Selected Readings on Economic Impact of Open Data

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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 open data was originally published in 2014.

Open data is publicly available data – often released by governments, scientists, and occasionally private companies – that is made available for anyone to use, in a machine-readable format, free of charge. Considerable attention has been devoted to the economic potential of open data for businesses and other organizations, and it is now widely accepted that open data plays an important role in spurring innovation, growth, and job creation. From new business models to innovation in local governance, open data is being quickly adopted as a valuable resource at many levels.

Measuring and analyzing the economic impact of open data in a systematic way is challenging, and governments as well as other providers of open data seek to provide access to the data in a standardized way. As governmental transparency increases and open data changes business models and activities in many economic sectors, it is important to understand best practices for releasing and using non-proprietary, public information. Costs, social challenges, and technical barriers also influence the economic impact of open data.

These selected readings are intended as a first step in the direction of answering the question of if we can and how we consider if opening data spurs economic impact.

Selected Reading List (in alphabetical order)

Annotated Selected Reading List (in alphabetical order)

Bonina, Carla. New Business Models and the Values of Open Data: Definitions, Challenges, and Opportunities. NEMODE 3K – Small Grants Call 2013. http://bit.ly/1xGf9oe

  • In this paper, Dr. Carla Bonina provides an introduction to open data and open data business models, evaluating their potential economic value and identifying future challenges for the effectiveness of open data, such as personal data and privacy, the emerging data divide, and the costs of collecting, producing and releasing open (government) data.

Carpenter, John and Phil Watts. Assessing the Value of OS OpenData™ to the Economy of Great Britain – Synopsis. June 2013. Accessed July 25, 2014. http://bit.ly/1rTLVUE

  • John Carpenter and Phil Watts of Ordnance Survey undertook a study to examine the economic impact of open data to the economy of Great Britain. Using a variety of methods such as case studies, interviews, downlad analysis, adoption rates, impact calculation, and CGE modeling, the authors estimates that the OS OpenData initiative will deliver a net of increase in GDP of £13 – 28.5 million for Great Britain in 2013.

Capgemini Consulting. The Open Data Economy: Unlocking Economic Value by Opening Government and Public Data. Capgemini Consulting. Accessed July 24, 2014. http://bit.ly/1n7MR02

  • This report explores how governments are leveraging open data for economic benefits. Through using a compariative approach, the authors study important open data from organizational, technological, social and political perspectives. The study highlights the potential of open data to drive profit through increasing the effectiveness of benchmarking and other data-driven business strategies.

Deloitte. Open Growth: Stimulating Demand for Open Data in the UK. Deloitte Analytics. December 2012. Accessed July 24, 2014. http://bit.ly/1oeFhks

  • This early paper on open data by Deloitte uses case studies and statistical analysis on open government data to create models of businesses using open data. They also review the market supply and demand of open government data in emerging sectors of the economy.

Gruen, Nicholas, John Houghton and Richard Tooth. Open for Business: How Open Data Can Help Achieve the G20 Growth Target.  Accessed July 24, 2014, http://bit.ly/UOmBRe

  • This report highlights the potential economic value of the open data agenda in Australia and the G20. The report provides an initial literature review on the economic value of open data, as well as a asset of case studies on the economic value of open data, and a set of recommendations for how open data can help the G20 and Australia achieve target objectives in the areas of trade, finance, fiscal and monetary policy, anti-corruption, employment, energy, and infrastructure.

Heusser, Felipe I. Understanding Open Government Data and Addressing Its Impact (draft version). World Wide Web Foundation. http://bit.ly/1o9Egym

  • The World Wide Web Foundation, in collaboration with IDRC has begun a research network to explore the impacts of open data in developing countries. In addition to the Web Foundation and IDRC, the network includes the Berkman Center for Internet and Society at Harvard, the Open Development Technology Alliance and Practical Participation.

Howard, Alex. San Francisco Looks to Tap Into the Open Data Economy. O’Reilly Radar: Insight, Analysis, and Reach about Emerging Technologies.  October 19, 2012.  Accessed July 24, 2014. http://oreil.ly/1qNRt3h

  • Alex Howard points to San Francisco as one of the first municipalities in the United States to embrace an open data platform.  He outlines how open data has driven innovation in local governance.  Moreover, he discusses the potential impact of open data on job creation and government technology infrastructure in the City and County of San Francisco.

