Helen Margetts in Nature: “Social networks are not a new phenomenon — people have always associated with like-minded others — but the advent of social media has led to a vast increase in the amount of social information that we see. We need data and experiments to understand how this information shapes our political landscape…(More)”
Crowdmapping as a new data source for journalists
Ana Brandusescu and Renée Sieber in Data Driven Journalism: “Crowdsourced data, especially for mapping, is a boon for data driven journalism. In 2015, Nepal’s earthquake was mapped in an astounding 48 hours. The number of volunteers increased to over 2,400 mappers, most of them international, a number that increased exponentially from the initial range of seven to 100 mapping volunteers present before the earthquake occurred.
A significant use of crowdsourced data for mapping, or crowdmapping, is to inform crisis responses like the Nepal earthquake by providing a medium for citizens to communicate with one another and with those seeking to help victims. The benefits to affected peoples are immediate information sharing and visualization of dire and urgent events. These apps have the ability to fill information gaps and even provide aid for disaster victims. Volunteers from across the globe also can contribute to crowdsource entire maps of post-disaster road infrastructures and refugee sites. As a platform and medium, crisis mapping has become so popular that it is increasingly replacing traditional mapping methods for humanitarian emergencies. This is also a huge benefit to journalists as they demonstrate connectivity between open source software, humanitarian crises, and crowdsourcing. According to the Tow Center’s Guide to Crowdsourcing, “Crowdsourcing allows newsrooms to build audience entry points at every stage of the journalistic process—from story assigning, to pre-data collection, to data mining, to sharing specialized expertise, to collecting personal experiences and continuing post-story conversations”….
But let’s get real. Crowdsourced apps have a highly nuanced and complex process with many problems. Here’s five points.
1. Some crises are sexier than others…
2. These apps are far from being zero-cost…
3. Participant engagement is opaque…
4. The problem with “disruption” as a transformative tech…
5. The technical literacy of journalists… (More)
This article is based on the authors’ research article “Confronting the hype: The use of crisis mapping for community development”. Read the full article here.
Welcome to E-Estonia, the tiny nation that’s leading Europe in digital innovation
The Conversation: “Big Brother does “just want to help” – in Estonia, at least. In this small nation of 1.3 million people, citizens have overcome fears of an Orwellian dystopia with ubiquitous surveillance to become a highly digital society.
The government took nearly all its services online in 2003 with the e-Estonia State Portal. The country’s innovative digital governance was not the result of a carefully crafted master plan, it was a pragmatic and cost-efficient response to budget limitations.
It helped that citizens trusted their politicians after Estonia regained independence in 1991. And, in turn, politicians trusted the country’s engineers, who had no commitment to legacy hardware or software systems, to build something new.
This proved to be a winning formula that can now benefit all the European countries.
The once-only principle
With its digital governance, Estonia introduced the “once-only” principle, mandating that the state is not allowed to ask citizens for the same information twice.
In other words, if you give your address or a family member’s name to the census bureau, the health insurance provider will not later ask you for it again. No department of any government agency can make citizens repeat information already stored in their database or that of some other agency….The once-only principle has been such a big success that, based on Estonia’s common-sense innovation, the EU enacted a digital Once Only Principle and Initiative early this year. It ensures that “citizens and businesses supply certain standard information only once, because public administration offices take action to internally share this data, so that no additional burden falls on citizens and businesses.”…
‘Twice-mandatory’ principle
Governments should always be brainstorming, asking themselves, for example, if one government agency needs this information, who else might benefit from it? And beyond need, what insights could we glean from this data?
Financier Vernon Hill introduced an interesting “One to Say YES, Two to Say NO” rule when founding Metro Bank UK: “It takes only one person to make a yes decision, but it requires two people to say no. If you’re going to turn away business, you need a second check for that.”
Imagine how simple and powerful a policy it would be if governments learnt this lesson. What if every bit of information collected from citizens or businesses had to be used for two purposes (at least!) or by two agencies in order to merit requesting it?
The Estonian Tax and Customs Board is, perhaps unexpectedly given the reputation of tax offices, an example of the potential for such a paradigm shift. In 2014, it launched a new strategy to address tax fraud, requiring every business transaction of over €1,000 to be declared monthly by the entities involved.
To minimise the administrative burden of this, the government introduced an application-programming interface that allows information to be automatically exchanged between the company’s accounting software and the state’s tax system.
Though there was some negative push back in the media at the beginning by companies and former president Toomas Hendrik Ilves even vetoed the initial version of the act, the system was a spectacular success. Estonia surpassed its original estimate of €30 million in reduced tax fraud by more than twice.
Latvia, Spain, Belgium, Romania, Hungary and several others have taken a similar path for controlling and detecting tax fraud. But analysing this data beyond fraud is where the real potential is hidden….(More).”
