Crowdsourcing Ideas to Accelerate Economic Growth and Prosperity through a Strategy for American Innovation

Curated on Posted on by Stefaan Verhulst

Jason Miller and Tom Kalil at the White House Blog: “America’s future economic growth and international competitiveness depend crucially on our capacity to innovate. Creating the jobs and industries of the future will require making the right investments to unleash the unmatched creativity and imagination of the American people.
We want to gather bold ideas for how we as a nation can build on and extend into the future our historic strengths in innovation and discovery. Today we are calling on thinkers, doers, and entrepreneurs across the country to submit their proposals for promising new initiatives or pressing needs for renewed investment to be included in next year’s updated Strategy for American Innovation.
What will the next Strategy for American Innovation accomplish? In part, it’s up to you. Your input will help guide the Administration’s efforts to catalyze the transformative innovation in products, processes, and services that is the hallmark of American ingenuity.
Today, we released a set of questions for your comment, which you can access here and on Quora – an online platform that allows us to crowdsource ideas from the American people.

  Calling on America’s Inventors and Innovators for Great Ideas
Among the questions we are posing today to innovators across the country are:

  • What specific policies or initiatives should the Administration consider prioritizing in the next version of the Strategy for American Innovation?
  • What are the biggest challenges to, and opportunities for, innovation in the United States that will generate long-term economic growth and rising standards of living for more Americans?
  • What additional opportunities exist to develop high-impact platform technologies that reduce the time and cost associated with the “design, build, test” cycle for important classes of materials, products, and systems?
  • What investments, strategies, or technological advancements, across both the public and private sectors, are needed to rebuild the U.S. “industrial commons” (i.e., regional manufacturing capabilities) and ensure the latest technologies can be produced here?
  • What partnerships or novel models for collaboration between the Federal Government and regions should the Administration consider in order to promote innovation and the development of regional innovation ecosystems?

 
In today’s world of rapidly evolving technology, the Administration is adapting its approach to innovation-driven economic growth to reflect the emergence of new and exciting possibilities. Now is the time to gather input from the American people in order to envision and shape the innovations of the future. The full Request for Information can be found here and the 2011 Strategy for American Innovation can be found here. Comments are due by September 23, 2014, and can be sent to innovationstrategy@ostp.gov.  We look forward to hearing your ideas!”

Indonesian techies crowdsource election results

Curated on Posted on by Stefaan Verhulst
Ben Bland in the Financial Times: “Three Indonesian tech experts say they have used crowdsourcing to calculate an accurate result for the country’s contested presidential election in six days, while 4m officials have been beavering away for nearly two weeks counting the votes by hand.

The Indonesian techies, who work for multinational companies, were spurred into action after both presidential candidates claimed victory and accused each other of trying to rig the convoluted counting process, raising fears that the country’s young democracy was under threat.

“We did this to prevent the nation being ripped apart because of two claims to victory that nobody can verify,” said Ainun Najib, who is based in Singapore. “This solution was only possible because all the polling station data were openly available for public scrutiny and verification.”

Mr Najib and two friends took advantage of the decision by the national election commission (KPU) to upload the individual results from Indonesia’s 480,000 polling stations to its website for the first time, in an attempt to counter widespread fears about electoral fraud.

The three Indonesians scraped the voting data from the KPU website on to a database and then recruited 700 friends and acquaintances through Facebook to type in the results and check them. They uploaded the data to a website called kawalpemilu.org, which means “guard the election” in Indonesian.

Throughout the process, Mr Najib said he had to fend off hacking attacks, forcing him to shift data storage to a cloud-based service. The whole exercise cost $10 for a domain name and $0.10 for the data storage….”

Brief survey of crowdsourcing for data mining

Curated on Posted on by Stefaan Verhulst

Paper by Guo Xintong,  Wang Hongzhi, Yangqiu Song, and Gao Hong in Expert Systems with Applications: “Crowdsourcing allows large-scale and flexible invocation of human input for data gathering and analysis, which introduces a new paradigm of data mining process. Traditional data mining methods often require the experts in analytic domains to annotate the data. However, it is expensive and usually takes a long time. Crowdsourcing enables the use of heterogeneous background knowledge from volunteers and distributes the annotation process to small portions of efforts from different contributions. This paper reviews the state-of-the-arts on the crowdsourcing for data mining in recent years. We first review the challenges and opportunities of data mining tasks using crowdsourcing, and summarize the framework of them. Then we highlight several exemplar works in each component of the framework, including question designing, data mining and quality control. Finally, we conclude the limitation of crowdsourcing for data mining and suggest related areas for future research.

