Study finds that a GPS outage would cost $1 billion per day


Eric Berger at Ars Technica: “….one of the most comprehensive studies on the subject has assessed the value of this GPS technology to the US economy and examined what effect a 30-day outage would have—whether it’s due to a severe space weather event or “nefarious activity by a bad actor.” The study was sponsored by the US government’s National Institutes of Standards and Technology and performed by a North Carolina-based research organization named RTI International.

Economic effect

As part of the analysis, researchers spoke to more than 200 experts in the use of GPS technology for various services, from agriculture to the positioning of offshore drilling rigs to location services for delivery drivers. (If they’d spoken to me, I’d have said the value of using GPS to navigate Los Angeles freeways and side streets was incalculable). The study covered a period from 1984, when the nascent GPS network was first opened to commercial use, through 2017. It found that GPS has generated an estimated $1.4 trillion in economic benefits during that time period.

The researchers found that the largest benefit, valued at $685.9 billion, came in the “telecommunications” category,  including improved reliability and bandwidth utilization for wireless networks. Telematics (efficiency gains, cost reductions, and environmental benefits through improved vehicle dispatch and navigation) ranked as the second most valuable category at $325 billion. Location-based services on smartphones was third, valued at $215 billion.

Notably, the value of GPS technology to the US economy is growing. According to the study, 90 percent of the technology’s financial impact has come since just 2010, or just 20 percent of the study period. Some sectors of the economy are only beginning to realize the value of GPS technology, or are identifying new uses for it, the report says, indicating that its value as a platform for innovation will continue to grow.

Outage impact

In the case of some adverse event leading to a widespread outage, the study estimates that the loss of GPS service would have a $1 billion per-day impact, although the authors acknowledge this is at best a rough estimate. It would likely be higher during the planting season of April and May, when farmers are highly reliant on GPS technology for information about their fields.

To assess the effect of an outage, the study looked at several different variables. Among them was “precision timing” that enables a number of wireless services, including the synchronization of traffic between carrier networks, wireless handoff between base stations, and billing management. Moreover, higher levels of precision timing enable higher bandwidth and provide access to more devices. (For example, the implementation of 4G LTE technology would have been impossible without GPS technology)….(More)”

The New York Times has a course to teach its reporters data skills, and now they’ve open-sourced it


Joshua Benton at Nieman Labs: “The New York Times wants more of its journalists to have those basic data skills, and now it’s releasing the curriculum they’ve built in-house out into the world, where it can be of use to reporters, newsrooms, and lots of other people too.

Here’s Lindsey Rogers Cook, an editor for digital storytelling and training at the Times, and the sort of person who is willing to have “spreadsheets make my heart sing” appear under her byline:

Even with some of the best data and graphics journalists in the business, we identified a challenge: data knowledge wasn’t spread widely among desks in our newsroom and wasn’t filtering into news desks’ daily reporting.

Yet fluency with numbers and data has become more important than ever. While journalists once were fond of joking that they got into the field because of an aversion to math, numbers now comprise the foundation for beats as wide-ranging as education, the stock market, the Census, and criminal justice. More data is released than ever before — there are nearly 250,000 datasets on data.govalone — and increasingly, government, politicians, and companies try to twist those numbers to back their own agendas…

We wanted to help our reporters better understand the numbers they get from sources and government, and give them the tools to analyze those numbers. We wanted to increase collaboration between traditional and non-traditional journalists…And with more competition than ever, we wanted to empower our reporters to find stories lurking in the hundreds of thousands of databases maintained by governments, academics, and think tanks. We wanted to give our reporters the tools and support necessary to incorporate data into their everyday beat reporting, not just in big and ambitious projects.

