Building Trust in Data and Statistics

Curated on Posted on by Stefaan Verhulst

Shaida Badiee at UN World Data Forum: …What do we want for a 2030 data ecosystem?

Hope to achieve: A world where data are part of the DNA and culture of decision-making, used by all and valued as an important public good. A world where citizens trust the systems that produce data and have the skills and means to use and verify their quality and accuracy. A world where there are safeguards in place to protect privacy, while bringing the benefits of open data to all. In this world, countries value their national statistical systems, which are working independently with trusted partners in the public and private sectors and citizens to continuously meet the changing and expanding demands from data users and policy makers. Private sector data generators are generously sharing their data with public sector. And gaps in data are closing, making the dream of “leaving no one behind” come true, with SDG goals on the path to being met by 2030.

Hope to avoid: A world where large corporations control the bulk of national and international data and statistics with only limited sharing with the public sector, academics, and citizens. The culture of every man for himself and who pays, wins, dominates data sharing practices. National statistical systems are under-resourced and under-valued, with low trust from users, further weakening them and undermining their independence from political interference and their ability to control quality. The divide between those who have and those who do not have access, skills, and the ability to use data for decision-making and policy has widened. Data systems and their promise to count the uncounted and “leave no one behind” are falling behind due to low capacity and poor standards and institutions, and the hope of the 2030 agenda is fading.

With this vision in mind, are we on the right path? An optimist would say we are closer to the data ecosystem that we want to achieve. However, there are also some examples of movement in the wrong direction. There is no magic wand to make our wish come true, but a powerful enabler would be building trust in data and statistics. Therefore, this should be included as a goal in all our data strategies and action plans.

Here are some important building blocks underlying trust in data and statistics:

  1. Building strong organizational infrastructure, governance, and partnerships;
  2. Following sound data standards and principles for production, sharing, interoperability, and dissemination; and
  3. Addressing the last mile in the data value chain to meet users’ needs, create value with data, and ensure meaningful impacts…(More)”.

Invisible Algorithms, Invisible Politics

Curated on Posted on by Stefaan Verhulst

Laura Forlano at Public Books: “Over the past several decades, politicians and business leaders, technology pundits and the mainstream media, engineers and computer scientists—as well as science fiction and Hollywood films—have repeated a troubling refrain, championing the shift away from the material and toward the virtual, the networked, the digital, the online. It is as if all of life could be reduced to 1s and 0s, rendering it computable….

Today, it is in design criteria and engineering specifications—such as “invisibility” and “seamlessness,” which aim to improve the human experience with technology—that ethical decisions are negotiated….

Take this example. In late July 2017, the City of Chicago agreed to settle a $38.75 million class-action lawsuit related to its red-light-camera program. Under the settlement, the city will repay drivers who were unfairly ticketed a portion of the cost of their ticket. Over the past five years, the program, ostensibly implemented to make Chicago’s intersections safer, has been mired in corruption, bribery, mismanagement, malfunction, and moral wrongdoing. This confluence of factors has resulted in a great deal of negative press about the project.

The red-light-camera program is just one of many examples of such technologies being adopted by cities in their quest to become “smart” and, at the same time, increase revenue. Others include ticketless parking, intelligent traffic management, ride-sharing platforms, wireless networks, sensor-embedded devices, surveillance cameras, predictive policing software, driverless car testbeds, and digital-fabrication facilities.

The company that produced the red-light cameras, Redflex, claims on their website that their technology can “reliably and consistently address negative driving behaviors and effectively enforce traffic laws on roadways and intersections with a history of crashes and incidents.”Nothing could be further from the truth. Instead, the cameras were unnecessarily installed at some intersections without a history of problems; they malfunctioned; they issued illegal tickets due to short yellow-lights that were not within federal limits; and they issued tickets after enforcement hours. And, due to existing structural inequalities, these difficulties were more likely to negatively impact poorer and less advantaged city residents.

The controversies surrounding red-light cameras in Chicago make visible the ways in which design criteria and engineering specifications—concepts including safety and efficiency, seamlessness and stickiness, convenience and security—are themselves ways of defining the ethics, values, and politics of our cities and citizens. To be sure, these qualities seem clean, comforting, and cuddly at first glance. They are difficult to argue against.

