Book edited by Georg Aichholzer, Herbert Kubicek and Lourdes Torres: “There is a widely acknowledged evaluation gap in the field of e-participation practice and research, a lack of systematic evaluation with regard to process organization, outcome and impacts. This book addresses the state of the art of e-participation research and the existing evaluation gap by reviewing various evaluation approaches and providing a multidisciplinary concept for evaluating the output, outcome and impact of citizen participation via the Internet as well as via traditional media. It offers new knowledge based on empirical results of its application (tailored to different forms and levels of e-participation) in an international comparative perspective. The book will advance the academic study and practical application of e-participation through fresh insights, largely drawing on theoretical arguments and empirical research results gained in the European collaborative project “e2democracy”. It applies the same research instruments to a set of similar citizen participation processes in seven local communities in three countries (Austria, Germany and Spain). The generic evaluation framework has been tailored to a tested toolset, and the presentation and discussion of related evaluation results aims at clarifying to what extent these tools can be applied to other consultation and collaboration processes, making the book of interest to policymakers and scholars alike….(More)”
Elements of a New Ethical Framework for Big Data Research
“The Berkman Center is pleased to announce the publication of a new paper from the Privacy Tools for Sharing Research Data project team. In this paper, Effy Vayena, Urs Gasser, Alexandra Wood, and David O’Brien from the Berkman Center, with Micah Altman from MIT Libraries, outline elements of a new ethical framework for big data research.
Emerging large-scale data sources hold tremendous potential for new scientific research into human biology, behaviors, and relationships. At the same time, big data research presents privacy and ethical challenges that the current regulatory framework is ill-suited to address. In light of the immense value of large-scale research data, the central question moving forward is not whether such data should be made available for research, but rather how the benefits can be captured in a way that respects fundamental principles of ethics and privacy.
The authors argue that a framework with the following elements would support big data utilization and help harness the value of big data in a sustainable and trust-building manner:
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Oversight should aim to provide universal coverage of human subjects research, regardless of funding source, across all stages of the information lifecycle.
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New definitions and standards should be developed based on a modern understanding of privacy science and the expectations of research subjects.
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Researchers and review boards should be encouraged to incorporate systematic risk-benefit assessments and new procedural and technological solutions from the wide range of interventions that are available.
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Oversight mechanisms and the safeguards implemented should be tailored to the intended uses, benefits, threats, harms, and vulnerabilities associated with a specific research activity.
Development of a new ethical framework with these elements should be the product of a dynamic multistakeholder process that is designed to capture the latest scientific understanding of privacy, analytical methods, available safeguards, community and social norms, and best practices for research ethics as they evolve over time.
The full paper is available for download through the Washington and Lee Law Review Online as part of a collection of papers featured at the Future of Privacy Forum workshop Beyond IRBs: Designing Ethical Review Processes for Big Data Research held on December 10, 2015, in Washington, DC….(More)”
The Curious Journalist’s Guide to Data
New book by The Tow Center: “This is a book about the principles behind data journalism. Not what visualization software to use and how to scrape a website, but the fundamental ideas that underlie the human use of data. This isn’t “how to use data” but “how data works.”
This gets into some of the mathy parts of statistics, but also the difficulty of taking a census of race and the cognitive psychology of probabilities. It traces where data comes from, what journalists do with it, and where it goes after—and tries to understand the possibilities and limitations. Data journalism is as interdisciplinary as it gets, which can make it difficult to assemble all the pieces you need. This is one attempt. This is a technical book, and uses standard technical language, but all mathematical concepts are explained through pictures and examples rather than formulas.
The life of data has three parts: quantification, analysis, and communication. Quantification is the process that creates data. Analysis involves rearranging the data or combining it with other information to produce new knowledge. And none of this is useful without communicating the result.
Quantification is a problem without a home. Although physicists study measurement extensively, physical theory doesn’t say much about how to quantify things like “educational attainment” or even “unemployment.” There are deep philosophical issues here, but the most useful question to a journalist is simply, how was this data created? Data is useful because it represents the world, but we can only understand data if we correctly understand how it came to be. Representation through data is never perfect: all data has error. Randomly sampled surveys are both a powerful quantification technique and the prototype for all measurement error, so this report explains where the margin of error comes from and what it means – from first principles, using pictures.
All data analysis is really data interpretation, which requires much more than math. Data needs context to mean anything at all: Imagine if someone gave you a spreadsheet with no column names. Each data set could be the source of many different stories, and there is no objective theory that tells us which true stories are the best. But the stories still have to be true, which is where data journalism relies on established statistical principles. The theory of statistics solves several problems: accounting for the possibility that the pattern you see in the data was purely a fluke, reasoning from incomplete and conflicting information, and attempting to isolate causes. Stats has been taught as something mysterious, but it’s not. The analysis chapter centers on a single problem – asking if an earlier bar closing time really did reduce assaults in a downtown neighborhood – and traces through the entire process of analysis by explaining the statistical principles invoked at each step, building up to the state-of-the-art methods of Bayesian inference and causal graphs.