….You can access the Times’ training materials here. Some of what you’ll find:

  • An outline of the data skills the course aims to teach. It’s all run on Google Docs and Google Sheets; class starts with the uber-basics (mean! median! sum!), crosses the bridge of pivot tables, and then heads into data cleaning and more advanced formulas.
  • The full day-by-day outline of the Times’ three-week course, which of course you’re free to use or reshape to your newsroom’s needs.
  • It’s not just about cells, columns, and rows — the course also includes more journalism-based information around ethical questions, how to use data effectively inside a story’s narrative, and how best to work with colleagues in the graphic department.
  • Cheat sheets! If you don’t have time to dig too deeply, they’ll give a quick hit of information: onetwothreefourfive.
  • Data sets that you use to work through the beginner, intermediate, and advanced stages of the training, including such journalism classics as census datacampaign finance data, and BLS data.But don’t be a dummy and try to write real news stories off these spreadsheets; the Times cautions in bold: “NOTE: We have altered many of these datasets for instructional purposes, so please download the data from the original source if you want to use it in your reporting.”
  • How Not To Be Wrong,” which seems like a useful thing….(More)”

Data & Policy: A new venue to study and explore policy–data interaction


Opening editorial by Stefaan G. Verhulst, Zeynep Engin and Jon Crowcroft: “…Policy–data interactions or governance initiatives that use data have been the exception rather than the norm, isolated prototypes and trials rather than an indication of real, systemic change. There are various reasons for the generally slow uptake of data in policymaking, and several factors will have to change if the situation is to improve. ….

  • Despite the number of successful prototypes and small-scale initiatives, policy makers’ understanding of data’s potential and its value proposition generally remains limited (Lutes, 2015). There is also limited appreciation of the advances data science has made the last few years. This is a major limiting factor; we cannot expect policy makers to use data if they do not recognize what data and data science can do.
  • The recent (and justifiable) backlash against how certain private companies handle consumer data has had something of a reverse halo effect: There is a growing lack of trust in the way data is collected, analyzed, and used, and this often leads to a certain reluctance (or simply risk-aversion) on the part of officials and others (Engin, 2018).
  • Despite several high-profile open data projects around the world, much (probably the majority) of data that could be helpful in governance remains either privately held or otherwise hidden in silos (Verhulst and Young, 2017b). There remains a shortage not only of data but, more specifically, of high-quality and relevant data.
  • With few exceptions, the technical capacities of officials remain limited, and this has obviously negative ramifications for the potential use of data in governance (Giest, 2017).
  • It’s not just a question of limited technical capacities. There is often a vast conceptual and values gap between the policy and technical communities (Thompson et al., 2015; Uzochukwu et al., 2016); sometimes it seems as if they speak different languages. Compounding this difference in world views is the fact that the two communities rarely interact.
  • Yet, data about the use and evidence of the impact of data remain sparse. The impetus to use more data in policy making is stymied by limited scholarship and a weak evidential basis to show that data can be helpful and how. Without such evidence, data advocates are limited in their ability to make the case for more data initiatives in governance.
  • Data are not only changing the way policy is developed, but they have also reopened the debate around theory- versus data-driven methods in generating scientific knowledge (Lee, 1973; Kitchin, 2014; Chivers, 2018; Dreyfuss, 2017) and thus directly questioning the evidence base to utilization and implementation of data within policy making. A number of associated challenges are being discussed, such as: (i) traceability and reproducibility of research outcomes (due to “black box processing”); (ii) the use of correlation instead of causation as the basis of analysis, biases and uncertainties present in large historical datasets that cause replication and, in some cases, amplification of human cognitive biases and imperfections; and (iii) the incorporation of existing human knowledge and domain expertise into the scientific knowledge generation processes—among many other topics (Castelvecchi, 2016; Miller and Goodchild, 2015; Obermeyer and Emanuel, 2016; Provost and Fawcett, 2013).
  • Finally, we believe that there should be a sound under-pinning a new theory of what we call Policy–Data Interactions. To date, in reaction to the proliferation of data in the commercial world, theories of data management,1 privacy,2 and fairness3 have emerged. From the Human–Computer Interaction world, a manifesto of principles of Human–Data Interaction (Mortier et al., 2014) has found traction, which intends reducing the asymmetry of power present in current design considerations of systems of data about people. However, we need a consistent, symmetric approach to consideration of systems of policy and data, how they interact with one another.

All these challenges are real, and they are sticky. We are under no illusions that they will be overcome easily or quickly….

During the past four conferences, we have hosted an incredibly diverse range of dialogues and examinations by key global thought leaders, opinion leaders, practitioners, and the scientific community (Data for Policy, 2015201620172019). What became increasingly obvious was the need for a dedicated venue to deepen and sustain the conversations and deliberations beyond the limitations of an annual conference. This leads us to today and the launch of Data & Policy, which aims to confront and mitigate the barriers to greater use of data in policy making and governance.