Incentivizing Peer Review

Curated on Posted on by Stefaan Verhulst

in Wired on “The Last Obstacle for Open Access Science: The Galapagos Islands’ Charles Darwin Foundation runs on an annual operating budget of about $3.5 million. With this money, the center conducts conservation research, enacts species-saving interventions, and provides educational resources about the fragile island ecosystems. As a science-based enterprise whose work would benefit greatly from the latest research findings on ecological management, evolution, and invasive species, there’s one glaring hole in the Foundation’s budget: the $800,000 it would cost per year for subscriptions to leading academic journals.
According to Richard Price, founder and CEO of Academia.edu, this episode is symptomatic of a larger problem. “A lot of research centers” – NGOs, academic institutions in the developing world – “are just out in the cold as far as access to top journals is concerned,” says Price. “Research is being commoditized, and it’s just another aspect of the digital divide between the haves and have-nots.”
 
Academia.edu is a key player in the movement toward open access scientific publishing, with over 11 million participants who have uploaded nearly 3 million scientific papers to the site. It’s easy to understand Price’s frustration with the current model, in which academics donate their time to review articles, pay for the right to publish articles, and pay for access to articles. According to Price, journals charge an average of $4000 per article: $1500 for production costs (reformatting, designing), $1500 to orchestrate peer review (labor costs for hiring editors, administrators), and $1000 of profit.
“If there were no legacy in the scientific publishing industry, and we were looking at the best way to disseminate and view scientific results,” proposes Price, “things would look very different. Our vision is to build a complete replacement for scientific publishing,” one that would allow budget-constrained organizations like the CDF full access to information that directly impacts their work.
But getting to a sustainable new world order requires a thorough overhaul of academic publishing industry. The alternative vision – of “open science” – has two key properties: the uninhibited sharing of research findings, and a new peer review system that incorporates the best of the scientific community’s feedback. Several groups have made progress on the former, but the latter has proven particularly difficult given the current incentive structure. The currency of scientific research is the number of papers you’ve published and their citation counts – the number of times other researchers have referred to your work in their own publications. The emphasis is on creation of new knowledge – a worthy goal, to be sure – but substantial contributions to the quality, packaging, and contextualization of that knowledge in the form of peer review goes largely unrecognized. As a result, researchers view their role as reviewers as a chore, a time-consuming task required to sustain the ecosystem of research dissemination.
“Several experiments in this space have tried to incorporate online comment systems,” explains Price, “and the result is that putting a comment box online and expecting high quality comments to flood in is just unrealistic. My preference is to come up with a system where you’re just as motivated to share your feedback on a paper as you are to share your own findings.” In order to make this lofty aim a reality, reviewers’ contributions would need to be recognized. “You need something more nuanced, and more qualitative,” says Price. “For example, maybe you gather reputation points from your community online.” Translating such metrics into tangible benefits up the food chain – hirings, tenure decisions, awards – is a broader community shift that will no doubt take time.
A more iterative peer review process could allow the community to better police faulty methods by crowdsourcing their evaluation. “90% of scientific studies are not reproducible,” claims Price; a problem that is exacerbated by the strong bias toward positive results. Journals may be unlikely to publish methodological refutations, but a flurry of well-supported comments attached to a paper online could convince the researchers to marshal more convincing evidence. Typically, this sort of feedback cycle takes years….”

Crowdsourcing Parking Lot Occupancy using a Mobile Phone Application

Curated on Posted on by Stefaan Verhulst

Paper by Davami, Erfan; Sukthankar, Gita available at ASE@360: “Participatory sensing is a specialized form of crowdsourcing for mobile devices in which the users act as sensors to report on local environmental conditions. • This poster describes the process of prototyping a mobile phone crowdsourcing app for monitoring parking availability on a large university campus. • We present a case study of how an agent-based urban model can be used to perform a sensitivity analysis of the comparative susceptibility of different data fusion paradigms to potentially troublesome user behaviors: 1. Poor user enrollment, 2. Infrequent usage, 3. A preponderance of untrustworthy users.”