But, like wolves in sheep’s clothing, they gnash their political-economic teeth, and show their insatiable desire to further the goals of neoliberal capitalism. Rather than merely slick marketing, these mundane infrastructures (hardware, software, data, and services) negotiate ethical questions around what kinds of societies we aspire to, what kind of cities we want to live in, what kinds of citizens we can become, who will benefit from these tradeoffs, and who will be left out….(More)

Republics of Makers: From the Digital Commons to a Flat Marginal Cost Society

Curated on Posted on by Stefaan Verhulst

Mario Carpo at eFlux: “…as the costs of electronic computation have been steadily decreasing for the last forty years at least, many have recently come to the conclusion that, for most practical purposes, the cost of computation is asymptotically tending to zero. Indeed, the current notion of Big Data is based on the assumption that an almost unlimited amount of digital data will soon be available at almost no cost, and similar premises have further fueled the expectation of a forthcoming “zero marginal costs society”: a society where, except for some upfront and overhead costs (the costs of building and maintaining some facilities), many goods and services will be free for all. And indeed, against all odds, an almost zero marginal cost society is already a reality in the case of many services based on the production and delivery of electricity: from the recording, transmission, and processing of electrically encoded digital information (bits) to the production and consumption of electrical power itself. Using renewable energies (solar, wind, hydro) the generation of electrical power is free, except for the cost of building and maintaining installations and infrastructure. And given the recent progress in the micro-management of intelligent electrical grids, it is easy to imagine that in the near future the cost of servicing a network of very small, local hydro-electric generators, for example, could easily be devolved to local communities of prosumers who would take care of those installations as their tend to their living environment, on an almost voluntary, communal basis.4 This was already often the case during the early stages of electrification, before the rise of AC (alternate current, which, unlike DC, or direct current, could be carried over long distances): AC became the industry’s choice only after Galileo Ferraris’s and Nikola Tesla’s developments in AC technologies in the 1880s.

Likewise, at the micro-scale of the electronic production and processing of bits and bytes of information, the Open Source movement and the phenomenal surge of some crowdsourced digital media (including some so-called social media) in the first decade of the twenty-first century has already proven that a collaborative, zero cost business model can effectively compete with products priced for profit on a traditional marketplace. As the success of Wikipedia, Linux, or Firefox proves, many are happy to volunteer their time and labor for free when all can profit from the collective work of an entire community without having to pay for it. This is now technically possible precisely because the fixed costs of building, maintaining, and delivering these service are very small; hence, from the point of view of the end-user, negligible.

Yet, regardless of the fixed costs of the infrastructure, content—even user-generated content—has costs, albeit for the time being these are mostly hidden, voluntarily born, or inadvertently absorbed by the prosumers themselves. For example, the wisdom of Wikipedia is not really a wisdom of crowds: most Wikipedia entries are de facto curated by fairly traditional scholar communities, and these communities can contribute their expertise for free only because their work has already been paid for by others—often by universities. In this sense, Wikipedia is only piggybacking on someone else’s research investments (but multiplying their outreach, which is one reason for its success). Ditto for most Open Source software, as training a software engineer, coder, or hacker, takes time and money—an investment for future returns that in many countries around the world is still born, at least in part, by public institutions….(More)”.

Self-Tracking: Empirical and Philosophical Investigations

Curated on Posted on by Stefaan Verhulst

Book edited by Btihaj Ajana: “…provides an empirical and philosophical investigation of self-tracking practices. In recent years, there has been an explosion of apps and devices that enable the data capturing and monitoring of everyday activities, behaviours and habits. Encouraged by movements such as the Quantified Self, a growing number of people are embracing this culture of quantification and tracking in the spirit of improving their health and wellbeing.
The aim of this book is to enhance understanding of this fast-growing trend, bringing together scholars who are working at the forefront of the critical study of self-tracking practices. Each chapter provides a different conceptual lens through which one can examine these practices, while grounding the discussion in relevant empirical examples.
From phenomenology to discourse analysis, from questions of identity, privacy and agency to issues of surveillance and tracking at the workplace, this edited collection takes on a wide, and yet focused, approach to the timely topic of self-tracking. It constitutes a useful companion for scholars, students and everyday users interested in the Quantified Self phenomenon…(More)”.

Feasibility Study of Using Crowdsourcing to Identify Critical Affected Areas for Rapid Damage Assessment: Hurricane Matthew Case Study

Curated on Posted on by Stefaan Verhulst

Paper by Faxi Yuan and Rui Liu at the International Journal of Disaster Risk Reduction: “…rapid damage assessment plays a critical role in crisis management. Collection of timely information for rapid damage assessment is particularly challenging during natural disasters. Remote sensing technologies were used for data collection during disasters. However, due to the large areas affected by major disasters such as Hurricane Matthew, specific data cannot be collected in time such as the location information.