Data & Policy is a venue for peer-reviewed research and discussion about the potential for and impact of data science on policy. Our aim is to provide a nuanced and multistranded assessment of the potential and challenges involved in using data for policy and to bridge the “two cultures” of science and humanism—as CP Snow famously described in his lecture on “Two Cultures and the Scientific Revolution” (Snow, 1959). By doing so, we also seek to bridge the two other dichotomies that limit an examination of datafication and is interaction with policy from various angles: the divide between practice and scholarship; and between private and public…

So these are our principles: scholarly, pragmatic, open-minded, interdisciplinary, focused on actionable intelligence, and, most of all, innovative in how we will share insight and pushing at the boundaries of what we already know and what already exists. We are excited to launch Data & Policy with the support of Cambridge University Press and University College London, and we’re looking for partners to help us build it as a resource for the community. If you’re reading this manifesto it means you have at least a passing interest in the subject; we hope you will be part of the conversation….(More)”.

From Planning to Prototypes: New Ways of Seeing Like a State


Fleur Johns at Modern Law Review: “All states have pursued what James C. Scott characterised as modernist projects of legibility and simplification: maps, censuses, national economic plans and related legislative programs. Many, including Scott, have pointed out blindspots embedded in these tools. As such criticism persists, however, the synoptic style of law and development has changed. Governments, NGOs and international agencies now aspire to draw upon immense repositories of digital data. Modes of analysis too have changed. No longer is legibility a precondition for action. Law‐ and policy‐making are being informed by business development methods that prefer prototypes over plans. States and international institutions continue to plan, but also seek insight from the release of minimally viable policy mock‐ups. Familiar critiques of law and development work, and arguments for its reform, have limited purchase on these practices, Scott’s included. Effective critical intervention in this field today requires careful attention to be paid to these emergent patterns of practice…(More)”.

Introducing ‘AI Commons’: A framework for collaboration to achieve global impact


Press Release: “Last week’s 3rd annual AI for Good Global Summit once again showcased the growing number of Artificial Intelligence (AI) projects with promise to advance the United Nations Sustainable Development Goals (SDGs).

Now, using the Summit’s momentum, AI innovators and humanitarian leaders are prepared to take the ‘AI for Good’ movement to the next level.

They are working together to launch an ‘AI Commons’ that aims to scale AI for Good projects and maximize their impact across the world.

The AI Commons will enable AI adopters to connect with AI specialists and data owners to align incentives for innovation and develop AI solutions to precisely defined problems.

“The concept of AI Commons has developed over three editions of the Summit and is now motivating implementation,” said ITU Secretary-General Houlin Zhao in closing remarks to the summit. “AI and data need to be a shared resource if we are serious about scaling AI for good. The community supporting the Summit is creating infrastructure to scale-up their collaboration − to convert the principles underlying the Summit into global impact.”…

The AI Commons will provide an open framework for collaboration, a decentralized system to democratize problem solving with AI.

It aims to be a “knowledge space”, says Banifatemi, answering a key question: “How can problem solving with AI become common knowledge?”

“The goal is to be an open initiative, like a Linux effort, like an open-source network, where everyone can participate and we jointly share and we create an abundance of knowledge, knowledge of how we can solve problems with AI,” said Banifatemi.

AI development and application will build on the state of the art, enabling AI solutions to scale with the help of shared datasets, testing and simulation environments, AI models and associated software, and storage and computing resources….(More)”.

Privacy Enhancing Technologies


The Royal Society: “How can technologies help organisations and individuals protect data in practice and, at the same time, unlock opportunities for data access and use?

The Royal Society’s Privacy Enhancing Technologies project has been investigating this question and has launched a report (PDF) setting out the current use, development and limits of privacy enhancing technologies (PETs) in data analysis. 

The data we generate every day holds a lot of value and potentially also contains sensitive information that individuals or organisations might not wish to share with everyone. The protection of personal or sensitive data featured prominently in the social and ethical tensions identified in our British Academy and Royal Society report Data management and use: Governance in the 21st century. For example, how can organisations best use data for public good whilst protecting sensitive information about individuals? Under other circumstances, how can they share data with groups with competing interests whilst protecting commercially or otherwise sensitive information?