A story isn’t isn’t finished until you’ve communicated your results. Data visualization works because it relies on the biology of human visual perception, just as all data communication relies on human cognitive processing. People tend to overestimate small risks and underestimate large risks; examples leave a much stronger impression than statistics; and data about some will, unconsciously, come to represent all, no matter how well you warn that your sample doesn’t generalize. If you’re not aware of these issues you can leave people with skewed impressions or reinforce harmful stereotypes. The journalist isn’t only responsible for what they put in the story, but what ends up in the mind of the audience.
This report brings together many fields to explore where data comes from, how to analyze it, and how to communicate your results. It uses examples from journalism to explain everything from Bayesian statistics to the neurobiology of data visualization, all in plain language with lots of illustrations. Some of these ideas are thousands of years old, some were developed only a decade ago, and all of them have come together to create the 21st century practice of data journalism….(More)”
The Bottom of the Data Pyramid: Big Data and the Global South
Payal Arora at the International Journal of Communication: “To date, little attention has been given to the impact of big data in the Global South, about 60% of whose residents are below the poverty line. Big data manifests in novel and unprecedented ways in these neglected contexts. For instance, India has created biometric national identities for her 1.2 billion people, linking them to welfare schemes, and social entrepreneurial initiatives like the Ushahidi project that leveraged crowdsourcing to provide real-time crisis maps for humanitarian relief.
While these projects are indeed inspirational, this article argues that in the context of the Global South there is a bias in the framing of big data as an instrument of empowerment. Here, the poor, or the “bottom of the pyramid” populace are the new consumer base, agents of social change instead of passive beneficiaries. This neoliberal outlook of big data facilitating inclusive capitalism for the common good sidelines critical perspectives urgently needed if we are to channel big data as a positive social force in emerging economies. This article proposes to assess these new technological developments through the lens of databased democracies, databased identities, and databased geographies to make evident normative assumptions and perspectives in this under-examined context….(More)”.
When open data is a Trojan Horse: The weaponization of transparency in science and governance
Openness and transparency are becoming hallmarks of responsible data practice in science and governance. Concerns about data falsification, erroneous analysis, and misleading presentation of research results have recently strengthened the call for new procedures that ensure public accountability for data-driven decisions. Though we generally count ourselves in favor of increased transparency in data practice, this Commentary highlights a caveat. We suggest that legislative efforts that invoke the language of data transparency can sometimes function as “Trojan Horses” through which other political goals are pursued. Framing these maneuvers in the language of transparency can be strategic, because approaches that emphasize open access to data carry tremendous appeal, particularly in current political and technological contexts. We illustrate our argument through two examples of pro-transparency policy efforts, one historical and one current: industry-backed “sound science” initiatives in the 1990s, and contemporary legislative efforts to open environmental data to public inspection. Rules that exist mainly to impede science-based policy processes weaponize the concept of data transparency. The discussion illustrates that, much as Big Data itself requires critical assessment, the processes and principles that attend it—like transparency—also carry political valence, and, as such, warrant careful analysis….(More)”
How to train Public Entrepreneurs
10 Lessons : “…The GovLab and its network of 25 world-class coaches and over 100 mentors helped 446 participants in more thana dozen US cities and thirty foreign countries to take a public interest technology project from idea to implementation. In the process, we ‘ve learned a lot about the need for new ways of training the next generation of leaders and problem solvers.
Our aim has been to aid public entrepreneurs — passionate and innovative people who wish to take advantage of new technology to do good in the world. That’s why we measure success, not by the number of participants in a class, but by the project’s participants create and the impact those projects have on communities….
Lesson 1: There is growing, and unmet, demand for training a new kind of public servant: the public entrepreneur…
Lesson 2: Tap the distributed supply of talent and expertise to accelerate learning…
Lesson 3: Create new methods for training public entrepreneurs to solve problems…
Lesson 4: Develop tools to help public interest innovators “cross the chasm” from idea to implementation…
Lesson 5: Teach collaboration and partnering for change…
Lesson 6: In order to be successful, public entrepreneurs must be able to define the problem — a skill widely lacking…
Lesson 7: Connecting innovators and alumni with one another generates a lasting public infrastructure that can help solve problems more effectively…
Lesson 8: Pedagogical priorities include making problem solving more data driven and evidence based….
Lesson 9: The demand and supply are global — which requires a global mindset and platform in order to learn what has worked elsewhere and why…
Lesson 10: Collaboration and coordination among anchor organizations is key to meeting the demand and coordinating the supply….(More)
Mapping a flood of new data
Rebecca Lipman at Economist Intelligence Unit Perspectives on “One city tweets to stay dry: From drones to old-fashioned phone calls, data come from many unlikely sources. In a disaster, such as a flood or earthquake, responders will take whatever information they can get to visualise the crisis and best direct their resources. Increasingly, cities prone to natural disasters are learning to better aid their citizens by empowering their local agencies and responders with sophisticated tools to cut through the large volume and velocity of disaster-related data and synthesise actionable information.