Selected Readings on Crowdsourcing Expertise

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.

Crowdsourcing enables leaders and citizens to work together to solve public problems in new and innovative ways. New tools and platforms enable citizens with differing levels of knowledge, expertise, experience and abilities to collaborate and solve problems together. Identifying experts, or individuals with specialized skills, knowledge or abilities with regard to a specific topic, and incentivizing their participation in crowdsourcing information, knowledge or experience to achieve a shared goal can enhance the efficiency and effectiveness of problem solving.

Selected Reading List (in alphabetical order)

Annotated Selected Reading List (in alphabetical order)

Börner, Katy, Michael Conlon, Jon Corson-Rikert, and Ying Ding. “VIVO: A Semantic Approach to Scholarly Networking and Discovery.” Synthesis Lectures on the Semantic Web: Theory and Technology 2, no. 1 (October 17, 2012): 1–178. http://bit.ly/17huggT.

  • This e-book “provides an introduction to VIVO…a tool for representing information about research and researchers — their scholarly works, research interests, and organizational relationships.”
  • VIVO is a response to the fact that, “Information for scholars — and about scholarly activity — has not kept pace with the increasing demands and expectations. Information remains siloed in legacy systems and behind various access controls that must be licensed or otherwise negotiated before access. Information representation is in its infancy. The raw material of scholarship — the data and information regarding previous work — is not available in common formats with common semantics.”
  • Providing access to structured information on the work and experience of a diversity of scholars enables improved expert finding — “identifying and engaging experts whose scholarly works is of value to one’s own. To find experts, one needs rich data regarding one’s own work and the work of potential related experts. The authors argue that expert finding is of increasing importance since, “[m]ulti-disciplinary and inter-disciplinary investigation is increasingly required to address complex problems. 

Bozzon, Alessandro, Marco Brambilla, Stefano Ceri, Matteo Silvestri, and Giuliano Vesci. “Choosing the Right Crowd: Expert Finding in Social Networks.” In Proceedings of the 16th International Conference on Extending Database Technology, 637–648. EDBT  ’13. New York, NY, USA: ACM, 2013. http://bit.ly/18QbtY5.

  • This paper explores the challenge of selecting experts within the population of social networks by considering the following problem: “given an expertise need (expressed for instance as a natural language query) and a set of social network members, who are the most knowledgeable people for addressing that need?”
  • The authors come to the following conclusions:
    • “profile information is generally less effective than information about resources that they directly create, own or annotate;
    • resources which are produced by others (resources appearing on the person’s Facebook wall or produced by people that she follows on Twitter) help increasing the assessment precision;
    • Twitter appears the most effective social network for expertise matching, as it very frequently outperforms all other social networks (either combined or alone);
    • Twitter appears as well very effective for matching expertise in domains such as computer engineering, science, sport, and technology & games, but Facebook is also very effective in fields such as locations, music, sport, and movies & tv;
    • surprisingly, LinkedIn appears less effective than other social networks in all domains (including computer science) and overall.”

Brabham, Daren C. “The Myth of Amateur Crowds.” Information, Communication & Society 15, no. 3 (2012): 394–410. http://bit.ly/1hdnGJV.

  • Unlike most of the related literature, this paper focuses on bringing attention to the expertise already being tapped by crowdsourcing efforts rather than determining ways to identify more dormant expertise to improve the results of crowdsourcing.
  • Brabham comes to two central conclusions: “(1) crowdsourcing is discussed in the popular press as a process driven by amateurs and hobbyists, yet empirical research on crowdsourcing indicates that crowds are largely self-selected professionals and experts who opt-in to crowdsourcing arrangements; and (2) the myth of the amateur in crowdsourcing ventures works to label crowds as mere hobbyists who see crowdsourcing ventures as opportunities for creative expression, as entertainment, or as opportunities to pass the time when bored. This amateur/hobbyist label then undermines the fact that large amounts of real work and expert knowledge are exerted by crowds for relatively little reward and to serve the profit motives of companies. 

Dutton, William H. Networking Distributed Public Expertise: Strategies for Citizen Sourcing Advice to Government. One of a Series of Occasional Papers in Science and Technology Policy, Science and Technology Policy Institute, Institute for Defense Analyses, February 23, 2011. http://bit.ly/1c1bpEB.

