Sharing Data Is a Form of Corporate Philanthropy

Curated on Posted on by Stefaan Verhulst

Matt Stempeck in HBR Blog:  “Ever since the International Charter on Space and Major Disasters was signed in 1999, satellite companies like DMC International Imaging have had a clear protocol with which to provide valuable imagery to public actors in times of crisis. In a single week this February, DMCii tasked its fleet of satellites on flooding in the United Kingdom, fires in India, floods in Zimbabwe, and snow in South Korea. Official crisis response departments and relevant UN departments can request on-demand access to the visuals captured by these “eyes in the sky” to better assess damage and coordinate relief efforts.

DMCii is a private company, yet it provides enormous value to the public and social sectors simply by periodically sharing its data.
Back on Earth, companies create, collect, and mine data in their day-to-day business. This data has quickly emerged as one of this century’s most vital assets. Public sector and social good organizations may not have access to the same amount, quality, or frequency of data. This imbalance has inspired a new category of corporate giving foreshadowed by the 1999 Space Charter: data philanthropy.
The satellite imagery example is an area of obvious societal value, but data philanthropy holds even stronger potential closer to home, where a wide range of private companies could give back in meaningful ways by contributing data to public actors. Consider two promising contexts for data philanthropy: responsive cities and academic research.
The centralized institutions of the 20th century allowed for the most sophisticated economic and urban planning to date. But in recent decades, the information revolution has helped the private sector speed ahead in data aggregation, analysis, and applications. It’s well known that there’s enormous value in real-time usage of data in the private sector, but there are similarly huge gains to be won in the application of real-time data to mitigate common challenges.
What if sharing economy companies shared their real-time housing, transit, and economic data with city governments or public interest groups? For example, Uber maintains a “God’s Eye view” of every driver on the road in a city:
stempeck2
Imagine combining this single data feed with an entire portfolio of real-time information. An early leader in this space is the City of Chicago’s urban data dashboard, WindyGrid. The dashboard aggregates an ever-growing variety of public datasets to allow for more intelligent urban management.
stempeck3
Over time, we could design responsive cities that react to this data. A responsive city is one where services, infrastructure, and even policies can flexibly respond to the rhythms of its denizens in real-time. Private sector data contributions could greatly accelerate these nascent efforts.
Data philanthropy could similarly benefit academia. Access to data remains an unfortunate barrier to entry for many researchers. The result is that only researchers with access to certain data, such as full-volume social media streams, can analyze and produce knowledge from this compelling information. Twitter, for example, sells access to a range of real-time APIs to marketing platforms, but the price point often exceeds researchers’ budgets. To accelerate the pursuit of knowledge, Twitter has piloted a program called Data Grants offering access to segments of their real-time global trove to select groups of researchers. With this program, academics and other researchers can apply to receive access to relevant bulk data downloads, such as an period of time before and after an election, or a certain geographic area.
Humanitarian response, urban planning, and academia are just three sectors within which private data can be donated to improve the public condition. There are many more possible applications possible, but few examples to date. For companies looking to expand their corporate social responsibility initiatives, sharing data should be part of the conversation…
Companies considering data philanthropy can take the following steps:

  • Inventory the information your company produces, collects, and analyzes. Consider which data would be easy to share and which data will require long-term effort.
  • Think who could benefit from this information. Who in your community doesn’t have access to this information?
  • Who could be harmed by the release of this data? If the datasets are about people, have they consented to its release? (i.e. don’t pull a Facebook emotional manipulation experiment).
  • Begin conversations with relevant public agencies and nonprofit partners to get a sense of the sort of information they might find valuable and their capacity to work with the formats you might eventually make available.
  • If you expect an onslaught of interest, an application process can help qualify partnership opportunities to maximize positive impact relative to time invested in the program.
  • Consider how you’ll handle distribution of the data to partners. Even if you don’t have the resources to set up an API, regular releases of bulk data could still provide enormous value to organizations used to relying on less-frequently updated government indices.
  • Consider your needs regarding privacy and anonymization. Strip the data of anything remotely resembling personally identifiable information (here are some guidelines).
  • If you’re making data available to researchers, plan to allow researchers to publish their results without obstruction. You might also require them to share the findings with the world under Open Access terms….”