Consider the plight of the metro area of Jakarta, Indonesia, home to some 28m people, 13 rivers and 1,100 km of canals. With 40% of the city below sea level (and sinking), and regularly subject to extreme weather events including torrential downpours in monsoon season, Jakarta’s residents face far-too-frequent, life-threatening floods. Despite the unpredictability of flooding conditions, citizens have long taken a passive approach that depended on government entities to manage the response. But the information Jakarta’s responders had on the flooding conditions was patchy at best. So in the last few years, the government began to turn to the local population for help. It helped.
Today, Jakarta’s municipal government is relying on the web-based PetaJakarta.org project and a handful of other crowdsourcing mobile apps such as Qlue and CROP to collect data and respond to floods and other disasters. Through these programmes, crowdsourced, time-sensitive data derived from citizens’ social-media inputs have made it possible for city agencies to more precisely map the locations of rising floods and help the residents at risk. In January 2015, for example, the web-based Peta Jakarta received 5,209 reports on floods via tweets with detailed text and photos. Anytime there’s a flood, Peta Jakarta’s data from the tweets are mapped and updated every minute, and often cross-checked by Jakarta Disaster Management Agency (BPBD) officials through calls with community leaders to assess the information and guide responders.
But in any city Twitter is only one piece of a very large puzzle. …
Even with such life-and-death examples, government agencies remain deeply protective of data because of issues of security, data ownership and citizen privacy. They are also concerned about liability issues if incorrect data lead to an activity that has unsuccessful outcomes. These concerns encumber the combination of crowdsourced data with operational systems of record, and impede the fast progress needed in disaster situations….Download the case study here.”
Feedback Loop Failure: Implications for the Self-Regulation of the Sharing Economy
Essay by Abbey Stemler: “Ratings and reviews are the lifeblood of the sharing economy. They provide a reputation proxy and make us feel comfortable jumping into stranger’s cars, sleeping in their beds, and having a meal at their kitchen tables. However, as the fields of psychology, management, and behavioral economics are beginning to tell us, these trust building mechanisms might be flawed. Instead of relying on the wisdom of the crowd, we might be relying on the collective bias of the crowd. This essay examines how proposed theories for regulating the sharing economy depend on accurate feedback mechanisms and argues that this reliance should be questioned, because feedback loop failure occurs in the sharing economy and distorts the risk calculation for participants. This failure can lead to uniformed decision-making and consumer fraud….(More)”
The creative citizen unbound
Book by Ian Hargreaves and John Hartley on “How social media and DIY culture contribute to democracy, communities and the creative economy”: “The creative citizen unbound introduces the concept of ‘creative citizenship’ to explore the potential of civic-minded creative individuals in the era of social media and in the context of an expanding creative economy. Drawing on the findings of a 30-month study of communities supported by the UK research funding councils, multidisciplinary contributors examine the value and nature of creative citizenship, not only in terms of its contribution to civic life and social capital but also to more contested notions of value, both economic and cultural. This original book will be beneficial to researchers and students across a range of disciplines including media and communication, political science, economics, planning and economic geography, and the creative and performing arts….(More)”
How to Win a Science Contest
Nathan Collins at Pacific Standard: “…there are contests like the DARPA Robotics Challenge, which gives prizes for solving particularly difficult problems, like how to prevent an autonomous vehicle from crashing.
But who wins such contests, and how? One might think it’s the science insiders, since they have the knowledge and background to solve difficult scientific problems. It’s hard to imagine, for example, a political scientist solving a major problem in theoretical physics. At the same time, insiders can become inflexible, having been so ensconced in a particular way of thinking that they can’t see outside of the box, let alone think outside it.
Unfortunately, most of what we know about insiders, outsiders, and scientific success is anecdotal. (Hedy Lamarr, the late actress and co-inventor of a key wireless technology, is a prominent anecdote, but still just an anecdote.) To remedy that, Oguz Ali Acar and Jan van den Ende decided to conduct a proper study. For data, they looked to InnoCentive, an online platform that “crowdsource[s] innovative solutions from the world’s smartest people, who compete to provide ideas and solutions to important business, social, policy, scientific, and technical challenges,” according to its website.
Acar and van den Ende surveyed 230 InnoCentive contest participants, who reported how much expertise they had related to the last problem they’d solved, along with how much experience they had solving similar problems in the past, regardless of whether it was related to their professional expertise. The researchers also asked how many different scientific fields problem solvers had looked to for ideas, and how much effort they’d put into their solutions. For each of the solvers, the researchers then looked at all the contests that person won and computed their odds of winning—a measure of creativity, they argue, since contests are judged in part on the solutions’ creativity.
That data revealed an intuitive, though not entirely simple pattern. Insiders (think Richard Feynman in physics) were more likely to win a contest when they cast a wide net for ideas, while outsiders (like Lamarr) performed best when they focused on one scientific or technological domain. In other words, outsiders—who may bring a useful new perspective to bear—should bone up on the problem they’re trying to solve, while insiders, who’ve already done their homework, benefit from thinking outside the box.
Still, there’s something both groups can’t do without: hard work. “[I]f insiders … spend significant amounts of time seeking out knowledge from a wide variety of other fields, they are more likely to be creative in that domain,” Acar and van den Ende write, and if outsiders work hard, they “can turn their lack of knowledge in a domain into an advantage.”….(More)”