Stefaan Verhulst
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)
New Report by Congressional Research Service: “Promoting democratic institutions, processes, and values has long been a U.S. foreign policy objective, though the priority given to this objective has been inconsistent. World events, competing priorities, and political change within the United States all shape the attention and resources provided to democracy promotion efforts and influence whether such efforts focus on supporting fair elections abroad, strengthening civil society, promoting rule of law and human rights, or other aspects of democracy promotion.
Proponents of democracy promotion often assert that such efforts are essential to global development and U.S. security because stable democracies tend to have better economic growth and stronger protection of human rights, and are less likely to go to war with one another. Critics contend that U.S. relations with foreign countries should focus exclusively on U.S. interests and stability in the world order. U.S. interest in global stability, regardless of the democratic nature of national political systems, could discourage U.S. support for democratic transitions—the implementation of which is uncertain and may lead to more, rather than less, instability.
Funding for democracy promotion assistance is deeply integrated into U.S. foreign policy institutions. More than $2 billion annually has been allocated from foreign assistance funds over the past decade for democracy promotion activities managed by the State Department, the U.S. Agency for International Development, the National Endowment for Democracy, and other entities. Programs promoting good governance (characterized by participation, transparency, accountability, effectiveness, and equity), rule of law, and promotion of human rights have typically received the largest share of this funding in contrast to lower funding to promote electoral processes and political competition. In recent years, increasing restrictions imposed by some foreign governments on civil society organizations have resulted in an increased emphasis in democracy promotion assistance for strengthening civil society.
Despite bipartisan support for the general concept of democracy promotion, policy debates in the 115th Congress continue to question the consistency, effectiveness, and appropriateness of such foreign assistance. With the Trump Administration indicating that democracy and human rights might not be a top foreign policy priority, advocates in Congress may be challenged to find common ground with the Administration on this issue.
As part of its budget and oversight responsibilities, the 115th Congress may consider the impact of the Trump Administration’s requested FY2018 foreign assistance spending cuts on U.S. democracy promotion assistance, review the effectiveness of democracy promotion activities, evaluate the various channels available for democracy promotion, and consider where democracy promotion ranks among a wide range of foreign policy and budget priorities….(More)”.
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)”.
Over the past years, an increasing number of studies have looked at the use of internet and communications technology (ICT) in the fight against corruption. While there is broad agreement that ICT tools can be effective in controlling corruption, the mechanisms by which they are doing this are much less clear. This paper attempts to shine some light on this relationship. It focusses on the role of ICT in empowering citizens and supporting civil society. It argues that enlightened citizens can use internet access and social media to inform themselves on corruption, mobilise support for anti-corruption movements and gather information in order to shine a light on particularistic practices. Defining corruption as a collective action problem, the paper provides quantitative evidence to support its claim that ICT can support collective action of an informed citizenry and thus contribute to the control of corruption….(more)”
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)”.
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)”.
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)”.
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)”.
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.”
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)”