  • In this paper, a case is made for more structured and well-managed crowdsourcing efforts within government. Specifically, the paper “explains how collaborative networking can be used to harness the distributed expertise of citizens, as distinguished from citizen consultation, which seeks to engage citizens — each on an equal footing.” Instead of looking for answers from an undefined crowd, Dutton proposes “networking the public as advisors” by seeking to “involve experts on particular public issues and problems distributed anywhere in the world.”
  • Dutton argues that expert-based crowdsourcing can be successfully for government for a number of reasons:
    • Direct communication with a diversity of independent experts
    • The convening power of government
    • Compatibility with open government and open innovation
    • Synergy with citizen consultation
    • Building on experience with paid consultants
    • Speed and urgency
    • Centrality of documents to policy and practice.
  • He also proposes a nine-step process for government to foster bottom-up collaboration networks:
    • Do not reinvent the technology
    • Focus on activities, not the tools
    • Start small, but capable of scaling up
    • Modularize
    • Be open and flexible in finding and going to communities of experts
    • Do not concentrate on one approach to all problems
    • Cultivate the bottom-up development of multiple projects
    • Experience networking and collaborating — be a networked individual
    • Capture, reward, and publicize success.

Goel, Gagan, Afshin Nikzad and Adish Singla. “Matching Workers with Tasks: Incentives in Heterogeneous Crowdsourcing Markets.” Under review by the International World Wide Web Conference (WWW). 2014. http://bit.ly/1qHBkdf

  • Combining the notions of crowdsourcing expertise and crowdsourcing tasks, this paper focuses on the challenge within platforms like Mechanical Turk related to intelligently matching tasks to workers.
  • The authors’ call for more strategic assignment of tasks in crowdsourcing markets is based on the understanding that “each worker has certain expertise and interests which define the set of tasks she can and is willing to do.”
  • Focusing on developing meaningful incentives based on varying levels of expertise, the authors sought to create a mechanism that, “i) is incentive compatible in the sense that it is truthful for agents to report their true cost, ii) picks a set of workers and assigns them to the tasks they are eligible for in order to maximize the utility of the requester, iii) makes sure total payments made to the workers doesn’t exceed the budget of the requester.

Gubanov, D., N. Korgin, D. Novikov and A. Kalkov. E-Expertise: Modern Collective Intelligence. Springer, Studies in Computational Intelligence 558, 2014. http://bit.ly/U1sxX7

  • In this book, the authors focus on “organization and mechanisms of expert decision-making support using modern information and communication technologies, as well as information analysis and collective intelligence technologies (electronic expertise or simply e-expertise).”
  • The book, which “addresses a wide range of readers interested in management, decision-making and expert activity in political, economic, social and industrial spheres, is broken into five chapters:
    • Chapter 1 (E-Expertise) discusses the role of e-expertise in decision-making processes. The procedures of e-expertise are classified, their benefits and shortcomings are identified, and the efficiency conditions are considered.
    • Chapter 2 (Expert Technologies and Principles) provides a comprehensive overview of modern expert technologies. A special emphasis is placed on the specifics of e-expertise. Moreover, the authors study the feasibility and reasonability of employing well-known methods and approaches in e-expertise.
    • Chapter 3 (E-Expertise: Organization and Technologies) describes some examples of up-to-date technologies to perform e-expertise.
    • Chapter 4 (Trust Networks and Competence Networks) deals with the problems of expert finding and grouping by information and communication technologies.
    • Chapter 5 (Active Expertise) treats the problem of expertise stability against any strategic manipulation by experts or coordinators pursuing individual goals.

Holst, Cathrine. “Expertise and Democracy.” ARENA Report No 1/14, Center for European Studies, University of Oslo. http://bit.ly/1nm3rh4