Social media can serve as a crowdsourcing platform for citizens’ communication and information sharing during natural disasters and provide the timely data for identifying affected areas to support rapid damage assessment during disasters. Nevertheless, there is very limited existing research on the utility of social media data in damage assessment. Even though some investigation of the relationship between social media activities and damages was conducted, the employment of damage-related social media data in exploring the fore-mentioned relationship remains blank.

This paper for the first time, establishes the index dictionary by semantic analysis for the identification of damage-related tweets posted during Hurricane Matthew in Florida. Meanwhile, the insurance claim data from the publication of Florida Office of Insurance Regulation is used as a representative of real hurricane damage data in Florida. This study performs a correlation analysis and a comparative analysis of the geographic distribution of social media data and damage data at the county level in Florida. We find that employing social media data to identify critical affected areas at the county level during disasters is viable. Damage data has a closer relationship with damage-related tweets than disaster-related tweets….(More)”.

 

Algorithms of Oppression: How Search Engines Reinforce Racism

Curated on Posted on by Stefaan Verhulst
Book by Safiya Umoja Noble: “Run a Google search for “black girls”—what will you find? “Big Booty” and other sexually explicit terms are likely to come up as top search terms. But, if you type in “white girls,” the results are radically different. The suggested porn sites and un-moderated discussions about “why black women are so sassy” or “why black women are so angry” presents a disturbing portrait of black womanhood in modern society.
In Algorithms of Oppression, Safiya Umoja Noble challenges the idea that search engines like Google offer an equal playing field for all forms of ideas, identities, and activities. Data discrimination is a real social problem; Noble argues that the combination of private interests in promoting certain sites, along with the monopoly status of a relatively small number of Internet search engines, leads to a biased set of search algorithms that privilege whiteness and discriminate against people of color, specifically women of color.
Through an analysis of textual and media searches as well as extensive research on paid online advertising, Noble exposes a culture of racism and sexism in the way discoverability is created online. As search engines and their related companies grow in importance—operating as a source for email, a major vehicle for primary and secondary school learning, and beyond—understanding and reversing these disquieting trends and discriminatory practices is of utmost importance.
An original, surprising and, at times, disturbing account of bias on the internet, Algorithms of Oppression contributes to our understanding of how racism is created, maintained, and disseminated in the 21st century….(More)”.

Managing Democracy in the Digital Age

Curated on Posted on by Stefaan Verhulst

Book edited by Julia Schwanholz, Todd Graham and Peter-Tobias Stoll: “In light of the increased utilization of information technologies, such as social media and the ‘Internet of Things,’ this book investigates how this digital transformation process creates new challenges and opportunities for political participation, political election campaigns and political regulation of the Internet. Within the context of Western democracies and China, the contributors analyze these challenges and opportunities from three perspectives: the regulatory state, the political use of social media, and through the lens of the public sphere.

The first part of the book discusses key challenges for Internet regulation, such as data protection and censorship, while the second addresses the use of social media in political communication and political elections. In turn, the third and last part highlights various opportunities offered by digital media for online civic engagement and protest in the public sphere. Drawing on different academic fields, including political science, communication science, and journalism studies, the contributors raise a number of innovative research questions and provide fascinating theoretical and empirical insights into the topic of digital transformation….(More)”.

Landscape of Innovation Approaches

Curated on Posted on by Stefaan Verhulst

Bas Leurs at Nesta: “Through our work in the Innovation Skills team, we often find ourselves being asked by governments and civil servants which innovation tools and techniques they should use. So what innovation approaches are there that can be applied in the public sector? And how are they related to each other?

With these questions in mind, over the last couple of years we’ve been mapping out the various innovation methods and approaches we’ve come across from studying innovation practice and our many conversations with different lab practitioners, colleagues and other innovation experts.

Download this diagram as a PDF.

The map we’ve created provides an overview of innovation methods and approaches that help people make sense of reality, and approaches that help develop solutions and interventions to create change.

Understanding and shaping reality

The approaches mapped out in the diagram are structured into four spaces: intelligence, solution, technology and talent. These spaces are built on the premise that in order to create change, you need to make sense and understand reality, as well as develop solutions and interventions to change that reality:

  • intelligence space – focuses on approaches that help you make sense of and conceptualise reality

  • solution space – focuses on methods that help you test and develop solutions

In terms of mindsets, you could say that the intelligence space is more academic, whereas the solution space involves more of an entrepreneurial approach. The activities in these are supported by two further spaces:

  • technology space – includes approaches and technology that enable action and change, such as digital tools and data-related methods

  • talent space – focuses on how to mobilise talent, develop skills and increase organisational readiness in order to ultimately make change happen…(More)”.