Government, Foundations Turn to Cash Prizes to Generate Solutions

Curated on Posted on by Stefaan Verhulst

Megan O’Neil at the Chronicle of Philanthropy: “Government agencies and philanthropic organizations are increasingly staging competitions as a way generate interest in solving difficult technological, social, and environmental problems, according to a new report.
“The Craft of Prize Design: Lessons From the Public Sector” found that well-designed competitions backed by cash incentives can help organizations attract new ideas, mobilize action, and stimulate markets.
“Incentive prizes have transformed from an exotic open innovation to a proven innovation strategy for the public, private, and philanthropic sectors,” the report says.
Produced by Deloitte Consulting’s innovation practice, the report was financially supported by Bloomberg Philanthropies and the Case; Joyce; John S. and James L. Knight; Kresge; and Rockefeller foundations.
The federal government has staged more than 350 prize competitions during the past five years to stimulate innovation and crowdsource solutions, according to the report. And philanthropic organizations are also fronting prizes for competitions promoting innovative responses to questions such as how to strengthen communities and encourage sustainable energy consumption.
One example cited by the report is the Talent Dividend Prize, sponsored by CEOs for Cities and the Kresge Foundation, which awards $1-million to the city that most increases its college graduation rate during a four-year period. A second example is the MIT Clean Energy Prize, co-sponsored by the U.S. Department of Energy, which offered a total of $1 million in prize money. Submissions generated $85 million in capital and research grants, according to the report.
A prize-based project should not be adopted when an established approach to solve a problem already exists or if potential participants don’t have the interest or time to work on solving a problem, the report concludes. Instead, prize designers must gauge the capacity of potential participants before announcing a prize, and make sure that it will spur the discovery of new solutions.”

Lawsuit Would Force IRS to Release Nonprofit Tax Forms Digitally

Curated on Posted on by Stefaan Verhulst

Suzanne Perry at the Chronicle of Philanthropy on how “Open Data Could Shine a Light on Pay and Lobbying”: “Nonprofits that want to find out what their peers are doing can find a wealth of information in the forms the groups must file each year with the Internal Revenue Service—how much they pay their chief executives, how much they spend on fundraising, who is on their boards, where they offer services.
But the way the IRS makes those data available harkens to the digital dark ages, and critics who want to overhaul the system have been shaking up the generally polite nonprofit world with legal challenges, charges of monopoly, and talk of “disrupting” the status quo.
The issue will take center stage in a courtroom this week when a federal district judge in San Francisco is scheduled to consider arguments about whether to approve the IRS’s move to dismiss a lawsuit filed by an open-records group.
The group wants to obtain some specific Forms 990s, the informational tax documents filed by nonprofits, in a format that can be read by computers.
In theory, that shouldn’t be difficult since the nine nonprofits involved— including the American National Standards Institute, the New Horizons Foundation, and the International Code Council—submitted the forms electronically. But the IRS converts all 990s, no matter how they were filed, into images, rendering them useless for digital operations like searching multiple forms for information­.
That means watchdog groups and those that provide information on charities, like Charity Navigator, GuideStar, and the Urban Institute, have to spend money to manually enter the data they get from the IRS before making it available to the public, even if it has previously been digitized.
The lawsuit against the IRS, filed by Public.Resource.Org, aims to end that practice.
Carl Malamud, who heads the group, is a longtime activist who successfully pushed the Securities and Exchange Commission to post corporate filings free online in the 1990s, among other projects.
He wants to do the same with the IRS, arguing that data should be readily available at no cost about a sector that represents more than 1.5 million tax-exempt organizations and more than $1.5-trillion in revenue.