Realising the full potential of large-scale data analysis may be constrained by important legal, reputational, political, business and competition concerns.  Certain risks can potentially be mitigated and managed with a set of emerging technologies and approaches often collectively referred to as ‘Privacy Enhancing Technologies’ (PETs). 

This disruptive set of technologies, combined with changes in wider policy and business frameworks, could enable the sharing and use of data in a privacy-preserving manner. They also have the potential to reshape the data economy and to change the trust relationships between citizens, governments and companies.

This report provides a high-level overview of five current and promising PETs of a diverse nature, with their respective readiness levels and illustrative case studies from a range of sectors, with a view to inform in particular applied data science research and the digital strategies of government departments and businesses. This report also includes recommendations on how the UK could fully realise the potential of PETs and to allow their use on a greater scale.

The project was informed by a series of conversations and evidence gathering events, involving a range of stakeholders across academia, government and the private sector (also see the project terms of reference and Working Group)….(More)”.

AI and the Global South: Designing for Other Worlds


Chapter by Chinmayi Arun in Markus D. Dubber, Frank Pasquale, and Sunit Das (eds.), The Oxford Handbook of Ethics of AI: “This chapter is about the ways in which AI affects, and will continue to affect, the Global South. It highlights why the design and deployment of AI in the South should concern us. 

Towards this, it discusses what is meant by the South. The term has a history connected with the ‘Third World’ and has referred to countries that share post-colonial history and certain development goals. However scholars have expanded and refined on it to include different kinds of marginal, disenfranchised populations such that the South is now a plural concept – there are Souths. 

The risks of the ways in which AI affects Southern populations include concerns of discrimination, bias, oppression, exclusion and bad design. These can be exacerbated in the context of vulnerable populations, especially those without access to human rights law or institutional remedies. This Chapter outlines these risks as well as the international human rights law that is applicable. It argues that a human rights, centric, inclusive, empowering context-driven approach is necessary….(More)”.

Number of fact-checking outlets surges to 188 in more than 60 countries


Mark Stencel at Poynter: “The number of fact-checking outlets around the world has grown to 188 in more than 60 countries amid global concerns about the spread of misinformation, according to the latest tally by the Duke Reporters’ Lab.

Since the last annual fact-checking census in February 2018, we’ve added 39 more outlets that actively assess claims from politicians and social media, a 26% increase. The new total is also more than four times the 44 fact-checkers we counted when we launched our global database and map in 2014.

Globally, the largest growth came in Asia, which went from 22 to 35 outlets in the past year. Nine of the 27 fact-checking outlets that launched since the start of 2018 were in Asia, including six in India. Latin American fact-checking also saw a growth spurt in that same period, with two new outlets in Costa Rica, and others in Mexico, Panama and Venezuela.

The actual worldwide total is likely much higher than our current tally. That’s because more than a half-dozen of the fact-checkers we’ve added to the database since the start of 2018 began as election-related partnerships that involved the collaboration of multiple organizations. And some those election partners are discussing ways to continue or reactivate that work— either together or on their own.

Over the past 12 months, five separate multimedia partnerships enlisted more than 60 different fact-checking organizations and other news companies to help debunk claims and verify information for voters in MexicoBrazilSweden,Nigeria and the Philippines. And the Poynter Institute’s International Fact-Checking Network assembled a separate team of 19 media outlets from 13 countries to consolidate and share their reporting during the run-up to last month’s elections for the European Parliament. Our database includes each of these partnerships, along with several others— but not each of the individual partners. And because they were intentionally short-run projects, three of these big partnerships appear among the 74 inactive projects we also document in our database.

Politics isn’t the only driver for fact-checkers. Many outlets in our database are concentrating efforts on viral hoaxes and other forms of online misinformation — often in coordination with the big digital platforms on which that misinformation spreads.

We also continue to see new topic-specific fact-checkers such as Metafact in Australia and Health Feedback in France— both of which launched in 2018 to focus on claims about health and medicine for a worldwide audience….(More)”.

