Openness as social praxis

Curated on Posted on by Stefaan Verhulst

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

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)”

What Algorithms Want

Curated on Posted on by Stefaan Verhulst

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

Curated on Posted on by Stefaan Verhulst

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

Curated on Posted on by Stefaan Verhulst

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

Curated on Posted on by Stefaan Verhulst

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

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)”.

Will Computer Science become a Social Science?

Curated on Posted on by Stefaan Verhulst

Paper by Ingo Scholtes, Markus Strohmaier and Frank Schweitzer: “When Tay – a Twitter chatbot developed by Microsoft – was activated this March, the company was taken by surprise by what Tay had become. Within less than 24 hours of conversation with Twitter users Tay had learned to make racist, anti-semitic and misogynistic statements that have raised eyebrows in the Twitter community and beyond. What had happened? While Microsoft certainly tested the chat bot before release, planning for the reactions and the social environment in which it was deployed proved tremendously difficult. Yet, the Tay Twitter chatbot incident is just one example for the many challenges which arise when embedding algorithms and computing systems into an ever increasing spectrum of social systems. In this viewpoint we argue that, due to the resulting feedback loops by which computing technologies impact social behavior and social behavior feeds back on (learning) computing systems, we face the risk of losing control over the systems that we engineer. The result are unintended consequences that affect both the technical and social dimension of computing systems, and which computer science is currently not well-prepared to address. Highlighting exemplary challenges in core areas like (1) algorithm design, (2) cyber-physical systems, and (3) software engineering, we argue that social aspects must be turned into first-class citizens of our system models. We further highlight that the social sciences, in particular the interdisciplinary field of Computational Social Science [1], provide us with means to quantitatively analyze, model and predict human behavior. As such, a closer integration between computer science and social sciences not only provides social scientists with new ways to understand social phenomena. It also helps us to regain control over the systems that we engineer….(More)”

Confused by data visualisation? Here’s how to cope in a world of many features

Curated on Posted on by Stefaan Verhulst

 in The Conversation: “The late data visionary Hans Rosling mesmerised the world with his work, contributing to a more informed society. Rosling used global health data to paint a stunning picture of how our world is a better place now than it was in the past, bringing hope through data.

Now more than ever, data are collected from every aspect of our lives. From social media and advertising to artificial intelligence and automated systems, understanding and parsing information have become highly valuable skills. But we often overlook the importance of knowing how to communicate data to peers and to the public in an effective, meaningful way.

The first tools that come to mind in considering how to best communicate data – especially statistics – are graphs and scatter plots. These simple visuals help us understand elementary causes and consequences, trends and so on. They are invaluable and have an important role in disseminating knowledge.

Data visualisation can take many other forms, just as data itself can be interpreted in many different ways. It can be used to highlight important achievements, as Bill and Melinda Gates have shown with their annual letters in which their main results and aspirations are creatively displayed.

Everyone has the potential to better explore data sets and provide more thorough, yet simple, representations of facts. But how can do we do this when faced with daunting levels of complex data?

A world of too many features

We can start by breaking the data down. Any data set consists of two main elements: samples and features. The former correspond to individual elements in a group; the latter are the characteristics they share….

Venturing into network analysis is easier than undertaking dimensionality reduction, since usually a high level of programming skills is not required. Widely available user-friendly software and tutorials allow people new to data visualisation to explore several aspects of network science.

The world of data visualisation is vast and it goes way beyond what has been introduced here, but those who actually reap its benefits, garnering new insights and becoming agents of positive and efficient change, are few. In an age of overwhelming information, knowing how to communicate data can make a difference – and it can help keep data’s relevance in check…(More)”

How Open Data Can Revolutionize a Society in Crisis

Curated on Posted on by Stefaan Verhulst

Beth Noveck at BrinkNews:”…These myriad open data success stories, however, depend on the political will to be transparent and collaborative. There is a looming risk that governments will only post what is expedient and noncontroversial while seeking recognition for their proactive disclosure—a practice increasingly referred to as “open-washing.” Governments of all political stripes refuse to disclose data when they should. The data to be found on government websites is not always the information most in demand by journalists, activists, and researchers.

Especially as political administrations turnover, there is a risk that change will result in a failure to collect and publish important data. These practices will be subject to the vagaries of politics.

The genie should not, however, be put back in the bottle.

Open data appeals to both right and left politically: the former sees open data as a pathway to smaller, more efficient government and the latter sees open data as a tool to pursue more effective social programs. The bipartisan interest in evidence-based approaches to governing should fuel greater demand for access to administrative information of all kinds—including the data that agencies collect about companies, workplaces, the environment, and the world beyond government.

Government data should be open in part because of the ill-effects of secrecy, but also because taxpayers have paid for the collection of this data by government in its role as regulator and researcher.

It is a pragmatic tool to make government and companies more accountable at solving social problems and to help communities make better informed buying decisions. It helps create jobs and generate entrepreneurship. Perhaps of paramount importance, open data can advance civil rights and help us to govern more legitimately and effectively….(More).