Openness as social praxis
Matthew Longshore Smith and Ruhiya Seward in First Monday: “Since the early 2000s, there has been an explosion in the usage of the term open, arguably stemming from the advent of networked technologies — including the Internet and mobile technologies. ‘Openness’ seems to be everywhere, and takes many forms: from open knowledge, open education, open data and open science, to open Internet, open medical records systems and open innovation. These applications of openness are having a profound, and sometimes transformative, effect on social, political and economic life.
This explosion of the use of the term has led to multiple interpretations, ambiguities, and even misunderstandings, not to mention countless debates and disagreements over precise definitions. The paper “Fifty shades of open” by Pomerantz and Peek (2016) highlighted the increasing ambiguity and even confusion surrounding this term. This article builds on Pomerantz and Peek’s attempt to disambiguate the term by offering an alternative understanding to openness — that of social praxis. More specifically, our framing can be broken down into three social processes: open production, open distribution, and open consumption. Each process shares two traits that make them open: you don’t have to pay (free price), and anyone can participate (non-discrimination) in these processes.
We argue that conceptualizing openness as social praxis offers several benefits. First, it provides a way out of a variety of problems that result from ambiguities and misunderstandings that emerge from the current multitude of uses of openness. Second, it provides a contextually sensitive understanding of openness that allows space for the many different ways openness is experienced — often very different from the way that more formal definitions conceptualize it. Third, it points us towards an approach to developing practice-specific theory that we believe helps us build generalizable knowledge on what works (or not), for whom, and in what contexts….(More)”.
Ten simple rules for responsible big data research
Matthew Zook et al in PLOS Computational Biology: “The use of big data research methods has grown tremendously over the past five years in both academia and industry. As the size and complexity of available datasets has grown, so too have the ethical questions raised by big data research. These questions become increasingly urgent as data and research agendas move well beyond those typical of the computational and natural sciences, to more directly address sensitive aspects of human behavior, interaction, and health. The tools of big data research are increasingly woven into our daily lives, including mining digital medical records for scientific and economic insights, mapping relationships via social media, capturing individuals’ speech and action via sensors, tracking movement across space, shaping police and security policy via “predictive policing,” and much more.
The beneficial possibilities for big data in science and industry are tempered by new challenges facing researchers that often lie outside their training and comfort zone. Social scientists now grapple with data structures and cloud computing, while computer scientists must contend with human subject protocols and institutional review boards (IRBs). While the connection between individual datum and actual human beings can appear quite abstract, the scope, scale, and complexity of many forms of big data creates a rich ecosystem in which human participants and their communities are deeply embedded and susceptible to harm. This complexity challenges any normative set of rules and makes devising universal guidelines difficult.
Nevertheless, the need for direction in responsible big data research is evident, and this article provides a set of “ten simple rules” for addressing the complex ethical issues that will inevitably arise. Modeled on PLOS Computational Biology’s ongoing collection of rules, the recommendations we outline involve more nuance than the words “simple” and “rules” suggest. This nuance is inevitably tied to our paper’s starting premise: all big data research on social, medical, psychological, and economic phenomena engages with human subjects, and researchers have the ethical responsibility to minimize potential harm….
- Acknowledge that data are people and can do harm
- Recognize that privacy is more than a binary value
- Guard against the reidentification of your data
- Practice ethical data sharing
- Consider the strengths and limitations of your data; big does not automatically mean better
- Debate the tough, ethical choices
- Develop a code of conduct for your organization, research community, or industry
- Design your data and systems for auditability
- Engage with the broader consequences of data and analysis practices
- Know when to break these rules…(More)”
What Algorithms Want
Book by Ed Finn: “We depend on—we believe in—algorithms to help us get a ride, choose which book to buy, execute a mathematical proof. It’s as if we think of code as a magic spell, an incantation to reveal what we need to know and even what we want. Humans have always believed that certain invocations—the marriage vow, the shaman’s curse—do not merely describe the world but make it. Computation casts a cultural shadow that is shaped by this long tradition of magical thinking. In this book, Ed Finn considers how the algorithm—in practical terms, “a method for solving a problem”—has its roots not only in mathematical logic but also in cybernetics, philosophy, and magical thinking.
Finn argues that the algorithm deploys concepts from the idealized space of computation in a messy reality, with unpredictable and sometimes fascinating results. Drawing on sources that range from Neal Stephenson’s Snow Crash to Diderot’s Encyclopédie, from Adam Smith to the Star Trek computer, Finn explores the gap between theoretical ideas and pragmatic instructions. He examines the development of intelligent assistants like Siri, the rise of algorithmic aesthetics at Netflix, Ian Bogost’s satiric Facebook game Cow Clicker, and the revolutionary economics of Bitcoin. He describes Google’s goal of anticipating our questions, Uber’s cartoon maps and black box accounting, and what Facebook tells us about programmable value, among other things.