Huijboom, Noor and Tijs Van den Broek. Open Data: An International Comparison of Strategies. European Journal of ePractice. March 2011. Accessed July 24, 2014.  http://bit.ly/1AE24jq

  • This article examines five countries and their open data strategies, identifying key features, main barriers, and drivers of progress for of open data programs. The authors outline the key challenges facing European, and other national open data policies, highlighting the emerging role open data initiatives are playing in political and administrative agendas around the world.

Manyika, J., Michael Chui, Diana Farrell, Steve Van Kuiken, Peter Groves, and Elizabeth Almasi Doshi. Open Data: Unlocking Innovation and Performance with Liquid Innovation. McKinsey Global Institute. October 2013. Accessed July 24, 2014.  http://bit.ly/1lgDX0v

  • This research focuses on quantifying the potential value of open data in seven “domains” in the global economy: education, transportation, consumer products, electricity, oil and gas, health care, and consumer finance.

Moore, Alida. Congressional Transparency Caucus: How Open Data Creates Jobs. April 2, 2014. Accessed July 30, 2014. Socrata. http://bit.ly/1n7OJpp

  • Socrata provides a summary of the March 24th briefing of the Congressional Transparency Caucus on the need to increase government transparency through adopting open data initiatives. They include key takeaways from the panel discussion, as well as their role in making open data available for businesses.

Stott, Andrew. Open Data for Economic Growth. The World Bank. June 25, 2014. Accessed July 24, 2014. http://bit.ly/1n7PRJF

  • In this report, The World Bank examines the evidence for the economic potential of open data, holding that the economic potential is quite large, despite a variation in the published estimates, and difficulties assessing its potential methodologically. They provide five archetypes of businesses using open data, and provides recommendations for governments trying to maximize economic growth from open data.

Powerful new patent service shows every US invention, and a new view of R&D relationships

Curated on Posted on by Stefaan Verhulst
at GigaOm: “The website for the U.S. Patent Office website is famously clunky: searching and sorting patents can feel like playing an old Atari game, rather than watching innovation at work. But now a young inventor has come along with a tool to build a better patent office.
The service is called Trea, and was launched by Max Yuan, an engineer who received a patent of his own for a bike motor in 2007. After writing a tool to download patents related to his own invention, he expanded the process to slurp every patent and image in the USPTO database, and compile the information in a user-friendly interface.
Trea has been in beta for a while, but will formally launch on Wednesday. The tool not only provides an easy way to see what inventions a company or inventor is patenting, but also shows the fields in which they are most active. Here is a screenshot from Trea that shows what Apple has been up to in the last 12 months:
Trea screenshot of Apple inventions
Such information could be valuable to investors or to companies that want to use the filings as a way to track what might be in their competitors’ product pipelines. The Trea database also probes the USPTO for new filings, and can send alerts to subscribers. Yuan has also created a Twitter account just for new Apple filings.
Trea also draws on the patent database to display what Yuan calls a “unified knowledge graph” of relationships between inventors. Pictures, like the one below for IBM, show clusters of inventors and, at a broader level, the viral transmission of human ideas within a company:
Trea IBM screenshot
 
This type of information, gleaned from patent filings, could be valuable to corporate strategists, or to journalists, scholars or business historians. And making government websites more user-friendly, as Rankandfiled.com is attempting to do with Securities and Exchange Commission filings, can certainly help people understand what their regulators are doing….”

How to harness the wisdom of crowds to improve public service delivery and policymaking

Curated on Posted on by Stefaan Verhulst

Eddie Copeland in PolicyBytes: “…In summary, government has used technology to streamline transactions and better understand the public’s opinions. Yet it has failed to use it to radically change the way it works. Have public services been reinvented? Is government smaller and leaner? Have citizens, businesses and civic groups been offered the chance to take part in the work of government and improve their own communities? On all counts the answer is unequivocally, no. What is needed, therefore, is a means to enable citizens to provide data to government to inform policymaking and to improve – or even help deliver – public services. What is needed is a Government Data Marketplace.

