New book by Clive Thompson: “It’s undeniable—technology is changing the way we think. But is it for the better? Amid a chorus of doomsayers, Clive Thompson delivers a resounding “yes.” The Internet age has produced a radical new style of human intelligence, worthy of both celebration and analysis. We learn more and retain it longer, write and think with global audiences, and even gain an ESP-like awareness of the world around us. Modern technology is making us smarter, better connected, and often deeper—both as individuals and as a society.
In Smarter Than You Think Thompson shows that every technological innovation—from the written word to the printing press to the telegraph—has provoked the very same anxieties that plague us today. We panic that life will never be the same, that our attentions are eroding, that culture is being trivialized. But as in the past, we adapt—learning to use the new and retaining what’s good of the old.”
Cyberpsychology and New Media
A thematic reader, edited by Andrew Power, Grainne Kirwan: “Cyberpsychology is the study of human interactions with the internet, mobile computing and telephony, games consoles, virtual reality, artificial intelligence, and other contemporary electronic technologies. The field has grown substantially over the past few years and this book surveys how researchers are tackling the impact of new technology on human behaviour and how people interact with this technology.
Examining topics as diverse as online dating, social networking, online communications, artificial intelligence, health-information seeking behaviour, education online, online therapies and cybercrime, Cyberpsychology and New Media book provides an in-depth overview of this burgeoning field, and allows those with little previous knowledge to gain an appreciation of the diversity of the research being undertaken in the area.”
OECD's Revised Guidelines on Privacy
OECD: “Over many decades the OECD has played an important role in promoting respect for privacy as a fundamental value and a condition for the free flow of personal data across borders. The cornerstone of OECD work on privacy is its newly revised Guidelines on the Protection of Privacy and Transborder Flows of Personal Data (2013).
Another key component of work in this area aims to improve cross-border co-operation among privacy law enforcement authorities. This work produced an OECD Recommendation on Cross-border Co-operation in the Enforcement of Laws Protecting Privacy in 2007 and inspired the formation of the Global Privacy Enforcement Network, to which the OECD provides support.
Other projects have examined privacy notices and considered privacy in the context of horizontal issues such as radio frequency indentification (RFID), digital identity management, and looked at metrics to inform policy making in these areas. The important role of privacy is also addressed in the OECD Recommendation on Principles for Internet Policy Making (2011) and the Seoul Ministerial Declaration on the Future of the Internet Economy (2008).
Current work is examining privacy-related issues raised by large-scale data use and analytics. It is part of a broader project on the data-driven innovation and growth, which already produced a preliminary report identifying key issues.”
Here’s how the Recovery Act became a test case for open data
Andrea Peterson in the Washington Post: “Making sure that government money is spent efficiently and without fraud can be difficult. You need to collect the right data, get the information to the right people, and deal with the sheer volume of projects that need tracking. Open data make the job easier to draw comparisons across programs and agencies. And when data are released to the public, everyone can help be a government watchdog.
When President Obama was first elected in 2008, he promised transparency. Almost immediately after he was sworn into office, he had an opportunity to test that promise with the implementation of the Recovery Act. And it worked….
Recovery.gov used geospatial technology to “allow Americans to drill down to their zip codes exactly where government money was being spent in their neighborhood.” It’s this micro-level of attention that increased accountability, according to Devaney.
“The degree of transparency forced them to get it right because they didn’t want to be embarrassed by their neighbors who they knew were going to these Web sites and could see what they were doing with the money.”
As to the second question of what data to collect: “I finally put my foot down and said no more than 100 pieces of data,” Devaney recalls, “So naturally, we came up to 99.” Of course, even with limiting themselves to that number of data points, transparency and fraud prevention was a daunting task, with the 300,000 some grantees to keep tabs on.
But having those data points in an open format was what allowed investigators to use “sophisticated cyber-technology and software to review and analyze Recovery-related data and information for any possible concerns or issues.” And they were remarkably successful on that end. A status report in October, 2010 showed “less than 0.2 percent of all reported awards currently have active fraud investigations.” Indeed, for Devaney’s tenure leading the board he says the level of fraud hovered somewhere below half of one percent of all awards.”
OpenPrism
OpenPrism is my most recent attempt at understanding what is going on in all of these portals. Read on if you want to see why I made it, or just go to the site and start playing with it.