Why Statistically Significant Studies Aren’t Necessarily Significant

Curated on Posted on by Stefaan Verhulst

Michael White in PSMagazine on how modern statistics have made it easier than ever for us to fool ourselves: “Scientific results often defy common sense. Sometimes this is because science deals with phenomena that occur on scales we don’t experience directly, like evolution over billions of years or molecules that span billionths of meters. Even when it comes to things that happen on scales we’re familiar with, scientists often draw counter-intuitive conclusions from subtle patterns in the data. Because these patterns are not obvious, researchers rely on statistics to distinguish the signal from the noise. Without the aid of statistics, it would be difficult to convincingly show that smoking causes cancer, that drugged bees can still find their way home, that hurricanes with female names are deadlier than ones with male names, or that some people have a precognitive sense for porn.
OK, very few scientists accept the existence of precognition. But Cornell psychologist Daryl Bem’s widely reported porn precognition study illustrates the thorny relationship between science, statistics, and common sense. While many criticisms were leveled against Bem’s study, in the end it became clear that the study did not suffer from an obvious killer flaw. If it hadn’t dealt with the paranormal, it’s unlikely that Bem’s work would have drawn much criticism. As one psychologist put it after explaining how the study went wrong, “I think Bem’s actually been relatively careful. The thing to remember is that this type of fudging isn’t unusual; to the contrary, it’s rampant–everyone does it. And that’s because it’s very difficult, and often outright impossible, to avoid.”…
That you can lie with statistics is well known; what is less commonly noted is how much scientists still struggle to define proper statistical procedures for handling the noisy data we collect in the real world. In an exchange published last month in the Proceedings of the National Academy of Sciences, statisticians argued over how to address the problem of false positive results, statistically significant findings that on further investigation don’t hold up. Non-reproducible results in science are a growing concern; so do researchers need to change their approach to statistics?
Valen Johnson, at Texas A&M University, argued that the commonly used threshold for statistical significance isn’t as stringent as scientists think it is, and therefore researchers should adopt a tighter threshold to better filter out spurious results. In reply, statisticians Andrew Gelman and Christian Robert argued that tighter thresholds won’t solve the problem; they simply “dodge the essential nature of any such rule, which is that it expresses a tradeoff between the risks of publishing misleading results and of important results being left unpublished.” The acceptable level of statistical significance should vary with the nature of the study. Another team of statisticians raised a similar point, arguing that a more stringent significance threshold would exacerbate the worrying publishing bias against negative results. Ultimately, good statistical decision making “depends on the magnitude of effects, the plausibility of scientific explanations of the mechanism, and the reproducibility of the findings by others.”
However, arguments over statistics usually occur because it is not always obvious how to make good statistical decisions. Some bad decisions are clear. As xkcd’s Randall Munroe illustrated in his comic on the spurious link between green jelly beans and acne, most people understand that if you keep testing slightly different versions of a hypothesis on the same set of data, sooner or later you’re likely to get a statistically significant result just by chance. This kind of statistical malpractice is called fishing or p-hacking, and most scientists know how to avoid it.
But there are more subtle forms of the problem that pervade the scientific literature. In an unpublished paper (PDF), statisticians Andrew Gelman, at Columbia University, and Eric Loken, at Penn State, argue that researchers who deliberately avoid p-hacking still unknowingly engage in a similar practice. The problem is that one scientific hypothesis can be translated into many different statistical hypotheses, with many chances for a spuriously significant result. After looking at their data, researchers decide which statistical hypothesis to test, but that decision is skewed by the data itself.
To see how this might happen, imagine a study designed to test the idea that green jellybeans cause acne. There are many ways the results could come out statistically significant in favor of the researchers’ hypothesis. Green jellybeans could cause acne in men, but not in women, or in women but not men. The results may be statistically significant if the jellybeans you call “green” include Lemon Lime, Kiwi, and Margarita but not Sour Apple. Gelman and Loken write that “researchers can perform a reasonable analysis given their assumptions and their data, but had the data turned out differently, they could have done other analyses that were just as reasonable in those circumstances.” In the end, the researchers may explicitly test only one or a few statistical hypotheses, but their decision-making process has already biased them toward the hypotheses most likely to be supported by their data. The result is “a sort of machine for producing and publicizing random patterns.”
Gelman and Loken are not alone in their concern. Last year Daniele Fanelli, at the University of Edingburgh, and John Ioannidis, at Stanford University, reported that many U.S. studies, particularly in the social sciences, may overestimate the effect sizes of their results. “All scientists have to make choices throughout a research project, from formulating the question to submitting results for publication.” These choices can be swayed “consciously or unconsciously, by scientists’ own beliefs, expectations, and wishes, and the most basic scientific desire is that of producing an important research finding.”
What is the solution? Part of the answer is to not let measures of statistical significance override our common sense—not our naïve common sense, but our scientifically-informed common sense…”

Selected Readings on Crowdsourcing Tasks and Peer Production

Curated on Posted on by Andrew Young

The Living Library’s Selected Readings series seeks to build a knowledge base on innovative approaches for improving the effectiveness and legitimacy of governance. This curated and annotated collection of recommended works on the topic of crowdsourcing was originally published in 2014.

Technological advances are creating a new paradigm by which institutions and organizations are increasingly outsourcing tasks to an open community, allocating specific needs to a flexible, willing and dispersed workforce. “Microtasking” platforms like Amazon’s Mechanical Turk are a burgeoning source of income for individuals who contribute their time, skills and knowledge on a per-task basis. In parallel, citizen science projects – task-based initiatives in which citizens of any background can help contribute to scientific research – like Galaxy Zoo are demonstrating the ability of lay and expert citizens alike to make small, useful contributions to aid large, complex undertakings. As governing institutions seek to do more with less, looking to the success of citizen science and microtasking initiatives could provide a blueprint for engaging citizens to help accomplish difficult, time-consuming objectives at little cost. Moreover, the incredible success of peer-production projects – best exemplified by Wikipedia – instills optimism regarding the public’s willingness and ability to complete relatively small tasks that feed into a greater whole and benefit the public good. You can learn more about this new wave of “collective intelligence” by following the MIT Center for Collective Intelligence and their annual Collective Intelligence Conference.

Selected Reading List (in alphabetical order)

Annotated Selected Reading List (in alphabetical order)

Benkler, Yochai. The Wealth of Networks: How Social Production Transforms Markets and Freedom. Yale University Press, 2006. http://bit.ly/1aaU7Yb.

  • In this book, Benkler “describes how patterns of information, knowledge, and cultural production are changing – and shows that the way information and knowledge are made available can either limit or enlarge the ways people can create and express themselves.”
  • In his discussion on Wikipedia – one of many paradigmatic examples of people collaborating without financial reward – he calls attention to the notable ongoing cooperation taking place among a diversity of individuals. He argues that, “The important point is that Wikipedia requires not only mechanical cooperation among people, but a commitment to a particular style of writing and describing concepts that is far from intuitive or natural to people. It requires self-discipline. It enforces the behavior it requires primarily through appeal to the common enterprise that the participants are engaged in…”

Brabham, Daren C. Using Crowdsourcing in Government. Collaborating Across Boundaries Series. IBM Center for The Business of Government, 2013. http://bit.ly/17gzBTA.