A Really Bad Blockchain Idea: Digital Identity Cards for Rohingya Refugees

Curated on Posted on by Stefaan Verhulst

Wayan Vota at ICTworks: “The Rohingya Project claims to be a grassroots initiative that will empower Rohingya refugees with a blockchain-leveraged financial ecosystem tied to digital identity cards….

What Could Possibly Go Wrong?

Concerns about Rohingya data collection are not new, so Linda Raftree‘s Facebook post about blockchain for biometrics started a spirited discussion on this escalation of techno-utopia. Several people put forth great points about the Rohingya Project’s potential failings. For me, there were four key questions originating in the discussion that we should all be debating:

1. Who Determines Ethnicity?

Ethnicity isn’t a scientific way to categorize humans. Ethnic groups are based on human constructs such as common ancestry, language, society, culture, or nationality. Who are the Rohingya Project to be the ones determining who is Rohingya or not? And what is this rigorous assessment they have that will do what science cannot?

Might it be better not to perpetuate the very divisions that cause these issues? Or at the very least, let people self-determine their own ethnicity.

2. Why Digitally Identify Refugees?

Let’s say that we could group a people based on objective metrics. Should we? Especially if that group is persecuted where it currently lives and in many of its surrounding countries? Wouldn’t making a list of who is persecuted be a handy reference for those who seek to persecute more?

Instead, shouldn’t we focus on changing the mindset of the persecutors and stop the persecution?

3. Why Blockchain for Biometrics?

How could linking a highly persecuted people’s biometric information, such as fingerprints, iris scans, and photographs, to a public, universal, and immutable distributed ledger be a good thing?

Might it be highly irresponsible to digitize all that information? Couldn’t that data be used by nefarious actors to perpetuate new and worse exploitation of Rohingya? India has already lost Aadhaar data and the Equafax lost Americans’ data. How will the small, lightly funded Rohingya Project do better?

Could it be possible that old-fashioned paper forms are a better solution than digital identity cards? Maybe laminate them for greater durability, but paper identity cards can be hidden, even destroyed if needed, to conceal information that could be used against the owner.

4. Why Experiment on the Powerless?

Rohingya refugees already suffer from massive power imbalances, and now they’ll be asked to give up their digital privacy, and use experimental technology, as part of an NGO’s experiment, in order to get needed services.

Its not like they’ll have the agency to say no. They are homeless, often penniless refugees, who will probably have no realistic way to opt-out of digital identity cards, even if they don’t want to be experimented on while they flee persecution….(More)”

The Entrepreneurial Impact of Open Data

Curated on Posted on by Stefaan Verhulst

Sheena Iyengar and  Patrick Bergemann at Opening Governance Research Network: “…To understand how open data is being used to spur innovation and create value, the Governance Lab (GovLab) at NYU Tandon School of Engineering conducted the first ever census of companies that use open data. Using outreach campaigns, expert advice and other sources, they created a database of more than 500 companies founded in the United States called the Open Data 500 (OD500). Among the small and medium enterprises identified that use government data, the most common industries they found are data and technology, followed by finance and investment, business and legal services, and healthcare.

In the context of our collaboration with the GovLab-chaired MacArthur Foundation Research Network on Opening Governance, we sought to dig deeper into the broader impact of open data on entrepreneurship. To do so we combined the OD500 with databases on startup activity from Crunchbase and AngelList. This allowed us to look at the trajectories of open data companies from their founding to the present day. In particular, we compared companies that use open data to similar companies with the same founding year, location and industry to see how well open data companies fare at securing funding along with other indicators of success.

We first looked at the extent to which open data companies have access to investor capital, wondering if open data companies have difficulty gaining funding because their use of public data may be perceived as insufficiently innovative or proprietary. If this is the case, the economic impact of open data may be limited. Instead, we found that open data companies obtain more investors than similar companies that do not use open data. Open data companies have, on average, 1.74 more investors than similar companies founded at the same time. Interestingly, investors in open data companies are not a specific group who specialize in open data startups. Instead, a wide variety of investors put money into these companies. Of the investors who funded open data companies, 59 percent had only invested in one open data company, while 81 percent had invested in one or two. Open data companies appear to be appealing to a wide range of investors….(More)”.