social media

From Crowd-Sourcing Potholes to Community Policing

Curated on September 19, 2013August 3, 2018 by Stefaan Verhulst

New paper by Manik Suri (GovLab): “The tragic Boston Marathon bombing and hair-raising manhunt that ensued was a sobering event. It also served as a reminder that emerging “civic technologies” – platforms and applications that enable citizens to connect and collaborate with each other and with government – are more important today than ever before. As commentators have noted, local police and federal agents utilized a range of technological platforms to tap the “wisdom of the crowd,” relying on thousands of private citizens to develop a “hive mind” that identified two suspects within a record period of time.
In the immediate wake of the devastating attack on April 15th, investigators had few leads. But within twenty-four hours, senior FBI officials, determined to seek “assistance from the public,” called on everyone with information to submit all media, tips, and leads related to the Boston Marathon attack. This unusual request for help yielded thousands of images and videos from local Bostonians, tourists, and private companies through technological channels ranging from telephone calls and emails to Flickr posts and Twitter messages. In mere hours, investigators were able to “crowd-source” a tremendous amount of data – including thousands of images from personal cameras, amateur videos from smart phones, and cell-tower information from private carriers. Combing through data from this massive network of “eyes and ears,” law enforcement officials were quickly able to generate images of two lead suspects – enabling a “modern manhunt” to commence immediately.
Technological innovations have transformed our commercial, political, and social realities. These advances include new approaches to how we generate knowledge, access information, and interact with one another, as well as new pathways for building social movements and catalyzing political change. While a significant body of academic research has focused on the role of technology in transforming electoral politics and social movements, less attention has been paid to how technological innovation can improve the process of governance itself.
A growing number of platforms and applications lie at this intersection of technology and governance, in what might be termed the “civic technology” sector. Broadly speaking, this sector involves the application of new information and communication technologies – ranging from robust social media platforms to state-of-the-art big data analysis systems – to address public policy problems. Civic technologies encompass enterprises that “bring web technologies directly to government, build services on top of government data for citizens, and change the way citizens ask, get, or need services from government.” These technologies have the potential to transform governance by promoting greater transparency in policy-making, increasing government efficiency, and enhancing citizens’ participation in public sector decision-making.“

Participatory Budgeting Around the World

Curated on September 18, 2013August 15, 2018 by Stefaan Verhulst

Jay Colburn, from the International Budget Partnership: “Public participation in budget decision making can occur in many different forms. Participatory budgeting (PB) is an increasingly popular process in which the public is involved directly in making budgetary decisions, most often at the local level. The involvement of community members usually includes identifying and prioritizing the community’s needs and then voting on spending for specific projects.
PB was first developed in Porto Alegre, Brazil, in 1989 as an innovative reform to address the city’s severe inequality. Since then it has spread around the world. Though the specifics of how the PB process works varies depending on the context in which it is implemented, most PB processes have four basic similarities: 1) community members identify spending ideas; 2) delegates are selected to develop spending proposals based on those ideas; 3) residents vote on which proposals to fund; and 4) the government implements the chosen proposals.
During the 1990s PB spread throughout Brazil and across Latin America. Examples of participatory budgeting can now be found in every region of the world, including Central Asia, Europe, and the Middle East. As the use of PB has expanded, it has been adapted in many ways. One example is to incorporate new information and communication technologies as a way to broaden opportunities for participation (see Using Technology to Improve Transparency and Citizen Engagement in this newsletter for more on this topic.)…
There are also a number of different models of PB that have been developed, each with slightly different rules and processes. Using the different models and methods has expanded our knowledge on the potential impacts of PB. In addition to having demonstrable and measurable results on mobilizing public funds for services for the poor, participatory budgeting has also been linked to greater tax compliance, increased demands for transparency, and greater access to budget information and oversight.
However, not all instances of PB are equally successful; there are many variables to consider when weighing the impact of different cases. These can include the level and mechanisms of participation, information accessibility, knowledge of opportunities to participate, political context, and prevailing socioeconomic factors. There is a large and growing literature on the benefits and challenges of PB. The IBP Open Budgets Blog recently featured posts on participatory budgeting initiatives in Peru, Kyrgyzstan, and Kenya. While there are still many lessons to be learned about how PB can be used in different contexts, it is certainly a positive step toward increased citizen engagement in the budget process and influence over how public funds are spent.
For more information and resources on PB, visit the participatory budgeting Facebook group”

(Appropriate) Big Data for Climate Resilience?

Curated on September 13, 2013August 15, 2018 by Stefaan Verhulst

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…

Curated on September 13, 2013October 10, 2018 by Stefaan Verhulst

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 21^st 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 19^th 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.”

The Tech Intellectuals

Curated on September 11, 2013August 3, 2018 by Stefaan Verhulst

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.”