  • In this report, Brabham categorizes government crowdsourcing cases into a “four-part, problem-based typology, encouraging government leaders and public administrators to consider these open problem-solving techniques as a way to engage the public and tackle difficult policy and administrative tasks more effectively and efficiently using online communities.”
  • The proposed four-part typology describes the following types of crowdsourcing in government:
    • Knowledge Discovery and Management
    • Distributed Human Intelligence Tasking
    • Broadcast Search
    • Peer-Vetted Creative Production
  • In his discussion on Distributed Human Intelligence Tasking, Brabham argues that Amazon’s Mechanical Turk and other microtasking platforms could be useful in a number of governance scenarios, including:
    • Governments and scholars transcribing historical document scans
    • Public health departments translating health campaign materials into foreign languages to benefit constituents who do not speak the native language
    • Governments translating tax documents, school enrollment and immunization brochures, and other important materials into minority languages
    • Helping governments predict citizens’ behavior, “such as for predicting their use of public transit or other services or for predicting behaviors that could inform public health practitioners and environmental policy makers”

Boudreau, Kevin J., Patrick Gaule, Karim Lakhani, Christoph Reidl, Anita Williams Woolley. “From Crowds to Collaborators: Initiating Effort & Catalyzing Interactions Among Online Creative Workers.” Harvard Business School Technology & Operations Mgt. Unit Working Paper No. 14-060. January 23, 2014. https://bit.ly/2QVmGUu.

  • In this working paper, the authors explore the “conditions necessary for eliciting effort from those affecting the quality of interdependent teamwork” and “consider the the role of incentives versus social processes in catalyzing collaboration.”
  • The paper’s findings are based on an experiment involving 260 individuals randomly assigned to 52 teams working toward solutions to a complex problem.
  • The authors determined the level of effort in such collaborative undertakings are sensitive to cash incentives. However, collaboration among teams was driven more by the active participation of teammates, rather than any monetary reward.

Franzoni, Chiara, and Henry Sauermann. “Crowd Science: The Organization of Scientific Research in Open Collaborative Projects.” Research Policy (August 14, 2013). http://bit.ly/HihFyj.

  • In this paper, the authors explore the concept of crowd science, which they define based on two important features: “participation in a project is open to a wide base of potential contributors, and intermediate inputs such as data or problem solving algorithms are made openly available.” The rationale for their study and conceptual framework is the “growing attention from the scientific community, but also policy makers, funding agencies and managers who seek to evaluate its potential benefits and challenges. Based on the experiences of early crowd science projects, the opportunities are considerable.”
  • Based on the study of a number of crowd science projects – including governance-related initiatives like Patients Like Me – the authors identify a number of potential benefits in the following categories:
    • Knowledge-related benefits
    • Benefits from open participation
    • Benefits from the open disclosure of intermediate inputs
    • Motivational benefits
  • The authors also identify a number of challenges:
    • Organizational challenges
    • Matching projects and people
    • Division of labor and integration of contributions
    • Project leadership
    • Motivational challenges
    • Sustaining contributor involvement
    • Supporting a broader set of motivations
    • Reconciling conflicting motivations

Kittur, Aniket, Ed H. Chi, and Bongwon Suh. “Crowdsourcing User Studies with Mechanical Turk.” In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 453–456. CHI ’08. New York, NY, USA: ACM, 2008. http://bit.ly/1a3Op48.

  • In this paper, the authors examine “[m]icro-task markets, such as Amazon’s Mechanical Turk, [which] offer a potential paradigm for engaging a large number of users for low time and monetary costs. [They] investigate the utility of a micro-task market for collecting user measurements, and discuss design considerations for developing remote micro user evaluation tasks.”
  • The authors conclude that in addition to providing a means for crowdsourcing small, clearly defined, often non-skill-intensive tasks, “Micro-task markets such as Amazon’s Mechanical Turk are promising platforms for conducting a variety of user study tasks, ranging from surveys to rapid prototyping to quantitative measures. Hundreds of users can be recruited for highly interactive tasks for marginal costs within a timeframe of days or even minutes. However, special care must be taken in the design of the task, especially for user measurements that are subjective or qualitative.”

Kittur, Aniket, Jeffrey V. Nickerson, Michael S. Bernstein, Elizabeth M. Gerber, Aaron Shaw, John Zimmerman, Matthew Lease, and John J. Horton. “The Future of Crowd Work.” In 16th ACM Conference on Computer Supported Cooperative Work (CSCW 2013), 2012. http://bit.ly/1c1GJD3.

