Five hacks for digital democracy

Curated on Posted on by Stefaan Verhulst

Beth Simone Noveck in Nature: “…Technology is already changing the way public institutions make decisions. Governments at every level are using ‘big data’ to pinpoint or predict the incidence of crime, heart attack and foodborne illness. Expert networking platforms — online directories of people and their skills, such as NovaGob.org in Spain — are helping to match civil servants who have the relevant expertise with those who need the know-how.

To get beyond conventional democratic models of representation or referendum, and, above all, to improve learning in the civil service, we must build on these pockets of promise and evolve. That requires knowledge of what works and when. But there is a dearth of research on the impact of technology on public institutions. One reason is a lack of suitable research methods. Many academics prefer virtual labs with simulated conditions that are far from realistic. Field experiments have long been used to evaluate the choice between two policies. But much less attention is paid to how public organizations might operate differently with new technologies.

The perfect must not be the enemy of the good. Even when it is impractical to create a control group and run parallel interventions in the same institution, comparisons can yield insights. For instance, one could compare the effect of using citizen commenting on legislative proposals in the Brazilian parliament with similar practices in the Finnish parliament.

Of course, some leaders have little interest in advancing more than their own power. But many more politicians and public servants are keen to use research-based evidence to guide how they use technology to govern in the public interest.

The MacArthur Foundation in Chicago, Illinois, has started funding a research network — a dozen academics and public servants — to study the possibilities of using new technology to govern more transparently and in partnership with citizens (see www.opening-governance.org). More collaboration among universities and across disciplines is needed. New research platforms — such as the Open Governance Research Exchange, developed by the Governance Lab, the UK-based non-profit mySociety and the World Bank — can offer pathways for sharing research findings and co-creating methodologies….(More)”

Big Data for Achievement of the 2030 Agenda: Data Privacy, Ethics and Protection

Curated on Posted on by Stefaan Verhulst

UNDG Guidance Note: “This document sets out general guidance on data privacy, data protection and data ethics for the United Nations Development Group (UNDG) concerning the use of big data, collected in real time by private sector entities as part of their business offerings1 , and shared with UNDG members for the purposes of strengthening operational implementation of their programmes to support the achievement of the 2030 Agenda.

The Guidance Note is designed to:

• Establish common principles across UNDG to support the operational use of big data for achievement of the Sustainable Development Goals (SDGs);

• Serve as a risk-management tool taking into account fundamental human rights; and

• Set principles for obtaining, retention, use and quality control for data from the private sector. The data revolution was recognized as an enabler of the Sustainable Development Goals, not only to monitor progress but also to inclusively engage stakeholders at all levels to advance evidence-based policies and programmes and to reach the most vulnerable.

The 2030 Agenda asserts that “Quality, accessible, timely and reliable disaggregates data will be needed to help with the measurement of progress (SGDs) and to ensure that no one is left behind. Such data is key to decision making.” At the same time, there are legitimate concerns regarding risks associated with handling and processing of big data, particularly in light of the current fragmented regulatory landscape and in the absence of a common set of principles on data privacy, ethics and protection. These concerns continue to complicate efforts to develop standardized and scalable approaches to risk management and data access. A coordinated approach is required to ensure the emergence of frameworks for safe and responsible use of big data for the achievement of the 2030 Agenda.

The guidance described in this document acknowledges and is based on the UN Guidelines for the Regulation of Computerized Personal Data Files, adopted by the UN General Assembly resolution 45/95, and takes into account both existing international instruments and relevant regulations, rules and policies of UNDG member organizations concerning data privacy and data protection. This Guidance Note is based on standards that have withstood the test of time, reflecting the strength of their core values….(More)”.

Societal impacts of big data: challenges and opportunities in Europe

Curated on Posted on by Stefaan Verhulst

Martí Cuquet, Guillermo Vega-Gorgojo, Hans Lammerant, Rachel Finn, Umair ul Hassan at ArXiv: “This paper presents the risks and opportunities of big data and the potential social benefits it can bring. The research is based on an analysis of the societal impacts observed in a set of six case studies across different European sectors. These impacts are divided into economic, social and ethical, legal and political impacts, and affect areas such as improved efficiency, innovation and decision making, changing business models, dependency on public funding, participation, equality, discrimination and trust, data protection and intellectual property rights, private and public tensions and losing control to actors abroad. A special focus is given to the risks and opportunities coming from the legal framework and how to counter the negative impacts of big data. Recommendations are presented for four specific legal frameworks: copyright and database protection, protection of trade secrets, privacy and data protection and anti-discrimination. In addition, the potential social benefits of big data are exemplified in six domains: improved decision making and event detection; data-driven innovations and new business models; direct social, environmental and other citizen benefits; citizen participation, transparency and public trust; privacy-aware data practices; and big data for identifying discrimination. Several best practices are suggested to capture these benefits…(More)”.

