Paper by Natascha Just & Michael Latzer in Media, Culture & Society (fortcoming): “This paper explores the governance by algorithms in information societies. Theoretically, it builds on (co-)evolutionary innovation studies in order to adequately grasp the interplay of technological and societal change, and combines these with institutional approaches to incorporate governance by technology or rather software as institutions. Methodologically it draws from an empirical survey of Internet-based services that rely on automated algorithmic selection, a functional typology derived from it, and an analysis of associated potential social risks. It shows how algorithmic selection has become a growing source of social order, of a shared social reality in information societies. It argues that – similar to the construction of realities by traditional mass media – automated algorithmic selection applications shape daily lives and realities, affect the perception of the world, and influence behavior. However, the co-evolutionary perspective on algorithms as institutions, ideologies, intermediaries and actors highlights differences that are to be found first in the growing personalization of constructed realities, and second in the constellation of involved actors. Altogether, compared to reality construction by traditional mass media, algorithmic reality construction tends to increase individualization, commercialization, inequalities and deterritorialization, and to decrease transparency, controllability and predictability…(Full Paper)”
Liberating data for public value: The case of Data.gov
Paper by Rashmi Krishnamurthy and Yukika Awazu in the International Journal of Information Management: “Public agencies around the globe are liberating their data. Drawing on a case of Data.gov, we outline the challenges and opportunities that lie ahead for the liberation of public data. Data.gov is an online portal that provides open access to datasets generated by US public agencies and countries around the world in a machine-readable format. By discussing the challenges and opportunities faced by Data.gov, we provide several lessons that can inform research and practice. We suggest that providing access to open data in itself does not spur innovation. Specifically, we claim that public agencies need to spend resources to improve the capacities of their organizations to move toward ‘open data by default’; develop capacities of community to use data to solve problems; and think critically about the unintended consequences of providing access to public data. We also suggest that public agencies need better metrics to evaluate the success of open-data efforts in achieving its goals….(More)”
Open Data Impact: When Demand and Supply Meet
Stefaan Verhulst and Andrew Young at the GovLab: “Today, in “Open Data Impact: When Demand and Supply Meet,” the GovLab and Omidyar Network release key findings about the social, economic, cultural and political impact of open data. The findings are based on 19 detailed case studies of open data projects from around the world. These case studies were prepared in order to address an important shortcoming in our understanding of when, and how, open data works. While there is no shortage of enthusiasm for open data’s potential, nor of conjectural estimates of its hypothetical impact, few rigorous, systematic analyses exist of its concrete, real-world impact…. The 19 case studies that inform this report, all of which can be found at Open Data’s Impact (odimpact.org), a website specially set up for this project, were chosen for their geographic and sectoral representativeness. They seek to go beyond the descriptive (what happened) to the explanatory (why it happened, and what is the wider relevance or impact)….
In order to achieve the potential of open data and scale the impact of the individual projects discussed in our report, we need a better – and more granular – understanding of the enabling conditions that lead to success. We found 4 central conditions (“4Ps”) that play an important role in ensuring success:
- Partnerships: Intermediaries and data collaboratives play an important role in ensuring success, allowing for enhanced matching of supply and demand of data.
- Public infrastructure: Developing open data as a public infrastructure, open to all, enables wider participation, and a broader impact across issues and sectors.
- Policies: Clear policies regarding open data, including those promoting regular assessments of open data projects, are also critical for success.
- Problem definition: Open data initiatives that have a clear target or problem definition have more impact and are more likely to succeed than those with vaguely worded statements of intent or unclear reasons for existence.
Core Challenges
Finally, the success of a project is also determined by the obstacles and challenges it confronts. Our research uncovered 4 major challenges (“4Rs”) confronting open data initiatives across the globe:
- Readiness: A lack of readiness or capacity (evident, for example, in low Internet penetration or technical literacy rates) can severely limit the impact of open data.
- Responsiveness: Open data projects are significantly more likely to be successful when they remain agile and responsive—adapting, for instance, to user feedback or early indications of success and failure.
- Risks: For all its potential, open data does pose certain risks, notably to privacy and security; a greater, more nuanced understanding of these risks will be necessary to address and mitigate them.
- Resource Allocation: While open data projects can often be launched cheaply, those projects that receive generous, sustained and committed funding have a better chance of success over the medium and long term.
Toward a Next Generation Open Data Roadmap
The report we release today concludes with ten recommendations for policymakers, advocates, users, funders and other stakeholders in the open data community. For each step, we include a few concrete methods of implementation – ways to translate the broader recommendation into meaningful impact.
Together, these 10 recommendations and their means of implementation amount to what we call a “Next Generation Open Data Roadmap.” This roadmap is just a start, and we plan to continue fleshing it out in the near future. For now, it offers a way forward. It is our hope that this roadmap will help guide future research and experimentation so that we can continue to better understand how the potential of open data can be fulfilled across geographies, sectors and demographics.
