expectations of democracy, thought to be unrealistic by critics of the “folk theory”? The issue turns on the empirical possibility that the public can actually deliberate. Can the people weigh the trade-offs? Can they assess competing arguments? Can they connect their deliberations with their voting preferences or other expressions of preference about what should be done? Is the problem that the people are not competent, or that they are not in the right institutional context to be effectively motivated to participate? These are empirical questions, and the controversies about them are part of our dialogue.

This issue includes varying definitions, approaches, and contexts. The root notion is that deliberation requires “weighing” competing arguments for policies or candidates in a context of mutually civil and diverse discussion in which people can decide on the merits of arguments with good information. Is such a thing possible in an era of fake news, social media, and public discussions largely among the like-minded? These are some of the challenges facing those who might try to make deliberative democracy practical….(More)”

Local Government in China Trials Blockchain for Public Services

Curated on Posted on by Stefaan Verhulst

Wolfie Zhao at Coin Desk: “A city district in southern China is using blockchain to streamline government services for its one million residents.

Chan Cheng District, within Foshan City in Canton province, announced during an event on 23rd June the launch of a platform called Intelligent Multifunctional Identity (IMI) that lets registered local residents avoid filling repetitive personal information for different public services, presumably providing a more simple and secured process.

The newly revealed system is seen as an upgrade, incorporated to the current all-in-one workflow in the local administration.

Since 2014, the Chan Cheng District government has operated a central hub inside the city that serves as a physical portal for residents who need tax, pension, healthcare or utility services, among others. Despite offering a single source at which residents can access these services, repetitive work is needed for multiple processes.

According to the district’s announcement, residents who are able to register on and verified by the IMI platform will have the control of their personal information and can grant access to a government service they need. Using paired public and private keys, the system is also said to be able to verify users’ identity automatically without requiring them to be physically present at a service center….(More)”.

What is One Team Government?

Curated on Posted on by Stefaan Verhulst

Kit Collingwood-Richardso at Medium: “On 29th June, 186 people came together in London to talk about how we could work across disciplines to make government more effective…. Below are our current ideas on what we want it to be. We’d love your help shaping them up.

So what is One Team Government?

At its heart, it’s a community (join it here and see the bottom of this post), united and guided by a set of principles. Together, we are working to create a movement of reform through practical action.

The community is made up of people who are passionate about public sector reform (we deliberately want this to be wider than just government), with the emphasis on improving the services we offer to citizens and how we work. We believe the public sector can be brilliant, and we’re committed to making it so.

You don’t have to work for government to be in the community, nor be a public servant in the wider sense, nor indeed be in the UK; we need diverse perspectives, with people of all sectors, areas and interests helping. We think we’re unstoppable if we work together.

Our initial thinking (see below for how to help us iterate on this) is that we want the One Team Government movement to be guided by seven principles:

1. Work in the open and positively

We’re a community; everything we do will be documented and made to share. Where conversations happen that can’t be shared, the wider learning still will be. This is a reform cooperative, where we choose to be generous with knowledge. Ideas are infectious; we’ll share ours early and often….

2. Take practical action

Although talking is vital, we will be defined more by the things we do than the things we say. We will create change by taking small, measured steps every day — everything from creating a new contact in a different area or discipline, sharing something we’ve written, or giving our time to contribute to others’ work — and encouraging others to do the same. We won’t create huge plans, but do things that make a real difference today, no matter how big or small. We will document what they are.

3. Experiment and iterate

We don’t think there’s one way to ‘do’ reform. We will experiment with design, and put user-focused service design thinking into everything we do, learning from and with each other. We will test, iterate and reflect. We will be humble in our approach, focusing on asking the right questions to get to the best answers.

We will embrace small failures as opportunities to learn. We won’t get everything right, and we won’t try to. We will listen, learn and improve together.

4. Be diverse and inclusive

Our approach to inclusiveness and diversity is driven by a simple desire to better represent the citizens we serve. We’ll put effort into making that so, by balancing our events, making sure our teams are reflective of society at large and by making sure we have a range of citizen and team voices in the room with us….

