New report by McKinsey Global Institute:“Open data—machine-readable information, particularly government data, that’s made available to others—has generated a great deal of excitement around the world for its potential to empower citizens, change how government works, and improve the delivery of public services. It may also generate significant economic value, according to a new McKinsey report.1 Our research suggests that seven sectors alone could generate more than $3 trillion a year in additional value as a result of open data, which is already giving rise to hundreds of entrepreneurial businesses and helping established companies to segment markets, define new products and services, and improve the efficiency and effectiveness of operations.
Although the open-data phenomenon is in its early days, we see a clear potential to unlock significant economic value by applying advanced analytics to both open and proprietary knowledge. Open data can become an instrument for breaking down information gaps across industries, allowing companies to share benchmarks and spread best practices that raise productivity. Blended with proprietary data sets, it can propel innovation and help organizations replace traditional and intuitive decision-making approaches with data-driven ones. Open-data analytics can also help uncover consumer preferences, allowing companies to improve new products and to uncover anomalies and needless variations. That can lead to leaner, more reliable processes.
However, investments in technology and expertise are required to use the data effectively. And there is much work to be done by governments, companies, and consumers to craft policies that protect privacy and intellectual property, as well as establish standards to speed the flow of data that is not only open but also “liquid.” After all, consumers have serious privacy concerns, and companies are reluctant to share proprietary information—even when anonymity is assured—for fear of losing competitive advantage…
See also Executive Summary and Full Report”
New paper by Yochai Benkler, Aaron Shaw and Benjamin Mako Hill: “Peer production is the most significant organizational innovation that has emerged from
Internet-mediated social practice and among the most a visible and important examples of collective intelligence. Following Benkler, we define peer production as a form of open creation and sharing performed by groups online that: (1) sets and executes goals in a decentralized manner; (2) harnesses a diverse range of participant motivations, particularly non-monetary motivations; and (3) separates governance and management relations from exclusive forms of property and relational contracts (i.e., projects are governed as open commons or common property regimes and organizational governance utilizes combinations of participatory, meritocratic and charismatic, rather than proprietary or contractual, models). For early scholars of peer production, the phenomenon was both important and confounding for its ability to generate high quality work products in the absence of formal hierarchies and monetary incentives. However, as peer production has become increasingly established in society, the economy, and scholarship, merely describing the success of some peer production projects has become less useful. In recent years, a second wave of scholarship has emerged to challenge assumptions in earlier work; probe nuances glossed over by earlier framings of the phenomena; and identify the necessary dynamics, structures, and conditions for peer production success.
Peer production includes many of the largest and most important collaborative communities on the Internet….
Much of this academic interest in peer production stemmed from the fact that the phenomena resisted straightforward explanations in terms of extant theories of the organization and production of functional information goods like software or encyclopedias. Participants in peer production projects join and contribute valuable resources without the hierarchical bureaucracies or strong leadership structures common to state agencies or firms, and in the absence of clear financial incentives or rewards. As a result, foundationalresearch on peer production was focused on (1) documenting and explaining the organization and governance of peer production communities, (2) understanding the motivation of contributors to peer production, and (3) establishing and evaluating the quality of peer production’s outputs.
In the rest of this chapter, we describe the development of the academic literature on peer production in these three areas – organization, motivation, and quality.”
Article by Ines Mergel and Kevin C. Desouza in Public Administration Review: “As part of the Open Government Initiative, the Barack Obama administration has called for new forms of collaboration with stakeholders to increase the innovativeness of public service delivery. Federal managers are employing a new policy instrument called Challenge.gov to implement open innovation concepts invented in the private sector to crowdsource solutions from previously untapped problem solvers and to leverage collective intelligence to tackle complex social and technical public management problems. The authors highlight the work conducted by the Office of Citizen Services and Innovative Technologies at the General Services Administration, the administrator of the Challenge.gov platform. Specifically, this Administrative Profile features the work of Tammi Marcoullier, program manager for Challenge.gov, and Karen Trebon, deputy program manager, and their role as change agents who mediate collaborative practices between policy makers and public agencies as they navigate the political and legal environments of their local agencies. The profile provides insights into the implementation process of crowdsourcing solutions for public management problems, as well as lessons learned for designing open innovation processes in the public sector”.
Alexis Wichowski in The Atlantic: “Can the open-source model work for federal government? Not in every way—for security purposes, the government’s inner workings will never be completely open to the public. Even in the inner workings of government, fears of triggering the next Wikileaks or Snowden scandal may scare officials away from being more open with one another. While not every area of government can be more open, there are a few areas ripe for change.
