Andrei Sambra and Lalana Kagal for the 2017 ACM on Web Science Conference: “This extended abstract discusses how public services can become more open and engage citizens more actively, by providing the local, public administration with the right tools. It calls for public services to think more creatively about how they can collaborate with the public to make better use of the energy and enthusiasm, as well as missing skills that people have and want to offer. It explores the challenges, both in terms of policy and technology, that public services face in mobilizing resources that are by nature voluntary. We intend to provide the governance tools that enable public services to leverage skills coming from the local community, and improve their autonomy, transparency and analytical tools required for true open governance….(More)”.
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)”.
Are innovation labs delivering on their promise?
Catherine Cheney at DEVEX: “Next month, a first-of-its-kind event will take place in Denmark, and it will draw on traditions and ways of living in one of the happiest countries in the world to unlock new perspectives on achieving the Sustainable Development Goals.
Called UNLEASH, the new initiative will gather 1,000 young people from around the world in the capital city of Copenhagen. Then the participants will be transported to “folk high schools,” which are learning institutions in the countryside aimed at adult education. There, they will break into teams to tackle issues such as urban sustainability or education and ICT. The most promising ideas will have access to resources, including mentoring, angel investors and business plan development. Finally, all UNLEASH participants will be connected through an alumni network of individuals who come together at the annual event that will move country to country until 2030.
UNLEASH is a global innovation lab. It is just one of a growing number of innovation labs, which bring people together to develop and test new methods to address challenges across the global health, international development and humanitarian response sectors. But while the initiative sounds new and exciting, the description reads much like many other initiatives springing up around the SDGs: identifying innovative, scalable, implementable solutions, supporting disruptive ideas, and accelerating development impact.
As the global development sector seeks to take on global problems as complex as those captured by the SDGs, innovation will certainly be necessary. But with the growing number of innovation labs not translating as quickly as expected to real progress on the SDGs, some in the industry are also starting to ask tough questions: How can these initiatives go beyond generating ideas, transition into growing and scaling, then go on to changing entire systems in order to, for example, achieve SDG 1 to end poverty in all its forms by 2030? Experts tell Devex the road to success will not be an easy one, but those who have tested out and improved upon models of innovation in this sector are sharing what is working, what is not, and what needs to change….(More)”.
Civic Tech for Urban Collaborative Governance
Hollie Russon-Gilman in PS: Political Science & Politics: “This article aims to contribute to a burgeoning field of ‘civic technology’ to identify precise pathways through which multi-stakeholder partnerships can foster, embed, and encourage more collaborative governance, outlining a research agenda to guide next steps. Instead of looking at technology as a civic panacea or, at the other extreme, as an irrelevant force, this article takes seriously both the democratic potential and the political constraints of the use of technology for more collaborative governance. The article begins by delineating contours of a civic definition of technology focused on generating public good, provides case study examples of civic tech deployed in America’s cities, raises research questions to inform future multi-stakeholder partnerships, and concludes with implications for the public sector workforce and ecosystem.”…(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)”
Public servants to go on blind coffee dates for innovation
David Donaldson at The Mandarin: “Victorian public servants will have the opportunity to be randomly matched with others for coffee dates, as part of the government’s plan to foster links across silos and bolster innovation.
That is just one of many initiatives planned by Victoria in its new Public Sector Innovation Strategy, released on Tuesday.The plan acknowledges that plenty of innovative thinking is already happening, so the best way to drive further ideas is to connect people better and provide tools and case studies so they can learn from one another.
Six themes repeatedly came up in conversations around innovation in the public service, says the document:
- Leaders who enable and reward — too often new ideas are stifled by leaders who don’t support them;
- Employees who feel confident and supported;
- Learning well — pockets of innovation exist, and stronger efforts to learn and develop from them will help;
- Sharing with each other;
- Partnering with the community and other organisations;
- Delivering value — don’t innovate on random things. Focus on what makes a difference.
“This strategy helps to find, encourage and support change that adds value across the public sector. We need to unlock good intent and talent, share examples and experiences, and learn from each other,” says Chris Eccles, secretary of the Department of Premier and Cabinet.
“At our best, we all contribute our different skills and roles to generate more public value, shaped by the common purpose of creating a better society.”
To kick off progress, the government has outlined a series of actions it will undertake:
- A reverse mentoring plan to help executives learn from more junior staff. Due September 2017.
- Build on a current departmental trial that builds innovation into executive performance development plans. December 2017.
- Establish a high-profile event to recognise and reward practical innovation across government. March 2018.
- A practical innovation bank, to provide a common digital space for cross-government sharing of practical resources (case studies, contacts, templates, guides, lessons learned and so on). December 2017.
- Ideas challenge toolkit to provide guidance on how to run an ideas challenge. December 2017.
- Learning lab trial, which will provide an incubator environment for cross-government use on a project by project basis. March 2018.
- VPS Academy, a new peer to peer learning project, will go through two more pilots to build the case for scaling up. July and December 2017…(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)”