  • This report contains a set of 16 papers focused on the concept of “epistocracy,” meaning the “rule of knowers.” The papers inquire into the role of knowledge and expertise in modern democracies and especially in the European Union (EU). Major themes are: expert-rule and democratic legitimacy; the role of knowledge and expertise in EU governance; and the European Commission’s use of expertise.
    • Expert-rule and democratic legitimacy
      • Papers within this theme concentrate on issues such as the “implications of modern democracies’ knowledge and expertise dependence for political and democratic theory.” Topics include the accountability of experts, the legitimacy of expert arrangements within democracies, the role of evidence in policy-making, how expertise can be problematic in democratic contexts, and “ethical expertise” and its place in epistemic democracies.
    • The role of knowledge and expertise in EU governance
      • Papers within this theme concentrate on “general trends and developments in the EU with regard to the role of expertise and experts in political decision-making, the implications for the EU’s democratic legitimacy, and analytical strategies for studying expertise and democratic legitimacy in an EU context.”
    • The European Commission’s use of expertise
      • Papers within this theme concentrate on how the European Commission uses expertise and in particular the European Commission’s “expertgroup system.” Topics include the European Citizen’s Initiative, analytic-deliberative processes in EU food safety, the operation of EU environmental agencies, and the autonomy of various EU agencies.

King, Andrew and Karim R. Lakhani. “Using Open Innovation to Identify the Best Ideas.” MIT Sloan Management Review, September 11, 2013. http://bit.ly/HjVOpi.

  • In this paper, King and Lakhani examine different methods for opening innovation, where, “[i]nstead of doing everything in-house, companies can tap into the ideas cloud of external expertise to develop new products and services.”
  • The three types of open innovation discussed are: opening the idea-creation process, competitions where prizes are offered and designers bid with possible solutions; opening the idea-selection process, ‘approval contests’ in which outsiders vote to determine which entries should be pursued; and opening both idea generation and selection, an option used especially by organizations focused on quickly changing needs.

Long, Chengjiang, Gang Hua and Ashish Kapoor. “Active Visual Recognition with Expertise Estimation in Crowdsourcing.” 2013 IEEE International Conference on Computer Vision. December 2013. http://bit.ly/1lRWFur.

  • This paper is focused on improving the crowdsourced labeling of visual datasets from platforms like Mechanical Turk. The authors note that, “Although it is cheap to obtain large quantity of labels through crowdsourcing, it has been well known that the collected labels could be very noisy. So it is desirable to model the expertise level of the labelers to ensure the quality of the labels. The higher the expertise level a labeler is at, the lower the label noises he/she will produce.”
  • Based on the need for identifying expert labelers upfront, the authors developed an “active classifier learning system which determines which users to label which unlabeled examples” from collected visual datasets.
  • The researchers’ experiments in identifying expert visual dataset labelers led to findings demonstrating that the “active selection” of expert labelers is beneficial in cutting through the noise of crowdsourcing platforms.

Noveck, Beth Simone. “’Peer to Patent’: Collective Intelligence, Open Review, and Patent Reform.” Harvard Journal of Law & Technology 20, no. 1 (Fall 2006): 123–162. http://bit.ly/HegzTT.

  • This law review article introduces the idea of crowdsourcing expertise to mitigate the challenge of patent processing. Noveck argues that, “access to information is the crux of the patent quality problem. Patent examiners currently make decisions about the grant of a patent that will shape an industry for a twenty-year period on the basis of a limited subset of available information. Examiners may neither consult the public, talk to experts, nor, in many cases, even use the Internet.”
  • Peer-to-Patent, which launched three years after this article, is based on the idea that, “The new generation of social software might not only make it easier to find friends but also to find expertise that can be applied to legal and policy decision-making. This way, we can improve upon the Constitutional promise to promote the progress of science and the useful arts in our democracy by ensuring that only worth ideas receive that ‘odious monopoly’ of which Thomas Jefferson complained.”

Ober, Josiah. “Democracy’s Wisdom: An Aristotelian Middle Way for Collective Judgment.” American Political Science Review 107, no. 01 (2013): 104–122. http://bit.ly/1cgf857.

  • In this paper, Ober argues that, “A satisfactory model of decision-making in an epistemic democracy must respect democratic values, while advancing citizens’ interests, by taking account of relevant knowledge about the world.”
  • Ober describes an approach to decision-making that aggregates expertise across multiple domains. This “Relevant Expertise Aggregation (REA) enables a body of minimally competent voters to make superior choices among multiple options, on matters of common interest.”

Sims, Max H., Jeffrey Bigham, Henry Kautz and Marc W. Halterman. “Crowdsourcing medical expertise in near real time.” Journal of Hospital Medicine 9, no. 7, July 2014. http://bit.ly/1kAKvq7.