Perhaps the most glaring need for an open-source approach is in information sharing. Today, among and within several federal agencies, a culture of reflexive and unnecessary information withholding prevails. This knee-jerk secrecy can backfire with fatal consequences, as seen in the 1998 embassy bombings in Africa, the 9/11 attacks, and the Boston Marathon bombings. What’s most troubling is that decades after the dangers of information-sharing were identified, the problem persists.
What’s preventing reform? The answer starts with the government’s hierarchical structure—though an information-is-power mentality and “need to know” Cold War-era culture contribute too. To improve the practice of information sharing, government needs to change the structure of information sharing. Specifically, it needs to flatten the hierarchy.
Former Obama Administration regulation czar Cass Sunstein’s “nudge” approach shows how this could work. In his book Simpler: The Future of Government, he describes how making even small changes to an environment can affect significant changes in behavior. While Sunstein focuses on regulations, the broader lesson is clear: Change the environment to encourage better behavior and people tend to exhibit better behavior. Without such strict adherence to the many tiers of the hierarchy, those working within it could be nudged towards, rather than fight to, share information.
One example of where this worked is in with the State Department’s annual Religious Engagement Report (RER). In 2011, the office in charge of the RER decided that instead of having every embassy submit their data via email, they would post it on a secure wiki. On the surface, this was a decision to change an information-sharing procedure. But it also changed the information-sharing culture. Instead of sharing information only along the supervisor-subordinate axis, it created a norm of sharing laterally, among colleagues.
Another advantage to flattening information-sharing hierarchies is that it reduces the risk of creating “single points of failure,” to quote technology scholar Beth Noveck. The massive amounts of data now available to us may need massive amounts of eyeballs in order to spot patterns of problems—small pools of supervisors atop the hierarchy cannot be expected to shoulder those burdens alone. And while having the right tech tools to share information is part of the solution—as the wiki made it possible for the RER—it’s not enough. Leadership must also create a culture that nudges their staff to use these tools, even if that means relinquishing a degree of their own power.
Finally, a more open work culture would help connect interested parties across government to let them share the hard work of bringing new ideas to fruition. Government is filled with examples of interesting new projects that stall in their infancy. Creating a large pool of collaborators dedicated to a project increases the likelihood that when one torchbearer burns out, others in the agency will pick up for them.
When Linus Torvalds released Linux, it was considered, in Raymond’s words, “subversive” and “a distinct shock.” Could the federal government withstand such a shock?
Evidence suggests it can—and the transformation is already happening in small ways. One of the winners of the Harvard Kennedy School’s Innovations in Government award is State’s Consular Team India (CTI), which won for joining their embassy and four consular posts—each of which used to have its own distinct set of procedures-into a single, more effective unit who could deliver standardized services. As CTI describes it, “this is no top-down bureaucracy” but shares “a common base of information and shared responsibilities.” They flattened the hierarchy, and not only lived, but thrived.”
Adam Kucharski in The Conversation: “When people talk about ‘big data’, there is an oft-quoted example: a proposed public health tool called Google Flu Trends. It has become something of a pin-up for the big data movement, but it might not be as effective as many claim.
The idea behind big data is that large amount of information can help us do things which smaller volumes cannot. Google first outlined the Flu Trends approach in a 2008 paper in the journal Nature. Rather than relying on disease surveillance used by the US Centers for Disease Control and Prevention (CDC) – such as visits to doctors and lab tests – the authors suggested it would be possible to predict epidemics through Google searches. When suffering from flu, many Americans will search for information related to their condition….
Between 2003 and 2008, flu epidemics in the US had been strongly seasonal, appearing each winter. However, in 2009, the first cases (as reported by the CDC) started in Easter. Flu Trends had already made its predictions when the CDC data was published, but it turned out that the Google model didn’t match reality. It had substantially underestimated the size of the initial outbreak.
The problem was that Flu Trends could only measure what people search for; it didn’t analyse why they were searching for those words. By removing human input, and letting the raw data do the work, the model had to make its predictions using only search queries from the previous handful of years. Although those 45 terms matched the regular seasonal outbreaks from 2003–8, they didn’t reflect the pandemic that appeared in 2009.
Six months after the pandemic started, Google – who now had the benefit of hindsight – updated their model so that it matched the 2009 CDC data. Despite these changes, the updated version of Flu Trends ran into difficulties again last winter, when it overestimated the size of the influenza epidemic in New York State. The incidents in 2009 and 2012 raised the question of how good Flu Trends is at predicting future epidemics, as opposed to merely finding patterns in past data.