  • In this paper, the authors discuss paid crowd work, which “offers remarkable opportunities for improving productivity, social mobility, and the global economy by engaging a geographically distributed workforce to complete complex tasks on demand and at scale.” However, they caution that, “it is also possible that crowd work will fail to achieve its potential, focusing on assembly-line piecework.”
  • The authors argue that seven key challenges must be met to ensure that crowd work processes evolve and reach their full potential:
    • Designing workflows
    • Assigning tasks
    • Supporting hierarchical structure
    • Enabling real-time crowd work
    • Supporting synchronous collaboration
    • Controlling quality

Madison, Michael J. “Commons at the Intersection of Peer Production, Citizen Science, and Big Data: Galaxy Zoo.” In Convening Cultural Commons, 2013. http://bit.ly/1ih9Xzm.

  • This paper explores a “case of commons governance grounded in research in modern astronomy. The case, Galaxy Zoo, is a leading example of at least three different contemporary phenomena. In the first place, Galaxy Zoo is a global citizen science project, in which volunteer non-scientists have been recruited to participate in large-scale data analysis on the Internet. In the second place, Galaxy Zoo is a highly successful example of peer production, some times known as crowdsourcing…In the third place, is a highly visible example of data-intensive science, sometimes referred to as e-science or Big Data science, by which scientific researchers develop methods to grapple with the massive volumes of digital data now available to them via modern sensing and imaging technologies.”
  • Madison concludes that the success of Galaxy Zoo has not been the result of the “character of its information resources (scientific data) and rules regarding their usage,” but rather, the fact that the “community was guided from the outset by a vision of a specific organizational solution to a specific research problem in astronomy, initiated and governed, over time, by professional astronomers in collaboration with their expanding universe of volunteers.”

Malone, Thomas W., Robert Laubacher and Chrysanthos Dellarocas. “Harnessing Crowds: Mapping the Genome of Collective Intelligence.” MIT Sloan Research Paper. February 3, 2009. https://bit.ly/2SPjxTP.

  • In this article, the authors describe and map the phenomenon of collective intelligence – also referred to as “radical decentralization, crowd-sourcing, wisdom of crowds, peer production, and wikinomics – which they broadly define as “groups of individuals doing things collectively that seem intelligent.”
  • The article is derived from the authors’ work at MIT’s Center for Collective Intelligence, where they gathered nearly 250 examples of Web-enabled collective intelligence. To map the building blocks or “genes” of collective intelligence, the authors used two pairs of related questions:
    • Who is performing the task? Why are they doing it?
    • What is being accomplished? How is it being done?
  • The authors concede that much work remains to be done “to identify all the different genes for collective intelligence, the conditions under which these genes are useful, and the constraints governing how they can be combined,” but they believe that their framework provides a useful start and gives managers and other institutional decisionmakers looking to take advantage of collective intelligence activities the ability to “systematically consider many possible combinations of answers to questions about Who, Why, What, and How.”

Mulgan, Geoff. “True Collective Intelligence? A Sketch of a Possible New Field.” Philosophy & Technology 27, no. 1. March 2014. http://bit.ly/1p3YSdd.

  • In this paper, Mulgan explores the concept of a collective intelligence, a “much talked about but…very underdeveloped” field.
  • With a particular focus on health knowledge, Mulgan “sets out some of the potential theoretical building blocks, suggests an experimental and research agenda, shows how it could be analysed within an organisation or business sector and points to possible intellectual barriers to progress.”
  • He concludes that the “central message that comes from observing real intelligence is that intelligence has to be for something,” and that “turning this simple insight – the stuff of so many science fiction stories – into new theories, new technologies and new applications looks set to be one of the most exciting prospects of the next few years and may help give shape to a new discipline that helps us to be collectively intelligent about our own collective intelligence.”

Sauermann, Henry and Chiara Franzoni. “Participation Dynamics in Crowd-Based Knowledge Production: The Scope and Sustainability of Interest-Based Motivation.” SSRN Working Papers Series. November 28, 2013. http://bit.ly/1o6YB7f.

  • In this paper, Sauremann and Franzoni explore the issue of interest-based motivation in crowd-based knowledge production – in particular the use of the crowd science platform Zooniverse – by drawing on “research in psychology to discuss important static and dynamic features of interest and deriv[ing] a number of research questions.”
  • The authors find that interest-based motivation is often tied to a “particular object (e.g., task, project, topic)” not based on a “general trait of the person or a general characteristic of the object.” As such, they find that “most members of the installed base of users on the platform do not sign up for multiple projects, and most of those who try out a project do not return.”
  • They conclude that “interest can be a powerful motivator of individuals’ contributions to crowd-based knowledge production…However, both the scope and sustainability of this interest appear to be rather limited for the large majority of contributors…At the same time, some individuals show a strong and more enduring interest to participate both within and across projects, and these contributors are ultimately responsible for much of what crowd science projects are able to accomplish.”

Schmitt-Sands, Catherine E. and Richard J. Smith. “Prospects for Online Crowdsourcing of Social Science Research Tasks: A Case Study Using Amazon Mechanical Turk.” SSRN Working Papers Series. January 9, 2014. http://bit.ly/1ugaYja.