If we want to understand the gap between abstraction and messy reality, Finn argues, we need to build a model of “algorithmic reading” and scholarship that attends to process, spearheading a new experimental humanities….(More)”
For Whose Benefit? The Biological and Cultural Evolution of Human Cooperation
Book by Patrik Lindenfors: “… takes the reader on a journey, navigating the enigmatic aspects of cooperation; a journey that starts inside the body and continues via our thoughts to the human super-organism.
Cooperation is one of life’s fundamental principles. We are all made of parts – genes, cells, organs, neurons, but also of ideas, or ‘memes’. Our societies too are made of parts – us humans. Is all this cooperation fundamentally the same process?
From the smallest component parts of our bodies and minds to our complicated societies, everywhere cooperation is the organizing principle. Often this cooperation has emerged because the constituting parts have benefited from the interactions, but not seldom the cooperating units appear to lose on the interaction. How then to explain cooperation? How can we understand our intricate societies where we regularly provide small and large favors for people we are unrelated to, know, or even never expect to meet again? Where does the idea come from that it is right to risk one’s life for country, religion or freedom? The answers seem to reside in the two processes that have shaped humanity: biological and cultural evolution….(More)”
A Data-driven Approach to Assess the Potential of Smart Cities: The Case of Open Data for Brussels Capital Region
Paper by Miguel Angel Gomez Zotano and Hugues Bersini in Energy Procedia: “The success of smart city projects is intrinsically related to the existence of large volumes of data that could be processed to achieve their objectives. For this purpose, the plethora of data stored by public administrations becomes an incredibly rich source of insight and information due to its volume and diversity. However, it was only with the Open Government Movement when governments have been concerned with the need to open their data to citizens and businesses. Thus, with the emergence of open data portals, these myriad of data enables the development of new business models. The achievement of the benefits sought by making this data available triggers new challenges to cope with the diversity of sources involved. The business potential could be jeopardized by the scarcity of relevant data in the different blocks and domains that makes a city and by the lack of a common approach to data publication, in terms of format, content, etc.
This paper introduces a holistic approach that relies on the Smart City Ontology as the cornerstone to standardise and structure data. This approach, which is proposed to be an analytical tool to assess the potential of data in a given smart city, analyses three main aspects: availability of data, the criteria that data should fulfil to be considered eligible and the model used to structure and organise data. The approach has been applied to the case of Brussels Capital Region, which first results are presented and discussed in this paper. The main conclusion that has been obtained is that, besides its commitment with open data and smart cities, Brussels is not mature enough to fully exploit the real intelligence that smart cities could provide. This maturity would be achieved in the following years with the implementation of the new Brussels’ Smart City Strategy…(More)”.
The Governance Report 2017
Report by The Hertie School of Governance: “Looking at recent developments around the world, it seems that democratic values — from freedom of association and speech to fair and free elections and a system of checks and balances — have come under threat. Experts have, however, disproportionately focused on the problems of democracy in the West, and pointed to familiar sets of shortcomings and emerging deficiencies. By contrast, and with few exceptions, there is less attention to assessing the numerous efforts and innovative activities that are taking place at local, national and international levels. They seek to counteract backsliding and subversion by improving resilience and consolidation and by promoting the expansion of democracy, especially in an era of limited sovereignty and, frequently also, statehood.
The Governance Report 2017 focuses on those policies, programs, and initiatives meant to address the causes of the current democratic malaise, to foster democratic resilience, and to stimulate the (re-)consolidation and development of democratic regimes. The Report’s ambition, reflecting its evidence-based approach, is to shed light on how to manage and care for democracy itself. Specifically, against the backdrop of an assessment of the state of democracy and enriched by cross-national, comparative indicators and case studies, the Report emphasizes solutions geared toward enhancing citizen participation and improving institutions in various contexts, including the rise of neo-populism. Going beyond descriptions of best practices, the Report also examines their origins, identifies the actual and potential trade-offs these solutions entail, and makes concrete recommendations to policymakers….(More)”
Access to New Data Sources for Statistics: Business Models and Incentives for the Corporate Sector
Report by Thilo Klein and Stefaan Verhulst: “New data sources, commonly referred to as “Big Data”, have attracted growing interest from National Statistical Institutes. They have the potential to complement official and more conventional statistics used, for instance, to determine progress towards the Sustainable Development Goals (SDGs) and other targets. However, it is often assumed that this type of data is readily available, which is not necessarily the case. This paper examines legal requirements and business incentives to obtain agreement on private data access, and more generally ways to facilitate the use of Big Data for statistical purposes. Using practical cases, the paper analyses the suitability of five generic data access models for different data sources and data uses in an emerging new data ecosystem. Concrete recommendations for policy action are presented in the conclusions….(More)”.