Government Data Marketplace

A Government Data Marketplace (GDM) would be a website that brought together public sector bodies that needed data, with individuals, businesses and other organisations that could provide it. Imagine an open data portal in reverse: instead of government publishing its own datasets to be used by citizens and businesses, it would instead publish its data needs and invite citizens, businesses or community groups to provide that data (for free or in return for payment). Just as open data portals aim to provide datasets in standard, machine-readable formats, GDM would operate according to strict open standards, and provide a consistent and automated way to deliver data to government through APIs.
How would it work? Imagine a local council that wished to know where instances of graffiti occurred within its borough. The council would create an account on GDM and publish a new request, outlining the data it required (not dissimilar to someone posting a job on a site like Freelancer). Citizens, businesses and other organisations would be able to view that request on GDM and bid to offer the service. For example, an app-development company could offer to build an app that would enable citizens to photograph and locate instances of graffiti in the borough. The app would be able to upload the data to GDM. The council could connect its own IT system to GDM to pass the data to their own database.
Importantly, the app-development company would specify via GDM how much it would charge to provide the data. Other companies and organisations could offer competing bids for delivering the same – or an even better service – at different prices. Supportive local civic hacker groups could even offer to provide the data for free. Either way, the council would get the data it needed without having to collect it for itself, whilst also ensuring it paid the best price from a number of competing providers.
Since GDM would be a public marketplace, other local authorities would be able to see that a particular company had designed a graffiti-reporting solution for one council, and could ask for the same data to be collected in their own boroughs. This would be quick and easy for the developer, as instead of having to create a bespoke solution to work with each council’s IT system, they could connect to all of them using one common interface via GDM. That would good for the company, as they could sell to a much larger market (the same solution would work for one council or all), and good for the councils, as they would benefit from cheaper prices generated from economies of scale. And since GDM would use open standards, if a council was unhappy with the data provided by one supplier, it could simply look to another company to provide the same information.
What would be the advantages of such a system? Firstly, innovation. GDM would free government from having to worry about what software it needed, and instead allow it to focus on the data it required to provide a service. To be clear: councils themselves do not need a graffiti app – they need data on where graffiti is. By focusing attention on its data needs, the public sector could let the market innovate to find the best solutions for providing it. That might be via an app, perhaps via a website, social media, or Internet of Things sensors, or maybe even using a completely new service that collected information in a radically different way. It will not matter – the right information would be provided in a common format via GDM.
Secondly, the potential cost savings of this approach would be many and considerable. At the very least, by creating a marketplace, the public sector would be able to source data at a competitive price. If several public sector bodies needed the same service via GDM, companies providing that data would be able to offer much cheaper prices for all, as instead of having to deal with hundreds of different organisations (and different interfaces) they could create one solution that worked for all of them. As prices became cheaper for standard solutions, this would in turn encourage more public sector bodies to converge on common ways of working, driving down costs still further. Yet these savings would be dwarfed by those possible if GDM could be used to source data that public sectors bodies currently have to manually collect themselves. Imagine if instead of having teams of inspectors to locate instances X, Y or Z, it could instead source the same data from citizens via GDM?
There would no limit to the potential applications to which GDM could be put by central and local government and other public sector bodies: for graffiti, traffic levels, environmental issues, education or welfare. It could be used to crowdsource facts, figures, images, map coordinates, text – anything that can be collected as data. Government could request information on areas on which it previously had none, helping them to assign their finite resources and money in a much more targeted way. New York City’s Mayor’s Office of Data Analytics has demonstrated that up to 500% increases in the efficiency of providing some public services can be achieved, if only the right data is available.
For the private sector, GDM would stimulate the growth of innovative new companies offering community data, and make it easier for them to sell data solutions across the whole of the public sector. They could pioneer in new data methods, and potentially even take over the provision of entire services which the public sector currently has to provide itself. For citizens, it would offer a means to genuinely get involved in solving issues that matter to their local communities, either by using apps made by businesses, or working to provide the data themselves.
And what about the benefits for policymaking? It is important to acknowledge that the idea of harnessing the wisdom of crowds for policymaking is currently experimental. In the case of Policy Futures Markets, some applications have also been considered to be highly controversial. So which methods would be most effective? What would they look like? In what policy domains would they provide most value? The simple fact is that we do not know. What is certain, however, is that innovation in open policymaking and crowdsourcing ideas will never be achieved until a platform is available that allows such ideas to be tried and tested. GDM could be that platform.
Public sector bodies could experiment with asking citizens for information or answers to particular, fact-based questions, or even for predictions on future outcomes, to help inform their policymaking activities. The market could then innovate to develop solutions to source that data from citizens, using the many different models for harnessing the wisdom of crowds. The effectiveness of those initiatives could then be judged, and the techniques honed. In the worst case scenario that it did not work, money would not have been wasted on building the wrong platform – GDM would continue to have value in providing data for public service needs as described above….”