  • In this paper, the authors describe an experiment involving the nascent use of Amazon’s Mechanical Turk as a social science research tool. “While researchers have used crowdsourcing to find research subjects or classify texts, [they] used Mechanical Turk to conduct a policy scan of local government websites.”
  • Schmitt-Sands and Smith found that “crowdsourcing worked well for conducting an online policy program and scan.” The microtasked workers were helpful in screening out local governments that either did not have websites or did not have the types of policies and services for which the researchers were looking. However, “if the task is complicated such that it requires ongoing supervision, then crowdsourcing is not the best solution.”

Shirky, Clay. Here Comes Everybody: The Power of Organizing Without Organizations. New York: Penguin Press, 2008. https://bit.ly/2QysNif.

  • In this book, Shirky explores our current era in which, “For the first time in history, the tools for cooperating on a global scale are not solely in the hands of governments or institutions. The spread of the Internet and mobile phones are changing how people come together and get things done.”
  • Discussing Wikipedia’s “spontaneous division of labor,” Shirky argues that the process is like, “the process is more like creating a coral reef, the sum of millions of individual actions, than creating a car. And the key to creating those individual actions is to hand as much freedom as possible to the average user.”

Silvertown, Jonathan. “A New Dawn for Citizen Science.” Trends in Ecology & Evolution 24, no. 9 (September 2009): 467–471. http://bit.ly/1iha6CR.

  • This article discusses the move from “Science for the people,” a slogan adopted by activists in the 1970s to “’Science by the people,’ which is “a more inclusive aim, and is becoming a distinctly 21st century phenomenon.”
  • Silvertown identifies three factors that are responsible for the explosion of activity in citizen science, each of which could be similarly related to the crowdsourcing of skills by governing institutions:
    • “First is the existence of easily available technical tools for disseminating information about products and gathering data from the public.
    • A second factor driving the growth of citizen science is the increasing realisation among professional scientists that the public represent a free source of labour, skills, computational power and even finance.
    • Third, citizen science is likely to benefit from the condition that research funders such as the National Science Foundation in the USA and the Natural Environment Research Council in the UK now impose upon every grantholder to undertake project-related science outreach. This is outreach as a form of public accountability.”

Szkuta, Katarzyna, Roberto Pizzicannella, David Osimo. “Collaborative approaches to public sector innovation: A scoping study.” Telecommunications Policy. 2014. http://bit.ly/1oBg9GY.

  • In this article, the authors explore cases where government collaboratively delivers online public services, with a focus on success factors and “incentives for services providers, citizens as users and public administration.”
  • The authors focus on six types of collaborative governance projects:
    • Services initiated by government built on government data;
    • Services initiated by government and making use of citizens’ data;
    • Services initiated by civil society built on open government data;
    • Collaborative e-government services; and
    • Services run by civil society and based on citizen data.
  • The cases explored “are all designed in the way that effectively harnesses the citizens’ potential. Services susceptible to collaboration are those that require computing efforts, i.e. many non-complicated tasks (e.g. citizen science projects – Zooniverse) or citizens’ free time in general (e.g. time banks). Those services also profit from unique citizens’ skills and their propensity to share their competencies.”

OSTP’s Own Open Government Plan

Curated on Posted on by Stefaan Verhulst

Nick Sinai and Corinna Zarek: “The White House Office of Science and Technology Policy (OSTP) today released its 2014 Open Government Plan. The OSTP plan highlights three flagship efforts as well as the team’s ongoing work to embed the open government principles of transparency, participation, and collaboration into its activities.
OSTP advises the President on the effects of science and technology on domestic and international affairs. The work of the office includes policy efforts encompassing science, environment, energy, national security, technology, and innovation. This plan builds off of the 2010 and 2012 Open Government Plans, updating progress on past initiatives and adding new subject areas based on 2014 guidance.
Agencies began releasing biennial Open Government Plans in 2010, with direction from the 2009 Open Government Directive. These plans serve as a roadmap for agency openness efforts, explaining existing practices and announcing new endeavors to be completed over the coming two years. Agencies build these plans in consultation with civil society stakeholders and the general public. Open government is a vital component of the President’s Management Agenda and our overall effort to ensure the government is expanding economic growth and opportunity for all Americans.
OSTP’s 2014 flagship efforts include:

  • Access to Scientific Collections: OSTP is leading agencies in developing policies that will improve the management of and access to scientific collections that agencies own or support. Scientific collections are assemblies of physical objects that are valuable for research and education—including drilling cores from the ocean floor and glaciers, seeds, space rocks, cells, mineral samples, fossils, and more. Agency policies will help make scientific collections and information about scientific collections more transparent and accessible in the coming years.
  • We the Geeks: We the Geeks Google+ Hangouts feature informal conversations with experts to highlight the future of science, technology, and innovation in the United States. Participants can join the conversation on Twitter by using the hashtag #WeTheGeeks and asking questions of the presenters throughout the hangout.
  • “All Hands on Deck” on STEM Education: OSTP is helping lead President Obama’s commitment to an “all-hands-on-deck approach” to providing students with skills they need to excel in science, technology, engineering, and math (STEM). In support of this goal, OSTP is bringing together government, industry, non-profits, philanthropy, and others to expand STEM education engagement and awareness through events like the annual White House Science Fair and the upcoming White House Maker Faire.