  • In this article, the authors discuss the develoment of a mobile application called DocCHIRP, which was developed due to the fact that, “although the Internet creates unprecedented access to information, gaps in the medical literature and inefficient searches often leave healthcare providers’ questions unanswered.”
  • The DocCHIRP pilot project used a “system of point-to-multipoint push notifications designed to help providers problem solve by crowdsourcing from their peers.”
  • Healthcare providers (HCPs) sought to gain intelligence from the crowd, which included 85 registered users, on questions related to medication, complex medical decision making, standard of care, administrative, testing and referrals.
  • The authors believe that, “if future iterations of the mobile crowdsourcing applications can address…adoption barriers and support the organic growth of the crowd of HCPs,” then “the approach could have a positive and transformative effect on how providers acquire relevant knowledge and care for patients.”

Spina, Alessandro. “Scientific Expertise and Open Government in the Digital Era: Some Reflections on EFSA and Other EU Agencies.” in Foundations of EU Food Law and Policy, eds. A. Alemmano and S. Gabbi. Ashgate, 2014. http://bit.ly/1k2EwdD.

  • In this paper, Spina “presents some reflections on how the collaborative and crowdsourcing practices of Open Government could be integrated in the activities of EFSA [European Food Safety Authority] and other EU agencies,” with a particular focus on “highlighting the benefits of the Open Government paradigm for expert regulatory bodies in the EU.”
  • Spina argues that the “crowdsourcing of expertise and the reconfiguration of the information flows between European agencies and teh public could represent a concrete possibility of modernising the role of agencies with a new model that has a low financial burden and an almost immediate effect on the legal governance of agencies.”
  • He concludes that, “It is becoming evident that in order to guarantee that the best scientific expertise is provided to EU institutions and citizens, EFSA should strive to use the best organisational models to source science and expertise.”

Is Crowdsourcing the Future for Legislation?

Curated on Posted on by Stefaan Verhulst

Brian Heaton in GovTech: “…While drafting legislation is traditionally the job of elected officials, an increasing number of lawmakers are using digital platforms such as Wikispaces and GitHub to give constituents a bigger hand in molding the laws they’ll be governed by. The practice has been used this year in both California and New York City, and shows no signs of slowing down anytime soon, experts say.
Trond Undheim, crowdsourcing expert and founder of Yegii Inc., a startup company that provides and ranks advanced knowledge assets in the areas of health care, technology, energy and finance, said crowdsourcing was “certainly viable” as a tool to help legislators understand what constituents are most passionate about.
“I’m a big believer in asking a wide variety of people the same question and crowdsourcing has become known as the long-tail of answers,” Undheim said. “People you wouldn’t necessarily think of have something useful to say.”
California Assemblyman Mike Gatto, D-Los Angeles, agreed. He’s spearheaded an effort this year to let residents craft legislation regarding probate law — a measure designed to allow a court to assign a guardian to a deceased person’s pet. Gatto used the online Wikispaces platform — which allows for Wikipedia-style editing and content contribution — to let anyone with an Internet connection collaborate on the legislation over a period of several months.
The topic of the bill may not have been headline news, but Gatto was encouraged by the media attention his experiment received. As a result, he’s committed to running another crowdsourced bill next year — just on a bigger, more mainstream public issue.
New York City Council Member Ben Kallos has a plethora of technology-related legislation being considered in the Big Apple. Many of the bills are open for public comment and editing on GitHub. In an interview with Government Technology last month, Kallos said he believes using crowdsourcing to comment on and edit legislation is empowering and creates a different sense of democracy where people can put forward their ideas.
County governments also are joining the crowdsourcing trend. The Catawba Regional Council of Governments in South Carolina and the Centralia Council of Governments in North Carolina are gathering opinions on how county leaders should plan for future growth in the region.
At a public forum earlier this year, attendees were given iPads to go online and review four growth options and record their views on which they preferred. The priorities outlined by citizens will be taken back to decision-makers in each of the counties to see how well existing plans match up with what the public wants.
Gatto said he’s encouraged by how quickly the crowdsourcing of policy has spread throughout the U.S. He said there’s a disconnect between governments and their constituencies who believe elected officials don’t listen. But that could change as crowdsourcing continues to make its impact on lawmakers.
“When you put out a call like I did and others have done and say ‘I’m going to let the public draft a law and whatever you draft, I’m committed to introducing it … I think that’s a powerful message,” Gatto said. “I think the public appreciates it because it makes them understand that the government still belongs to them.”