5. Care deeply about citizens

We work for users and other citizens affected by our work; everything we do will be guided by our impact on them. We will talk to them, early and often; we will use the best research methods to understand them better. We will be distinguished by our empathy — for users and for each other. The policy that we develop will be tested with real people as early as possible, and refined with their needs in mind.

6. Work across borders

We believe that diverse views make our outcomes and services better. We will be characterised by our work to break down boundaries between groups. …

7. Embrace technology

We are passionate about public sector reform for the internet age. We will be a technology-enabled community, using online tools to collaborate, network and share. We will put the best of digital thinking into policy and service design, using technology to make us quicker, smarter, better and more data-driven. We will help to shape a public sector we can be proud to work in in the 21st century….(More)”.

Data for Development: The Case for Information, Not Just Data

Curated on Posted on by Stefaan Verhulst

Daniela Ligiero at the Council on Foreign Relations: “When it comes to development, more data is often better—but in the quest for more data, we can often forget about ensuring we have information, which is even more valuable. Information is data that have been recorded, classified, organized, analyzed, interpreted, and translated within a framework so that meaning emerges. At the end of the day, information is what guides action and change.

The need for more data

In 2015, world leaders came together to adopt a new global agenda to guide efforts over the next fifteen years, the Sustainable Development Goals. The High-level Political Forum (HLPF), to be held this year at the United Nations on July 10-19, is an opportunity for review of the 2030 Agenda, and will include an in-depth analysis of seven of the seventeen goals—including those focused on poverty, health, and gender equality. As part of the HLPF, member states are encouraged to undergo voluntary national reviews of progress across goals to facilitate the sharing of experiences, including successes, challenges, and lessons learned; to strengthen policies and institutions; and to mobilize multi-stakeholder support and partnerships for the implementation of the agenda.

A significant challenge that countries continue to face in this process, and one that becomes painfully evident during the HLPF, is the lack of data to establish baselines and track progress. Fortunately, new initiatives aligned with the 2030 Agenda are working to focus on data, such as the Global Partnership for Sustainable Development Data. There are also initiatives focus on collecting more and better data in particular areas, like gender data (e.g., Data2X; UN Women’s Making Every Girl and Woman Count). This work is important and urgently needed.

Data to monitor global progress on the goals is critical to keeping countries accountable to their commitments and allows countries to examine how they are doing across multiple, ambitious goals. However, equally important is the rich, granular national and sub-national level data that can guide the development and implementation of evidence-based, effective programs and policies. These kinds of data are also often lacking or of poor quality, in which case more data and better data is essential. But a frequently-ignored piece of the puzzle at the national level is improved use of the data we already have.

Making the most of the data we have

To illustrate this point, consider the Together for Girls partnership, which was built on obtaining new data where it was lacking and effectively translating it into information to change policies and programs. We are a partnership between national governments, UN agencies and private sector organizations working to break cycles of violence, with special attention to sexual violence against girls. …The first pillar of our work is focused on understanding violence against children within a country, always at the request of the national government. We do this through a national household survey – the Violence Against Children Survey (VACS), led by national governments, CDC, and UNICEF as part of the Together for Girls Partnership….

The truth is there is a plethora of data at the country level, generated by surveys, special studies, administrative systems, private sector, and citizens that can provide meaningful insights across all the development goals.

Connecting the dots

But data—like our programs’—often remain in silos. For example, data focused on violence against children is typically not top of mind for those working on women’s empowerment or adolescent health. Yet, as an example, the VACS can offer valuable information about how sexual violence against girls, as young as 13,is connected to adolescent pregnancy—or how one of the most common perpetrators of sexual violence against girls is a partner, a pattern that starts early and is a predictor for victimization and perpetration later in life.  However, these data are not consistently used across actors working on programs related to adolescent pregnancy and violence against women….(More)”.