As part of an ongoing effort to build a knowledge base for the field of opening governance by organizing and disseminating its learnings, the GovLab Selected Readings series provides an annotated and curated collection of recommended works on key opening governance topics. In this edition, we explore the literature on Crowdsourcing Expertise. To suggest additional readings on this or any other topic, please email firstname.lastname@example.org.
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)
- Katy Börner, Michael Conlon, Jon Corson-Rikert, and Ying Ding — VIVO: A Semantic Approach to Scholarly Networking and Discovery — an introduction to VIVO, a tool for representing information about researchers’ expertise and organizational relationships.
- Alessandro Bozzon, Marco Brambilla, Stefano Ceri, Matteo Silvestri, and Giuliano Vesci — Choosing the Right Crowd: Expert Finding in Social Networks — a paper exploring the challenge of identifying the expertise needed for a given problem through the use of social networks.
- Daren C. Brabham — The Myth of Amateur Crowds — a paper arguing that, contrary to popular belief, experts are more prevalent in crowdsourcing projects than hobbyists and amateurs.
- William H. Dutton — Networking Distributed Public Expertise: Strategies for Citizen Sourcing Advice to Government — a paper arguing for more structured and well-managed crowdsourcing efforts within government to help harness the distributed expertise of citizens.
- Gagan Goel, Afshin Nikzad and Adish Singla – Matching Workers with Tasks: Incentives in Heterogeneous Crowdsourcing Markets – a paper exploring the intelligent tasking of Mechanical Turk workers based on varying levels of expertise.
- D. Gubanov, N. Korgin, D. Novikov and A. Kalkov – E-Expertise: Modern Collective Intelligence – an ebook focusing on the organizations and mechanisms of expert decisionmaking.
- Cathrine Holst – Expertise and Democracy – a collection of papers on the role of knowledge and expertise in modern democracies.
- Andrew King and Karim R. Lakhani — Using Open Innovation to Identify the Best Ideas — a paper examining different methods for opening innovation and tapping the “ideas cloud” of external expertise.
- Chengjiang Long, Gang Hua and Ashish Kapoor – Active Visual Recognition with Expertise Estimation in Crowdsourcing – a paper proposing a mechanism for identifying experts in a Mechanical Turk project.
- Beth Simone Noveck — “Peer to Patent”: Collective Intelligence, Open Review, and Patent Reform — a law review article introducing the idea of crowdsourcing expertise to mitigate the challenge of patent processing.
- Josiah Ober — Democracy’s Wisdom: An Aristotelian Middle Way for Collective Judgment — a paper discussing the Relevant Expertise Aggregation (REA) model for improving democratic decision-making.
- Max H. Sims, Jeffrey Bigham, Henry Kautz and Marc W. Halterman – Crowdsourcing medical expertise in near real time – a paper describing the development of a mobile application to give healthcare providers with better access to expertise.
- Alessandro Spina – Scientific Expertise and Open Government in the Digital Era: Some Reflections on EFSA and Other EU Agencies – a paper proposing increased crowdsourcing of expertise within the European Food Safety Authority.
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
- 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.
- Expert-rule and democratic legitimacy
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.”
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