Additional Resources
In conjunction with the release of our key findings paper, we also launch today an “Additional Resources” section on the Open Data’s Impact website. The goal of that section is to provide context on our case studies, and to point in the direction of other, complementary research. It includes the following elements:
- A “repository of repositories,” including other compendiums of open data case studies and sources;
- A compilation of some popular open data glossaries;
- A number of open data research publications and reports, with a particular focus on impact;
- A collection of open data definitions and a matrix of analysis to help assess those definitions….(More)
Innovation Prizes in Practice and Theory
Paper by Michael J. Burstein and Fiona Murray: “Innovation prizes in reality are significantly different from innovation prizes in theory. The former are familiar from popular accounts of historical prizes like the Longitude Prize: the government offers a set amount for a solution to a known problem, like £20,000 for a method of calculating longitude at sea. The latter are modeled as compensation to inventors in return for donating their inventions to the public domain. Neither the economic literature nor the policy literature that led to the 2010 America COMPETES Reauthorization Act — which made prizes a prominent tool of government innovation policy — provides a satisfying justification for the use of prizes, nor does either literature address their operation. In this article, we address both of these problems. We use a case study of one canonical, high profile innovation prize — the Progressive Insurance Automotive X Prize — to explain how prizes function as institutional means to achieve exogenously defined innovation policy goals in the face of significant uncertainty and information asymmetries. Focusing on the structure and function of actual innovation prizes as an empirical matter enables us to make three theoretical contributions to the current understanding of prizes. First, we offer a stronger normative justification for prizes grounded in their status as a key institutional arrangement for solving a specified innovation problem. Second, we develop a model of innovation prize governance and then situate that model in the administrative state, as a species of “new governance” or “experimental” regulation. Third, we derive from those analyses a novel framework for choosing among prizes, patents, and grants, one in which the ultimate choice depends on a trade off between the efficacy and scalability of the institutional solution….(More)”
“Big data” and “open data”: What kind of access should researchers enjoy?
Paper by Gilles Chatellier, Vincent Varlet, and Corinne Blachier-Poisson in Thérapie: “The healthcare sector is currently facing a new paradigm, the explosion of “big data”. Coupled with advances in computer technology, the field of “big data” appears promising, allowing us to better understand the natural history of diseases, to follow-up new technologies (devices, drugs) implementation and to participate in precision medicine, etc. Data sources are multiple (medical and administrative data, electronic medical records, data from rapidly developing technologies such as DNA sequencing, connected devices, etc.) and heterogeneous while their use requires complex methods for accurate analysis. Moreover, faced with this new paradigm, we must determine who could (or should) have access to which data, how to combine collective interest and protection of personal data and how to finance in the long-term both operating costs and databases interrogation. This article analyses the opportunities and challenges related to the use of open and/or “big data”, … (More)”
The Social Intranet: Insights on Managing and Sharing Knowledge Internally
Paper by Ines Mergel for IBM Center for the Business of Government: “While much of the federal government lags behind, some agencies are pioneers in the internal use of social media tools. What lessons and effective practices do they have to offer other agencies?
“Social intranets,” Dr. Mergel writes, “are in-house social networks that use technologies – such as automated newsfeeds, wikis, chats, or blogs – to create engagement opportunities among employees.” They also include the use of internal profile pages that help people identify expertise and interest (similar to Facebook or LinkedIn profiles), and that are used in combination with other social Intranet tools such as on-line communities or newsfeeds.
The report documents four case studies of government use of social intranets – two federal government agencies (the Department of State and the National Aeronautics and Space Administration) and two cross-agency networks (the U.S. Intelligence Community and the Government of Canada).
The author observes: “Most enterprise social networking platforms fail,” but identifies what causes these failures and how successful social intranet initiatives can avoid that fate and thrive. She offers a series of insights for successfully implementing social intranets in the public sector, based on her observations and case studies. …(More)”
The Wisdom of Networks – and the Lessons of Wikipedia
Philip Reitinger at the Analogies Project: “Douglas Merrill said “All of us are smarter than any of us.” This motto of crowdsourcing – looking to the information that can arise from the combined observation by and intelligence of many – is also the prescription for a more secure cyber future. Crowdsourcing security among machines – rather than people – is our best path forward.
Attackers have the advantage online for many reasons, including the ability to leverage a simple error into a significant compromise, to scale attacks more readily than defenses can scale, and to attack at a distance. While the maxim that defenders have to be right all the time, while attackers only have to be right once, is not literally true, it conveys the dilemma of defenders. The connectivity of our devices and agents is inexorably increasing, creating more targets for attack. The complexity of the software we use and the network we must defend is also increasing, making an attack on the individual target or the network easier. And the criticality of our connected systems to our lives is also growing and will continue to grow. Together, this means that we live in a world of steadily increasing risk.