People don’t know much about open data
Nobody seems to know what is in the data portals. Many people know about datasets that are relevant to their work, municipality, &c., but nobody seems to know about the availability of data on broader topics, and nobody seems to have a good way of finding out what is available.
If someone does know any of this, he probably works for an open data portal. Still, he probably doesn’t know much about what is going on in other portals.
Naive search method
One difficulty in discovering open data is the search paradigm.
Open data portals approach searching data as if data were normal prose; your search terms are some keywords, a category, &c., and your results are dataset titles and descriptions.
There are other approaches. For example, AppGen searches for datasets with the same variables as each other, and the results are automatically generated app prototypes.
Siloed open data portals
Another issue is that people tend to use data from only one portal; they use their local government’s portals or their organizations’ portals.
Let me give you a couple examples of why this should maybe be different. Perhaps I’m considering making an app to help people find parking, and I want to see what parking lot data are available before I put much work into the app. Or maybe I want to find all of the data about sewer overflows so that I can expand my initiative to reduce water pollution.
OpenPrism is one small attempt at making it easier to search. Rather than going to all of the different portals and making a separate search for each portal, you type your search in one search bar, and you get results from a bunch of different Socrata, CKAN and Junar portals.”
(Appropriate) Big Data for Climate Resilience?
Amy Luers at the Stanford Social Innovation Review: “The answer to whether big data can help communities build resilience to climate change is yes—there are huge opportunities, but there are also risks.
Opportunities
- Feedback: Strong negative feedback is core to resilience. A simple example is our body’s response to heat stress—sweating, which is a natural feedback to cool down our body. In social systems, feedbacks are also critical for maintaining functions under stress. For example, communication by affected communities after a hurricane provides feedback for how and where organizations and individuals can provide help. While this kind of feedback used to rely completely on traditional communication channels, now crowdsourcing and data mining projects, such as Ushahidi and Twitter Earthquake detector, enable faster and more-targeted relief.
- Diversity: Big data is enhancing diversity in a number of ways. Consider public health systems. Health officials are increasingly relying on digital detection methods, such as Google Flu Trends or Flu Near You, to augment and diversify traditional disease surveillance.
- Self-Organization: A central characteristic of resilient communities is the ability to self-organize. This characteristic must exist within a community (see the National Research Council Resilience Report), not something you can impose on it. However, social media and related data-mining tools (InfoAmazonia, Healthmap) can enhance situational awareness and facilitate collective action by helping people identify others with common interests, communicate with them, and coordinate efforts.
Risks
- Eroding trust: Trust is well established as a core feature of community resilience. Yet the NSA PRISM escapade made it clear that big data projects are raising privacy concerns and possibly eroding trust. And it is not just an issue in government. For example, Target analyzes shopping patterns and can fairly accurately guess if someone in your family is pregnant (which is awkward if they know your daughter is pregnant before you do). When our trust in government, business, and communities weakens, it can decrease a society’s resilience to climate stress.
- Mistaking correlation for causation: Data mining seeks meaning in patterns that are completely independent of theory (suggesting to some that theory is dead). This approach can lead to erroneous conclusions when correlation is mistakenly taken for causation. For example, one study demonstrated that data mining techniques could show a strong (however spurious) correlation between the changes in the S&P 500 stock index and butter production in Bangladesh. While interesting, a decision support system based on this correlation would likely prove misleading.
- Failing to see the big picture: One of the biggest challenges with big data mining for building climate resilience is its overemphasis on the hyper-local and hyper-now. While this hyper-local, hyper-now information may be critical for business decisions, without a broader understanding of the longer-term and more-systemic dynamism of social and biophysical systems, big data provides no ability to understand future trends or anticipate vulnerabilities. We must not let our obsession with the here and now divert us from slower-changing variables such as declining groundwater, loss of biodiversity, and melting ice caps—all of which may silently define our future. A related challenge is the fact that big data mining tends to overlook the most vulnerable populations. We must not let the lure of the big data microscope on the “well-to-do” populations of the world make us blind to the less well of populations within cities and communities that have more limited access to smart phones and the Internet.”
Three ways to think of the future…
Geoff Mulgan’s blog: “Here I suggest three complementary ways of thinking about the future which provide partial protection against the pitfalls.
The shape of the future
First, create your own composite future by engaging with the trends. There are many methods available for mapping the future – from Foresight to scenarios to the Delphi method.