Protecting the Process

Despite the benefits crowdsourcing brings to the legislative process, there remain some question marks about whether it truly provides insight into the public’s feelings on an issue. For example, because many political issues are driven by the influence of special interest groups, what’s preventing those groups from manipulating the bill-drafting process?
Not much, according to Undheim. He cautioned policymakers to be aware of the motivations from people taking part in crowdsourcing efforts to write and edit laws. Gatto shared Undheim’s concerns, but noted that the platform he used for developing his probate law – Wikispaces – has safeguards in place so that a member of his staff can revert language of a crowdsourced bill back to a previous version if it’s determined that someone was trying to unduly influence the drafting process….”

Making We the People More User-Friendly Than Ever

Curated on Posted on by Stefaan Verhulst

The White House: “With more than 14 million users and 21 million signatures, We the People, the White House’s online petition platform, has proved more popular than we ever thought possible. In the nearly three years since launch, we’ve heard from you on a huge range of topics, and issued more than 225 responses.

But we’re not stopping there. We’ve been working to make it easier to sign a petition and today we’re proud to announce the next iteration of We the People.

Since launch, we’ve heard from users who wanted a simpler, more streamlined way to sign petitions without creating an account and logging in every time. This latest update makes that a reality.

We’re calling it “simplified signing” and it takes the account creation step out of signing a petition. As of today, just enter your basic information, confirm your signature via email and you’re done. That’s it. No account to create, no logging in, no passwords to remember.

We the People User Statistics

That’s great news for new users, but we’re betting it’ll be welcomed by our returning signers, too. If you signed a petition six months ago and you don’t remember your password, you don’t have to worry about resetting it. Just enter your email address, confirm your signature, and you’re done.

Go check it out right now on petitions.whitehouse.gov.“

How Crowdsourced Astrophotographs on the Web Are Revolutionizing Astronomy

Curated on Posted on by Stefaan Verhulst

Emerging Technology From the arXiv: “Astrophotography is currently undergoing a revolution thanks to the increased availability of high quality digital cameras and the software available to process the pictures after they have been taken.
Since photographs of the night sky are almost always better with long exposures that capture more light, this processing usually involves combining several images of the same part of the sky to produce one with a much longer effective exposure.
That’s all straightforward if you’ve taken the pictures yourself with the same gear under the same circumstances. But astronomers want to do better.
“The astrophotography group on Flickr alone has over 68,000 images,” say Dustin Lang at Carnegie Mellon University in Pittsburgh and a couple of pals. These and other images represent a vast source of untapped data for astronomers.
The problem is that it’s hard to combine images accurately when little is known about how they were taken. Astronomers take great care to use imaging equipment in which the pixels produce a signal that is proportional to the number of photons that hit.
But the same cannot be said of the digital cameras widely used by amateurs. All kinds of processes can end up influencing the final image.
So any algorithm that combines them has to cope with these variations. “We want to do this without having to infer the (possibly highly nonlinear) processing that has been applied to each individual image, each of which has been wrecked in its own loving way by its creator,” say Lang and co.
Now, these guys say they’ve cracked it. They’ve developed a system that automatically combines images from the same part of the sky to increase the effective exposure time of the resulting picture. And they say the combined images can rival those from much professional telescopes.
They’ve tested this approach by downloading images of two well-known astrophysical objects: the NGC 5907 Galaxy and the colliding pair of galaxies—Messier 51a and 51b.
For NGC 5907, they ended up with 4,000 images from Flickr, 1,000 from Bing and 100 from Google. They used an online system called astrometry.net that automatically aligns and registers images of the night sky and then combined the images using their new algorithm, which they call Enhance.
The results are impressive. They say that the combined images of NGC5907 (bottom three images) show some of the same faint features that revealed a single image taken over 11 hours of exposure using a 50 cm telescope (the top left image). All the images reveal the same kind of fine detail such as a faint stellar stream around the galaxy.
The combined image for the M51 galaxies is just as impressive, taking only 40 minutes to produce on a single processor. It reveals extended structures around both galaxies, which astronomers know to be debris from their gravitational interaction as they collide.
Lang and co say these faint features are hugely important because they allow astronomers to measure the age, mass ratios, and orbital configurations of the galaxies involved. Interestingly, many of these faint features are not visible in any of the input images taken from the Web. They emerge only once images have been combined.
One potential problem with algorithms like this is that they need to perform well as the number of images they combine increases. It’s no good if they grind to a halt as soon as a substantial amount of data becomes available.
On this score, Lang and co say astronomers can rest easy. The performance of their new Enhance algorithm scales linearly with the number of images it has to combine. That means it should perform well on large datasets.
The bottom line is that this kind of crowd-sourced astronomy has the potential to make a big impact, given that the resulting images rival those from large telescopes.
And it could also be used for historical images, say Lang and co. The Harvard Plate Archives, for example, contain half a million images dating back to the 1880s. These were all taken using different emulsions, with different exposures and developed using different processes. So the plates all have different responses to light, making them hard to compare.
That’s exactly the problem that Lang and co have solved for digital images on the Web. So it’s not hard to imagine how they could easily combine the data from the Harvard archives as well….”
Ref: arxiv.org/abs/1406.1528 : Towards building a Crowd-Sourced Sky Map