In this environment, the good guys and gals have one significant but counter-intuitive advantage: the size of the network being defended. The soaring prevalence of smart devices is a risk only until it is not, until we combine the abilities of these devices to observe, to induce, and to act to defend the network itself. The cyber ecosystem is the greatest sensor network imaginable, and the data generated by its sensors can drive collective intelligence and collective action to stem threats and isolate infections. The ability of the network components to defend the network may make the future of cybersecurity on the Internet look very much like Wikipedia – one of the best known examples of crowdsourcing – with some obvious failures, but if of importance, generally quickly corrected….
What is necessary to enable the crowdsourcing of defense among network components? A few years ago, while I was at the Department of Homeland Security, it published a paper entitled “Enabling Distributed Security in Cyberspace: Building a Healthy and Resilient Cyber Ecosystem with Automated Collective Action.” This paper posits three requirements:
- Automation so the network can act at Internet speed;
- Interoperability so the barriers to effective collective (network or “crowd”) action are those we impose by policy, as opposed to those imposed on us by technology or process; and
- Authentication to enhance the decision-making and action of the network against attacks.
It has been five years since the paper was published, and I still think these are the key elements of a more secure Internet future. Until we enable the network to defend itself, using its own wisdom of crowds (of agents), offense wins. People should do what people do best, adjust how the network defends itself, and take action when necessary based on intuition, rather than responding to alerts. So when you think about future Internet security problems, think about Stephen Colbert and Wikipedia….(More)”
Next Generation Crowdsourcing for Collective Intelligence
Paper by John Prpić : “New techniques leveraging IT-mediated crowds such as Crowdsensing, Situated Crowdsourcing, Spatial Crowdsourcing, and Wearables Crowdsourcing have now materially emerged. These techniques, here termed next generation Crowdsourcing, serve to extend Crowdsourcing efforts beyond the heretofore dominant desktop computing paradigm. Employing new configurations of hardware, software, and people, these techniques represent new forms of organization for IT-mediated crowds. However, it is not known how these new techniques change the processes and outcomes of IT-mediated crowds for Collective Intelligence purposes? The aim of this exploratory work is to begin to answer this question. The work ensues by outlining the relevant findings of the first generation Crowdsourcing paradigm, before reviewing the emerging literature pertaining to the new generation of Crowdsourcing techniques. Premised on this review, a collectively exhaustive and mutually exclusive typology is formed, organizing the next generation Crowdsourcing techniques along two salient dimensions common to all first generation Crowdsourcing techniques. As a result, this work situates the next generation Crowdsourcing techniques within the extant Crowdsourcing literature, and identifies new research avenues stemming directly from the analysis….(More)”
Cities, Data, and Digital Innovation
Paper by Mark Kleinman: “Developments in digital innovation and the availability of large-scale data sets create opportunities for new economic activities and new ways of delivering city services while raising concerns about privacy. This paper defines the terms Big Data, Open Data, Open Government, and Smart Cities and uses two case studies – London (U.K.) and Toronto – to examine questions about using data to drive economic growth, improve the accountability of government to citizens, and offer more digitally enabled services. The paper notes that London has been one of a handful of cities at the forefront of the Open Data movement and has been successful in developing its high-tech sector, although it has so far been less innovative in the use of “smart city” technology to improve services and lower costs. Toronto has also made efforts to harness data, although it is behind London in promoting Open Data. Moreover, although Toronto has many assets that could contribute to innovation and economic growth, including a growing high-technology sector, world-class universities and research base, and its role as a leading financial centre, it lacks a clear narrative about how these assets could be used to promote the city. The paper draws some general conclusions about the links between data innovation and economic growth, and between open data and open government, as well as ways to use big data and technological innovation to ensure greater efficiency in the provision of city services…(More)“
Crowdsourcing On-street Parking Space Detection
Paper by Ruizhi Liao et al in: “As the number of vehicles continues to grow, parking spaces are at a premium in city streets. Additionally, due to the lack of knowledge about street parking spaces, heuristic circling the blocks not only costs drivers’ time and fuel, but also increases city congestion. In the wake of recent trend to build convenient, green and energy-efficient smart cities, we rethink common techniques adopted by high-profile smart parking systems, and present a user-engaged (crowdsourcing) and sonar-based prototype to identify urban on-street parking spaces. The prototype includes an ultrasonic sensor, a GPS receiver and associated Arduino micro-controllers. It is mounted on the passenger side of a car to measure the distance from the vehicle to the nearest roadside obstacle. Multiple road tests are conducted around Wheatley, Oxford to gather results and emulate the crowdsourcing approach. By extracting parked vehicles’ features from the collected trace, a supervised learning algorithm is developed to estimate roadside parking occupancy and spot illegal parking vehicles. A quantity estimation model is derived to calculate the required number of sensing units to cover urban streets. The estimation is quantitatively compared to a fixed sensing solution. The results show that the crowdsourcing way would need substantially fewer sensors compared to the fixed sensing system…(More)”