Behind all are implicit views about the shapes of change. Indeed any quantitative exploration of the future uses a common language of patterns (shown in this table above) which summarises the fact that some things will go up, some go down, some change suddenly and some not at all.
All of us have implicit or explicit assumptions about these. But it’s rare to interrogate them systematically and test whether our assumptions about what fits in which category are right.
Let’s start with the J shaped curves. Many of the long-term trends around physical phenomena look J-curved: rising carbon emissions, water useage and energy consumption have been exponential in shape over the centuries. As we know, physical constraints mean that these simply can’t go on – the J curves have to become S shaped sooner or later, or else crash. That is the ecological challenge of the 21st century.
New revolutions
But there are other J curves, particularly the ones associated with digital technology. Moore’s Law and Metcalfe’s Law describe the dramatically expanding processing power of chips, and the growing connectedness of the world. Some hope that the sheer pace of technological progress will somehow solve the ecological challenges. That hope has more to do with culture than evidence. But these J curves are much faster than the physical ones – any factor that doubles every 18 months achieves stupendous rates of change over decades.
That’s why we can be pretty confident that digital technologies will continue to throw up new revolutions – whether around the Internet of Things, the quantified self, machine learning, robots, mass surveillance or new kinds of social movement. But what form these will take is much harder to predict, and most digital prediction has been unreliable – we have Youtube but not the Interactive TV many predicted (when did you last vote on how a drama should end?); relatively simple SMS and twitter spread much more than ISDN or fibre to the home. And plausible ideas like the long tail theory turned out to be largely wrong.
If the J curves are dramatic but unusual, much more of the world is shaped by straight line trends – like ageing or the rising price of disease that some predict will take costs of healthcare up towards 40 or 50% of GDP by late in the century, or incremental advances in fuel efficiency, or the likely relative growth of the Chinese economy.
Also important are the flat straight lines – the things that probably won’t change in the next decade or two: the continued existence of nation states not unlike those of the 19th century? Air travel making use of fifty year old technologies?
Great imponderables
If the Js are the most challenging trends, the most interesting ones are the ‘U’s’- the examples of trends bending: like crime which went up for a century and then started going down, or world population that has been going up but could start going down in the later part of this century, or divorce rates which seem to have plateaued, or Chinese labour supply which is forecast to turn down in the 2020s.
No one knows if the apparently remorseless upward trends of obesity and depression will turn downwards. No one knows if the next generation in the West will be poorer than their parents. And no one knows if democratic politics will reinvent itself and restore trust. In every case, much depends on what we do. None of these trends is a fact of nature or an act of God.
That’s one reason why it’s good to immerse yourself in these trends and interrogate what shape they really are. Out of that interrogation we can build a rough mental model and generate our own hypotheses – ones not based on the latest fashion or bestseller but hopefully on a sense of what the data shows and in particular what’s happening to the deltas – the current rates of change of different phenomena.”
Open Access
Reports by the UK’s House of Commons, Business, Innovation and Skills Committee: “Open access refers to the immediate, online availability of peer reviewed research articles, free at the point of access (i.e. without subscription charges or paywalls). Open access relates to scholarly articles and related outputs. Open data (which is a separate area of Government policy and outside the scope of this inquiry) refers to the availability of the underlying research data itself. At the heart of the open access movement is the principle that publicly funded research should be publicly accessible. Open access expanded rapidly in the late twentieth century with the growth of the internet and digitisation (the transcription of data into a digital form), as it became possible to disseminate research findings more widely, quickly and cheaply.
Whilst there is widespread agreement that the transition to open access is essential in order to improve access to knowledge, there is a lack of consensus about the best route to achieve it. To achieve open access at scale in the UK, there will need to be a shift away from the dominant subscription-based business model. Inevitably, this will involve a transitional period and considerable change within the scholarly publishing market.
For the UK to transition to open access, an effective, functioning and competitive market in scholarly communications will be vital. The evidence we saw over the course of this inquiry shows that this is currently far from the case, with journal subscription prices rising at rates that are unsustainable for UK universities and other subscribers. There is a significant risk that the Government’s current open access policy will inadvertently encourage and prolong the dysfunctional elements of the scholarly publishing market, which are a major barrier to access.