OSTP looks forward to implementing the 2014 Open Government Plan over the coming two years to continue building on its strong tradition of transparency, participation, and collaboration—with and for the American people.”

Twitter releasing trove of user data to scientists for research

Curated on Posted on by Stefaan Verhulst

Joe Silver at ArsTechnica: “Twitter has a 200-million-strong and ever-growing user base that broadcasts 500 million updates daily. It has been lauded for its ability to unsettle repressive political regimes, bring much-needed accountability to corporations that mistreat their customers, and combat other societal ills (whether such characterizations are, in fact, accurate). Now, the company has taken aim at disrupting another important sphere of human society: the scientific research community.
Back in February, the site announced its plan—in collaboration with Gnip—to provide a handful of research institutions with free access to its data sets from 2006 to the present. It’s a pilot program called “Twitter Data Grants,” with the hashtag #DataGrants. At the time, Twitter’s engineering blog explained the plan to enlist grant applications to access its treasure trove of user data:

Twitter has an expansive set of data from which we can glean insights and learn about a variety of topics, from health-related information such as when and where the flu may hit to global events like ringing in the new year. To date, it has been challenging for researchers outside the company who are tackling big questions to collaborate with us to access our public, historical data. Our Data Grants program aims to change that by connecting research institutions and academics with the data they need.

In April, Twitter announced that, after reviewing the more than 1,300 proposals submitted from more than 60 different countries, it had selected six institutions to provide with data access. Projects approved included a study of foodborne gastrointestinal illnesses, a study measuring happiness levels in cities based on images shared on Twitter, and a study using geosocial intelligence to model urban flooding in Jakarta, Indonesia. There’s even a project exploring the relationship between tweets and sports team performance.
Twitter did not directly respond to our questions on Tuesday afternoon regarding the specific amount and types of data the company is providing to the six institutions. But in its privacy policy, Twitter explains that most user information is intended to be broadcast widely. As a result, the company likely believes that sharing such information with scientific researchers is well within its rights, as its services “are primarily designed to help you share information with the world,” Twitter says. “Most of the information you provide us is information you are asking us to make public.”
While mining such data sets will undoubtedly aid scientists in conducting experiments for which similar data was previously either unavailable or quite limited, these applications raise some legal and ethical questions. For example, Scientific American has asked whether Twitter will be able to retain any legal rights to scientific findings and whether mining tweets (many of which are not publicly accessible) for scientific research when Twitter users have not agreed to such uses is ethically sound.
In response, computational epidemiologists Caitlin Rivers and Bryan Lewis have proposed guidelines for ethical research practices when using social media data, such as avoiding personally identifiable information and making all the results publicly available….”

Closing the Feedback Loop: Can Technology Bridge the Accountability Gap

Curated on Posted on by Stefaan Verhulst

(WorldBank) Book edited by Björn-Sören Gigler and Savita Bailur:  “This book is a collection of articles, written by both academics and practitioners as an evidence base for citizen engagement through information and communication technologies (ICTs). In it, the authors ask: how do ICTs empower through participation, transparency and accountability? Specifically, the authors examine two principal questions: Are technologies an accelerator to closing the “accountability gap” – the space between the supply (governments, service providers) and demand (citizens, communities, civil society organizations or CSOs) that requires bridging for open and collaborative governance? And under what conditions does this occur? The introductory chapters lay the theoretical groundwork for understanding the potential of technologies to achieving intended goals. Chapter 1 takes us through the theoretical linkages between empowerment, participation, transparency and accountability. In Chapter 2, the authors devise an informational capability framework, relating human abilities and well-being to the use of ICTs. The chapters to follow highlight practical examples that operationalize ICT-led initiatives. Chapter 3 reviews a sample of projects targeting the goals of transparency and accountability in governance to make preliminary conclusions around what evidence exists to date, and where to go from here. In chapter 4, the author reviews the process of interactive community mapping (ICM) with examples that support general local development and others that mitigate natural disasters. Chapter 5 examines crowdsourcing in fragile states to track aid flows, report on incitement or organize grassroots movements. In chapter 6, the author reviews Check My School (CMS), a community monitoring project in the Philippines designed to track the provision of services in public schools. Chapter 7 introduces four key ICT-led, citizen-governance initiatives in primary health care in Karnataka, India. Chapter 8 analyzes the World Bank Institute’s use of ICTs in expanding citizen project input to understand the extent to which technologies can either engender a new “feedback loop” or ameliorate a “broken loop”. The authors’ analysis of the evidence signals ICTs as an accelerator to closing the “accountability gap”. In Chapter 9, the authors conclude with the Loch Ness model to illustrate how technologies contribute to shrinking the gap, why the gap remains open in many cases, and what can be done to help close it. This collection is a critical addition to existing literature on ICTs and citizen engagement for two main reasons: first, it is expansive, covering initiatives that leverage a wide range of technology tools, from mobile phone reporting to crowdsourcing to interactive mapping; second, it is the first of its kind to offer concrete recommendations on how to close feedback loops.”