Crowdsourcing moving beyond the fringe

Curated on Posted on by Stefaan Verhulst

Bob Brown in Networked World: ” Depending up on how you look at it, crowdsourcing is all the rage these days — think Wikipedia, X Prize and Kickstarter — or at the other extreme, greatly underused.
To the team behind the new “insight network” Yegii, crowdsourcing has not nearly reached its potential despite having its roots as far back as the early 1700s and a famous case of the British Government seeking a solution to “The Longitude Problem” in order to make sailing less life threatening. (I get the impression that mention of this example is obligatory at any crowdsourcing event.)
This angel-funded startup, headed by an MIT Sloan School of Management senior lecturer and operating from a Boston suburb, is looking to exploit crowdsourcing’s potential through a service that connects financial, healthcare, technology and other organizations seeking knowledge with experts who can provide it – and fairly fast. To CEO Trond Undheim, crowdsourcing is “no longer for fringe freelance work,” and the goal is to get more organizations and smart individuals involved.
“Yegii is essentially a network of networks, connecting people, organizations, and knowledge in new ways,” says Undheim, who explains that the name Yegii is Korean for “talk” or “discussion”. “Our focus is laser sharp: we only rank and rate knowledge that says something essential about what I see as the four forces of industry disruption: technology, policy, user dynamics and business models.  We tackle challenging business issues across domains, from life sciences to energy to finance.  The point is that today’s industry classification is falling apart. We need more specific insight than in-house strategizing or generalist consulting advice.”
Undheim attempted to drum up interest in the new business last week at an event at Babson College during which a handful of crowdsourcing experts spoke. Harvard Business School adjunct professor Alan MacCormack discussed the X Prize, Netflix Prize and other examples of spurring competition through crowdsourcing. MIT’s Peter Gloor extolled the virtue of collaborative and smart swarms of people vs. stupid crowds (such as football hooligans). A couple of advertising/marketing execs shared stories of how clients and other brands are increasingly tapping into their customer base and the general public for new ideas from slogans to products, figuring that potential new customers are more likely to trust their peers than corporate ads. Another speaker dove into more details about how to run a crowdsourcing challenge, which includes identifying motivation that goes beyond money.
All of this was to frame Yegii’s crowdsourcing plan, which is at the beta stage with about a dozen clients (including Akamai and Santander bank) and is slated for mass production later this year. Yegii’s team consists of five part-timers, plus a few interns, who are building a web-based platform that consists of “knowledge assets,” that is market research, news reports and datasets from free and paid sources. That content – on topics that range from Bitcoin’s impact on banks to telecom bandwidth costs — is reviewed and ranked through a combination of machine learning and human peers. Information seekers would pay Yegii up to hundreds of dollars per month or up to tens of thousands of dollars per project, and then multidisciplinary teams would accept the challenge of answering their questions via customized reports within staged deadlines.
“We are focused on building partnerships with other expert networks and associations that have access to smart people with spare capacity, wherever they are,” Undheim says.
One reason organizations can benefit from crowdsourcing, Undheim says, is because of the “ephemeral nature of expertise in today’s society.” In other words, people within your organization might think of themselves as experts in this or that, but when they really think about it, they might realize their level of expertise has faded. Yegii will strive to narrow down the best sources of information for those looking to come up to speed on a subject over a weekend, whereas hunting for that information across a vast search engine would not be nearly as efficient….”