See Volume I and Volume II “
From Networked Publics to Issue Publics: Reconsidering the Public/Private Distinction in Web Science
New paper by Andreas Birkbak: “As an increasing part of everyday life becomes connected with the web in many areas of the globe, the question of how the web mediates political processes becomes still more urgent. Several scholars have started to address this question by thinking about the web in terms of a public space. In this paper, we aim to make a twofold contribution towards the development of the concept of publics in web science. First, we propose that although the notion of publics raises a variety of issues, two major concerns continue to be user privacy and democratic citizenship on the web. Well-known arguments hold that the complex connectivity of the web puts user privacy at risk and enables the enclosure of public debate in virtual echo chambers. Our first argument is that these concerns are united by a set of assumptions coming from liberal political philosophy that are rarely made explicit. As a second contribution, this paper points towards an alternative way to think about publics by proposing a pragmatist reorientation of the public/private distinction in web science, away from seeing two spheres that needs to be kept separate, towards seeing the public and the private as something that is continuously connected. The theoretical argument is illustrated by reference to a recently published case study of Facebook groups, and future research agendas for the study of web-mediated publics are proposed.”
The Tech Intellectuals
New Essay by Henry Farrell in Democracy: “A quarter of a century ago, Russell Jacoby lamented the demise of the public intellectual. The cause of death was an improvement in material conditions. Public intellectuals—Dwight Macdonald, I.F. Stone, and their like—once had little choice but to be independent. They had difficulty getting permanent well-paying jobs. However, as universities began to expand, they offered new opportunities to erstwhile unemployables. The academy demanded a high price. Intellectuals had to turn away from the public and toward the practiced obscurities of academic research and prose. In Jacoby’s description, these intellectuals “no longer need[ed] or want[ed] a larger public…. Campuses [were] their homes; colleagues their audience; monographs and specialized journals their media.”
Over the last decade, conditions have changed again. New possibilities are opening up for public intellectuals. Internet-fueled media such as blogs have made it much easier for aspiring intellectuals to publish their opinions. They have fostered the creation of new intellectual outlets (Jacobin, The New Inquiry, The Los Angeles Review of Books), and helped revitalize some old ones too (The Baffler, Dissent). Finally, and not least, they have provided the meat for a new set of arguments about how communications technology is reshaping society.
These debates have created opportunities for an emergent breed of professional argument-crafters: technology intellectuals. Like their predecessors of the 1950s and ’60s, they often make a living without having to work for a university. Indeed, the professoriate is being left behind. Traditional academic disciplines (except for law, which has a magpie-like fascination with new and shiny things) have had a hard time keeping up. New technologies, to traditionalists, are suspect: They are difficult to pin down within traditional academic boundaries, and they look a little too fashionable to senior academics, who are often nervous that their fields might somehow become publicly relevant.
Many of these new public intellectuals are more or less self-made. Others are scholars (often with uncomfortable relationships with the academy, such as Clay Shirky, an unorthodox professor who is skeptical that the traditional university model can survive). Others still are entrepreneurs, like technology and media writer and podcaster Jeff Jarvis, working the angles between public argument and emerging business models….
Different incentives would lead to different debates. In a better world, technology intellectuals might think more seriously about the relationship between technological change and economic inequality. Many technology intellectuals think of the culture of Silicon Valley as inherently egalitarian, yet economist James Galbraith argues that income inequality in the United States “has been driven by capital gains and stock options, mostly in the tech sector.”
They might think more seriously about how technology is changing politics. Current debates are still dominated by pointless arguments between enthusiasts who believe the Internet is a model for a radically better democracy, and skeptics who claim it is the dictator’s best friend.
Finally, they might pay more attention to the burgeoning relationship between technology companies and the U.S. government. Technology intellectuals like to think that a powerful technology sector can enhance personal freedom and constrain the excesses of government. Instead, we are now seeing how a powerful technology sector may enable government excesses. Without big semi-monopolies like Facebook, Google, and Microsoft to hoover up personal information, surveillance would be far more difficult for the U.S. government.
Debating these issues would require a more diverse group of technology intellectuals. The current crop are not diverse in some immediately obvious ways—there are few women, few nonwhites, and few non-English speakers who have ascended to the peak of attention. Yet there is also far less intellectual diversity than there ought to be. The core assumptions of public debates over technology get less attention than they need and deserve.”