Realising the Data Revolution for Sustainable Development: Towards Capacity Development 4.0

Curated on Posted on by Stefaan Verhulst

Report by Niels Keijzer and Stephan Klingebiel for Paris21: “An ever-deepening data revolution is shaping everyday lives in many parts of the world. As just one of many mindboggling statistics on Big Data, it has been estimated that by the year 2020, about 1.7 megabytes of new information will be created every second for every human being on the planet. The benefits of the data revolution extend to different groups of people, social movements, institutions and businesses. Yet many people and countries do not have access to these positive benefits and in richer countries potentially positive changes raise suspicion amongst citizens as well as concerns related to privacy and confidentiality. The availability of potential advantages is, to a large extent, guided by levels of development and income. Despite the rapid spread of mobile phone technology that allows regions otherwise disconnected from the grid to ‘leapfrog’ in terms of using and producing data and statistics, poor people are still less likely to benefit from the dramatic changes in the field of data.

Against the background of the 2030 Agenda for Sustainable Development and its Sustainable Development Goals (SDGs), the main challenge for statistics is to manage the data revolution in support of sustainable development. The main priorities are the broadening and deepening of production, dissemination and use of data and statistics, and achieving them requires identifying those population groups which are most vulnerable and making governments more accountable to their citizens. In parallel, the risks accompanying the data revolution need to be mitigated and reduced, including the use of data for purposes of repression or otherwise infringing on the privacy of citizens. In addition to representing a universal agenda that breaks away from the dichotomy of developed and developing countries, the new agenda calls for tailor-made approaches in each country or region concerned, supported by global actions. The 2030 Agenda further states the international community’s realisation of the need to move away from ‘business as usual’ in international support for data and statistics.

The most important driving forces shaping the data revolution are domestic (legal) frameworks and public policies across the globe. This applies not only to wealthier countries but also developing countries2 , and external support cannot compensate for absent domestic leadership and investment. Technical, legal and political factors all affect whether countries are willing and able to succeed in benefiting from the data revolution. However, in both low income countries and lower-middle income countries, and to some extent in upper-middle income countries, we can observe two constraining factors in this regard, capacities and funding. These factors are, to some degree, interrelated: if funding is not sufficiently available it might be difficult to increase the capacities required, and if capacities are insufficient funding issues might be more challenging….(More)”

Ten simple rules for responsible big data research

Curated on Posted on by Stefaan Verhulst

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….

  1. Acknowledge that data are people and can do harm
  2. Recognize that privacy is more than a binary value
  3. Guard against the reidentification of your data
  4. Practice ethical data sharing
  5. Consider the strengths and limitations of your data; big does not automatically mean better
  6. Debate the tough, ethical choices
  7. Develop a code of conduct for your organization, research community, or industry
  8. Design your data and systems for auditability
  9. Engage with the broader consequences of data and analysis practices
  10. Know when to break these rules…(More)”

Access to New Data Sources for Statistics: Business Models and Incentives for the Corporate Sector

Curated on Posted on by Stefaan Verhulst

Screen Shot 2017-03-28 at 11.45.07 AMReport 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)”.

Technology and the Voluntary Sector: Don’t (always) Believe the Hype

Curated on Posted on by Stefaan Verhulst

Gareth Lloyd at the NCVO: “One of the most important questions for voluntary sector organisations of all sizes is how their work can be supported by technology. We have talked before about how the sector needs to identify technology that is replicable and has low barriers to uptake, but we have also recently carried out a research project with Tata Consultacy Services on this issue, which involved an evidence review, mapping exercise and workshop with voluntary sector experts.

Here’s a brief overview of what we learned, including the different challenges for large and small organisations; as well as those that apply to everyone.

Grand ambitions

First, our work looked at the attraction – and possible dangers of – investing in new and largely unproven technologies. We have seen the voluntary sector undergo fleeting love affairs with new and exciting types of technology, such as big data, crowdfunding and bitcoin; and we go through periods of hearing about technologies that have the potential to change the way that the sector works…..