In a new analysis, published in the journal PLOS Computational Biology, US researchers report that there are “substantial errors in Google Flu Trends estimates of influenza timing and intensity”. This is based on comparison of Google Flu Trends predictions and the actual epidemic data at the national, regional and local level between 2003 and 2013
Even when search behaviour was correlated with influenza cases, the model sometimes misestimated important public health metrics such as peak outbreak size and cumulative cases. The predictions were particularly wide of the mark in 2009 and 2012:
Original and updated Google Flu Trends (GFT) model compared with CDC influenza-like illness (ILI) data. PLOS Computational Biology 9:10
Click to enlarge
Although they criticised certain aspects of the Flu Trends model, the researchers think that monitoring internet search queries might yet prove valuable, especially if it were linked with other surveillance and prediction methods.
Other researchers have also suggested that other sources of digital data – from Twitter feeds to mobile phone GPS – have the potential to be useful tools for studying epidemics. As well as helping to analysing outbreaks, such methods could allow researchers to analyse human movement and the spread of public health information (or misinformation).
Although much attention has been given to web-based tools, there is another type of big data that is already having a huge impact on disease research. Genome sequencing is enabling researchers to piece together how diseases transmit and where they might come from. Sequence data can even reveal the existence of a new disease variant: earlier this week, researchers announced a new type of dengue fever virus….”
Dan Sokolov and David Cassidy at the Consumer Financial Protection Bureau (CFPB): “We write rules to protect consumers, but what actually protects consumers is people: advocates knowing what rights people have, government agencies’ supervision and enforcement staff having a clear view of what potential violations to look out for; and responsible industry employees following the rules.
Today, we’re releasing a new open source tool we built, eRegulations, to help make regulations easier to understand. Check it out: consumerfinance.gov/eregulations
One thing that’s become clear during our two years as an agency is that federal regulations can be difficult to navigate. Finding answers to questions about a regulation is hard. Frequently, it means connecting information from different places, spread throughout a regulation, often separated by dozens or even hundreds of pages. As a result, we found people were trying to understand regulations by using paper editions, several different online tools to piece together the relevant information, or even paid subscription services that still don’t make things easy, and are expensive.
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Here’s hoping that even more people who work with regulations will have the same reaction as this member of our bank supervision team: “The eRegulations site has been very helpful to my work. It has become my go-to resource on Reg. E and the Official Interpretations. I use it several times a week in the course of completing regulatory compliance evaluations. My prior preference was to use the printed book or e-CFR, but I’ve found the eRegulations (tool) to be easier to read, search, and navigate than the printed book, and more efficient than the e-CFR because of the way eRegs incorporates the commentary.”
New rules about international money transfers – also called “remittances” – in Regulation E will take effect on October 28, 2013, and you can now use the eRegulations tool to check out the regulation.
We need your help
There are two ways we’d love your help with our work to make regulations easier to use. First, the tool is a work in progress. If you have comments or suggestions, please write to us at [email protected]. We read every message and would love to hear what you think.
Second, the tool is open source, so we’d love for other agencies, developers, or groups to use it and adapt it. And remember, the first time a citizen developer suggested a change to our open source software, it was to fix a typo (thanks again, by the way!), so no contribution is too small.”
Sue Halpern in The New York Review of Books: “Also not obvious was how the Web would evolve, though its open architecture virtually assured that it would. The original Web, the Web of static homepages, documents laden with “hot links,” and electronic storefronts, segued into Web 2.0, which, by providing the means for people without technical knowledge to easily share information, recast the Internet as a global social forum with sites like Facebook, Twitter, FourSquare, and Instagram.
Once that happened, people began to make aspects of their private lives public, letting others know, for example, when they were shopping at H+M and dining at Olive Garden, letting others know what they thought of the selection at that particular branch of H+M and the waitstaff at that Olive Garden, then modeling their new jeans for all to see and sharing pictures of their antipasti and lobster ravioli—to say nothing of sharing pictures of their girlfriends, babies, and drunken classmates, or chronicling life as a high-paid escort, or worrying about skin lesions or seeking a cure for insomnia or rating professors, and on and on.
The social Web celebrated, rewarded, routinized, and normalized this kind of living out loud, all the while anesthetizing many of its participants. Although they likely knew that these disclosures were funding the new information economy, they didn’t especially care…
The assumption that decisions made by machines that have assessed reams of real-world information are more accurate than those made by people, with their foibles and prejudices, may be correct generally and wrong in the particular; and for those unfortunate souls who might never commit another crime even if the algorithm says they will, there is little recourse. In any case, computers are not “neutral”; algorithms reflect the biases of their creators, which is to say that prediction cedes an awful lot of power to the algorithm creators, who are human after all. Some of the time, too, proprietary algorithms, like the ones used by Google and Twitter and Facebook, are intentionally biased to produce results that benefit the company, not the user, and some of the time algorithms can be gamed. (There is an entire industry devoted to “optimizing” Google searches, for example.)