New crowdsourcing site like ‘Yelp’ for philanthropy

Curated on Posted on by Stefaan Verhulst

Vanessa Small in the Washington Post: “Billionaire investor Warren Buffett once said that there is no market test for philanthropy. Foundations with billions in assets often hand out giant grants to charity without critique. One watchdog group wants to change that.
The National Committee for Responsive Philanthropy has created a new Web site that posts public feedback about a foundation’s giving. Think Yelp for the philanthropy sector.
Along with public critiques, the new Web site, Philamplify.org, uploads a comprehensive assessment of a foundation conducted by researchers at the National Committee for Responsive Philanthropy.
The assessment includes a review of the foundation’s goals, strategies, partnerships with grantees, transparency, diversity in its board and how any investments support the mission.
The site also posts recommendations on what would make the foundation more effective in the community. The public can agree or disagree with each recommendation and then provide feedback about the grantmaker’s performance.
People who post to the site can remain anonymous.
NCRP officials hope the site will stir debate about the giving practices of foundations.
“Foundation leaders rarely get honest feedback because no one wants to get on the wrong side of a foundation,” said Lisa Ranghelli, a director at NCRP. “There’s so much we need to do as a society that we just want these philanthropic resources to be used as powerfully as possible and for everyone to feel like they have a voice in how philanthropy operates.”
With nonprofit rating sites such as Guidestar and Charity Navigator, Philamplify is just one more move to create more transparency in the nonprofit sector. But the site might be one of the first to force transparency and public commentary exclusively about the organizations that give grants.
Foundation leaders are open to the site, but say that some grantmakers already use various evaluation methods to improve their strategies.
Groups such as Grantmakers for Effective Organizations and the Center for Effective Philanthropy provide best practices for foundation giving.
The Council on Foundations, an Arlington-based membership organization of foundation groups, offers a list of tools and ideas for foundations to make their giving more effective.
“We will be paying close attention to Philamplify and new developments related to it as the project unfolds,” said Peter Panepento, senior vice president of community and knowledge at the Council on Foundations.
Currently there are three foundations up for review on the Web site: the William Penn Foundation in Philadelphia, which focuses on improving the Greater Philadelphia community; the Robert W. Woodruff Foundation in Atlanta, which gives grants in science and education; and the Lumina Foundation for Education in Indianapolis, which focuses on access to higher learning….”
Officials say Philamplify will focus on the top 100 largest foundations to start. Large foundations would include groups such as the Bill and Melinda Gates Foundation, the Robert Wood Johnson Foundation and Silicon Valley Community Foundation, and the foundations of companies such as Wal-Mart, Wells Fargo, Johnson & Johnson and GlaxoSmithKline.
Although there are concerns about the site’s ability to keep comments objective, grantees hope it will start a dialogue that has been absent in philanthropy.

The advent of crowdfunding innovations for development

Curated on Posted on by Stefaan Verhulst

SciDevNet: “FundaGeek, TechMoola and RocketHub have more in common than just their curious names. These are all the monikers of crowdsourcing websites that are dedicated to raising money for science and technology projects. As the coffers that were traditionally used to fund research and development have been squeezed in recent years, several such sites have sprouted up.
In 2013, general crowdsourcing site Kickstarter saw a total of US$480 million pledged to its projects by three million backers. That’s up from US$320 million in 2012, US$99 million in 2011 and just US$28million in 2010. Kickstarter expects the figures to climb further this year, and not just for popular projects such as films and books.
Science and technology projects — particularly those involving simple designs — are starting to make waves on these sites. And new sites, such as those bizarrely named ones, are now catering specifically for scientific projects, widening the choice of platforms on offer and raising crowdsourcing’s profile among the global scientific community online.
All this means that crowdsourcing is fast becoming one of the most significant innovations in funding the development of technology that can aid poor communities….
A good example of how crowdsourcing can help the developing world is the GravityLight, a product launched on Indiegogo over a year ago that uses gravity to create light. Not only did UK design company Therefore massively exceed its initial funding target — ultimately raising $US400,000 instead of a planned US$55,000 — it amassed a global network of investors and distributors that has allowed the light to be trialled in 26 countries as of last December.
The light was developed in-house after Therefore was given a brief to produce a cheap solar-powered lamp by private clients. Although this project faltered, the team independently set out to produce a lamp to replace the ubiquitous and dangerous kerosene lamps widely used in remote areas in Africa. After several months of development, Therefore had designed a product that is powered by a rope with a heavy weight on its end being slowly drawn through the light’s gears (see video)…
Crowdfunding is not always related to a specific product. Earlier this year, Indiegogo hosted a project hoping to build a clean energy store in a Ugandan village. The idea is to create an ongoing supply chain for technologies such as cleaner-burning stoves, water filters and solar lights that will improve or save lives, according to ENVenture, the project’s creators. [1] The US$2,000 target was comfortably exceeded…”