Defining problems and choosing solutions

For all the challenges mentioned so far, the underlying issue is the same: a mismatch between the problem to be solved and the solution implemented. The answer is to focus on the problem that you’re trying to solve, whether approaching it as a technology issue or not, and then look at the ways that technology can help you. For example, Jointly – the app developed by Carers UK to enable conversation between groups of carers – stands out as a problem that could have been addressed without use of technology, but was eventually enhanced by it.

But organisations also have to ensure that the technology used to solve those problems is cost effective, time effective, and appropriate for them in terms of where they are starting from. If the solution you choose is tying you up in knots, maybe it isn’t a solution at all.

Our research came up with some high level principles that organisations can use to avoid these problems, and try to ensure that adopting technology transforms the day-to-day activities of organisations while minimising disruption…

Think iterations, rather than discrete projects

Participants at our workshop talked about how the discrete project model doesn’t quite work when trying to embed technology at an organisation. That is, rather than these projects having straightforward planning and implementation phases, they need to be introduced iteratively, as an ongoing process of deployment, evaluation and redesign. Introducing technology in this way minimises risk, helps to ensure that the solution fits the problem, and ensures that it is tailored to the needs of the people who will use it on a day to day basis.

If you are interested in this research you can read the executive summary here, the full slide deck here, or find details of the Spark Salon event where it was launched here….(More)”

Big Data and the Well-Being of Women and Girls: Applications on the Social Scientific Frontier

Curated on Posted on by Stefaan Verhulst

Report by Bapu Vaitla et al for Data2X: “Conventional forms of data—household surveys, national economic accounts, institutional records, and so on—struggle to capture detailed information on the lives of women and girls. The many forms of big data, from geospatial information to digital transaction logs to records of internet activity, can help close the global gender data gap. This report profiles several big data projects that quantify the economic, social, and health status of women and girls…

This report illustrates the potential of big data in filling the global gender data gap. The rise of big data, however, does not mean that traditional sources of data will become less important. On the contrary, the successful implementation of big data approaches requires investment in proven methods of social scientific research, especially for validation and bias correction of big datasets. More broadly, the invisibility of women and girls in national and international data systems is a political, not solely a technical, problem. In the best case, the current “data revolution” will be reimagined as a step towards better “data governance”: a process through which novel types of information catalyze the creation of new partnerships to advocate for scientific, policy, and political reforms that include women and girls in all spheres of social and economic life….(More)”.

Software used to predict crime can now be scoured for bias

Curated on Posted on by Stefaan Verhulst

Dave Gershgorn in Quartz: “Predictive policing, or the idea that software can foresee where crime will take place, is being adopted across the country—despite being riddled with issues. These algorithms have been shown to disproportionately target minorities, and private companies won’t reveal how their software reached those conclusions.

In an attempt to stand out from the pack, predictive-policing startup CivicScape has released its algorithm and data online for experts to scour, according to Government Technology magazine. The company’s Github page is already populated with its code, as well as a variety of documents detailing how its algorithm interprets police data and what variables are included when predicting crime.

“By making our code and data open-source, we are inviting feedback and conversation about CivicScape in the belief that many eyes make our tools better for all,” the company writes on Github. “We must understand and measure bias in crime data that can result in disparate public safety outcomes within a community.”…

CivicScape claims to not use race or ethnic data to make predictions, although it is aware of other indirect indicators of race that could bias its software. The software also filters out low-level drug crimes, which have been found to be heavily biased against African Americans.

While this startup might be the first to publicly reveal the inner machinations of its algorithm and data practices, it’s not an assurance that predictive policing can be made fair and transparent across the board.

“Lots of research is going on about how algorithms can be transparent, accountable, and fair,” the company writes. “We look forward to being involved in this important conversation.”…(More)”.

Seeing Theory

Curated on Posted on by Stefaan Verhulst

“Seeing Theory is a project designed and created by Daniel Kunin with support from Brown University’s Royce Fellowship Program. The goal of the project is to make statistics more accessible to a wider range of students through interactive visualizations.

Statistics is quickly becoming the most important and multi-disciplinary field of mathematics. According to the American Statistical Association, “statistician” is one of the top ten fastest-growing occupations and statistics is one of the fastest-growing bachelor degrees. Statistical literacy is essential to our data driven society. Yet, for all the increased importance and demand for statistical competence, the pedagogical approaches in statistics have barely changed. Using Mike Bostock’s data visualization software, D3.js, Seeing Theory visualizes the fundamental concepts covered in an introductory college statistics or Advanced Placement statistics class. Students are encouraged to use Seeing Theory as an additional resource to their textbook, professor and peers….(More)”