But the real bias inherent in algorithms is that they are, by nature, reductive. They are intended to sift through complicated, seemingly discrete information and make some sort of sense of it, which is the definition of reductive.”
Books reviewed:
Springwise: “City residents may not have degrees in urban planning, but their everyday use of high streets, parks and main roads means they have some valuable input into what’s best for their local environment. A new website called Streetmix is helping to empower citizens, enabling them to become architects with an easy-to-use street-building platform.
Developed by Code for America, the site greets users with a colorful cartoon representation of a typical street, split into segments of varying widths. Designers can then swap and change each piece into road, cycle paths, pedestrian areas, bus stops, bike racks and other amenities, as well as alter their dimensions. Users can create their own perfect high street or use the exact measurements of their own neighborhood to come up with new propositions for planned construction work. Indeed, Streetmix has already found use among residents and organizations to demonstrate how to better use the local space available. Kansas City’s Bike Walk KC has utilized the platform to show how new bike lanes could figure in an upcoming study of traffic flow in the region, while New Zealand’s Transport Blog has presented several alternatives to current street layouts in Auckland.
Streetmix is an easy-to-use visualization tool that can help amateurs present their ideas to local authorities in a more coherent way, potentially increasing the chances of politicians hearing calls for change. Are there other ways to help laymen express complex ideas more eloquently?” Spotted by Murtaza Patel, written by Springwise
Abhi Nemani, co-director of Code for America: “Be it the burden placed on them by shrinking federal support, or the opportunity presented by modern technology, 21st-century cities are finding new ways to do things. For four years, Code for America has worked with dozens of cities, each finding creative ways to solve neighborhood problems, build local capacity and steward a national network. These aren’t one-offs. Cities are championing fundamental, institutional reforms to commit to an ongoing innovation agenda.
Here are a few of the ways how:
…Create an office of new urban mechanics or appoint a chief innovation officer…
…Appoint a chief data officer or create an office of performance management/enhancement…
…Adopt the Gov.UK Design Principles, and require plain, human language on every interface….
…Share open source technology with a sister city or change procurement rules to make it easier to redeploy civic tech….
…Work with the local civic tech community and engage citizens for their feedback on city policy through events, tech and existing forums…
…Create an open data policy and adopt open data specifications…
…Attract tech talent into city leadership, and create training opportunities citywide to level up the tech literacy for city staff…”
Evgeny Morozov in MIT Technology Review: “Intellectually, at least, it’s clear what needs to be done: we must confront the question not only in the economic and legal dimensions but also in a political one, linking the future of privacy with the future of democracy in a way that refuses to reduce privacy either to markets or to laws. What does this philosophical insight mean in practice?
First, we must politicize the debate about privacy and information sharing. Articulating the existence—and the profound political consequences—of the invisible barbed wire would be a good start. We must scrutinize data-intensive problem solving and expose its occasionally antidemocratic character. At times we should accept more risk, imperfection, improvisation, and inefficiency in the name of keeping the democratic spirit alive.
Second, we must learn how to sabotage the system—perhaps by refusing to self-track at all. If refusing to record our calorie intake or our whereabouts is the only way to get policy makers to address the structural causes of problems like obesity or climate change—and not just tinker with their symptoms through nudging—information boycotts might be justifiable. Refusing to make money off your own data might be as political an act as refusing to drive a car or eat meat. Privacy can then reëmerge as a political instrument for keeping the spirit of democracy alive: we want private spaces because we still believe in our ability to reflect on what ails the world and find a way to fix it, and we’d rather not surrender this capacity to algorithms and feedback loops.
Third, we need more provocative digital services. It’s not enough for a website to prompt us to decide who should see our data. Instead it should reawaken our own imaginations. Designed right, sites would not nudge citizens to either guard or share their private information but would reveal the hidden political dimensions to various acts of information sharing. We don’t want an electronic butler—we want an electronic provocateur. Instead of yet another app that could tell us how much money we can save by monitoring our exercise routine, we need an app that can tell us how many people are likely to lose health insurance if the insurance industry has as much data as the NSA, most of it contributed by consumers like us. Eventually we might discern such dimensions on our own, without any technological prompts.
Finally, we have to abandon fixed preconceptions about how our digital services work and interconnect. Otherwise, we’ll fall victim to the same logic that has constrained the imagination of so many well-meaning privacy advocates who think that defending the “right to privacy”—not fighting to preserve democracy—is what should drive public policy. While many Internet activists would surely argue otherwise, what happens to the Internet is of only secondary importance. Just as with privacy, it’s the fate of democracy itself that should be our primary goal.