Applying crowdsourcing techniques in urban planning: A bibliometric analysis of research and practice prospects


Paper by Pinchao Liao et al in Cities: “Urban planning requires more public involvement and larger group participation to achieve scientific and democratic decision making. Crowdsourcing is a novel approach to gathering information, encouraging innovation and facilitating group decision-making. Unfortunately, although previous research has explored the utility of crowdsourcing applied to urban planning theoretically, there are still rare real practices or empirical studies using practical data. This study aims to identify the prospects for implementing crowdsourcing in urban planning through a bibliometric analysis on current research.

First, database and keyword lists based on peer-reviewed journal articles were developed. Second, semantic analysis is applied to quantify co-occurrence frequencies of various terms in the articles based on the keyword lists, and in turn a semantic network is built.

Then, cluster analysis was conducted to identify major and correlated research topics, and bursting key terms were analyzed and explained chronologically. Lastly, future research and practical trends were discussed.

The major contribution of this study is identifying crowdsourcing as a novel urban planning method, which can strengthen government capacities by involving public participation, i.e., turning governments into task givers. Regarding future patterns, the application of crowdsourcing in urban planning is expected to expand to transportation, public health and environmental issues. It is also indicated that the use of crowdsourcing requires governments to adjust urban planning mechanisms….(More)”.

The Tricky Ethics of Using YouTube Videos for Academic Research


Jane C.Hu in P/S Magazine: “…But just because something is legal doesn’t mean it’s ethical. That doesn’t mean it’s necessarily unethical, either, but it’s worth asking questions about how and why researchers use social media posts, and whether those uses could be harmful. I was once a researcher who had to obtain human-subjects approval from a university institutional review board, and I know it can be a painstaking application process with long wait times. Collecting data from individuals takes a long time too. If you could just sub in YouTube videos in place of collecting your own data, that saves time, money, and effort. But that could be at the expense of the people whose data you’re scraping.

But, you might say, if people don’t want to be studied online, then they shouldn’t post anything. But most people don’t fully understand what “publicly available” really means or its ramifications. “You might know intellectually that technically anyone can see a tweet, but you still conceptualize your audience as being your 200 Twitter followers,” Fiesler says. In her research, she’s found that the majority of people she’s polled have no clue that researchers study public tweets.

Some may disagree that it’s researchers’ responsibility to work around social media users’ ignorance, but Fiesler and others are calling for their colleagues to be more mindful about any work that uses publicly available data. For instance, Ashley Patterson, an assistant professor of language and literacy at Penn State University, ultimately decided to use YouTube videos in her dissertation work on biracial individuals’ educational experiences. That’s a decision she arrived at after carefully considering her options each step of the way. “I had to set my own levels of ethical standards and hold myself to it, because I knew no one else would,” she says. One of Patterson’s first steps was to ask herself what YouTube videos would add to her work, and whether there were any other ways to collect her data. “It’s not a matter of whether it makes my life easier, or whether it’s ‘just data out there’ that would otherwise go to waste. The nature of my question and the response I was looking for made this an appropriate piece [of my work],” she says.

Researchers may also want to consider qualitative, hard-to-quantify contextual cues when weighing ethical decisions. What kind of data is being used? Fiesler points out that tweets about, say, a television show are way less personal than ones about a sensitive medical condition. Anonymized written materials, like Facebook posts, could be less invasive than using someone’s face and voice from a YouTube video. And the potential consequences of the research project are worth considering too. For instance, Fiesler and other critics have pointed out that researchers who used YouTube videos of people documenting their experience undergoing hormone replacement therapy to train an artificial intelligence to identify trans people could be putting their unwitting participants in danger. It’s not obvious how the results of Speech2Face will be used, and, when asked for comment, the paper’s researchers said they’d prefer to quote from their paper, which pointed to a helpful purpose: providing a “representative face” based on the speaker’s voice on a phone call. But one can also imagine dangerous applications, like doxing anonymous YouTubers.

One way to get ahead of this, perhaps, is to take steps to explicitly inform participants their data is being used. Fiesler says that, when her team asked people how they’d feel after learning their tweets had been used for research, “not everyone was necessarily super upset, but most people were surprised.” They also seemed curious; 85 percent of participants said that, if their tweet were included in research, they’d want to read the resulting paper. “In human-subjects research, the ethical standard is informed consent, but inform and consent can be pulled apart; you could potentially inform people without getting their consent,” Fiesler suggests….(More)”.