Interpreting Hashtag Politics – Policy Ideas in an Era of Social Media

Curated on Posted on by Stefaan Verhulst

New book by Stephen Jeffares: “Why do policy actors create branded terms like Big Society and does launching such policy ideas on Twitter extend or curtail their life? This book argues that the practice of hashtag politics has evolved in response to an increasingly congested and mediatised environment, with the recent and rapid growth of high speed internet connections, smart phones and social media. It examines how policy analysis can adapt to offer interpretive insights into the life and death of policy ideas in an era of hashtag politics.
This text reveals that policy ideas can at the same time be ideas, instruments, visions, containers and brands, and advises readers on how to tell if a policy idea is dead or dying, how to map the diversity of viewpoints, how to capture the debate, when to engage and when to walk away. Each chapter showcases innovative analytic techniques, illustrated by application to contemporary policy ideas.”

OkCupid reveals it’s been lying to some of its users. Just to see what’ll happen.

Curated on Posted on by Stefaan Verhulst

Brian Fung in the Washington Post: “It turns out that OkCupid has been performing some of the same psychological experiments on its users that landed Facebook in hot water recently.
In a lengthy blog post, OkCupid cofounder Christian Rudder explains that OkCupid has on occasion played around with removing text from people’s profiles, removing photos, and even telling some users they were an excellent match when in fact they were only a 30 percent match according to the company’s systems. Just to see what would happen.
OkCupid defends this behavior as something that any self-respecting Web site would do.
“OkCupid doesn’t really know what it’s doing. Neither does any other Web site,” Rudder wrote. “But guess what, everybody: if you use the Internet, you’re the subject of hundreds of experiments at any given time, on every site. That’s how websites work.”…
we have a bigger problem on our hands: A problem about how to reconcile the sometimes valuable lessons of data science with the creep factor — particularly when you aren’t notified about being studied. But as I’ve written before, these kinds of studies happen all the time; it’s just rare that the public is presented with the results.
Short of banning the practice altogether, which seems totally unrealistic, corporate data science seems like an opportunity on a number of levels, particularly if it’s disclosed to the public. First, it helps us understand how human beings tend to behave at Internet scale. Second, it tells us more about how Internet companies work. And third, it helps consumers make better decisions about which services they’re comfortable using.
I suspect that what bothers us most of all is not that the research took place, but that we’re slowly coming to grips with how easily we ceded control over our own information — and how the machines that collect all this data may all know more about us than we do ourselves. We had no idea we were even in a rabbit hole, and now we’ve discovered we’re 10 feet deep. As many as 62.5 percent of Facebook users don’t know the news feed is generated by a company algorithm, according to a recent study conducted by Christian Sandvig, an associate professor at the University of Michigan, and Karrie Karahalios, an associate professor at the University of Illinois.
OkCupid’s blog post is distinct in several ways from Facebook’s psychological experiment. OkCupid didn’t try to publish its findings in a scientific journal. It isn’t even claiming that what it did was science. Moreover, OkCupid’s research is legitimately useful to users of the service — in ways that Facebook’s research is arguably not….
But in any case, there’s no such motivating factor when it comes to Facebook. Unless you’re a page administrator or news organization, understanding how the newsfeed works doesn’t really help the average user in the way that understanding how OkCupid works does. That’s because people use Facebook for all kinds of reasons that have nothing to do with Facebook’s commercial motives. But people would stop using OkCupid if they discovered it didn’t “work.”
If you’re lying to your users in an attempt to improve your service, what’s the line between A/B testing and fraud?”

The Social Laboratory

Curated on Posted on by Stefaan Verhulst

Shane Harris in Foreign Policy: “…, Singapore has become a laboratory not only for testing how mass surveillance and big-data analysis might prevent terrorism, but for determining whether technology can be used to engineer a more harmonious society….Months after the virus abated, Ho and his colleagues ran a simulation using Poindexter’s TIA ideas to see whether they could have detected the outbreak. Ho will not reveal what forms of information he and his colleagues used — by U.S. standards, Singapore’s privacy laws are virtually nonexistent, and it’s possible that the government collected private communications, financial data, public transportation records, and medical information without any court approval or private consent — but Ho claims that the experiment was very encouraging. It showed that if Singapore had previously installed a big-data analysis system, it could have spotted the signs of a potential outbreak two months before the virus hit the country’s shores. Prior to the SARS outbreak, for example, there were reports of strange, unexplained lung infections in China. Threads of information like that, if woven together, could in theory warn analysts of pending crises.
The RAHS system was operational a year later, and it immediately began “canvassing a range of sources for weak signals of potential future shocks,” one senior Singaporean security official involved in the launch later recalled.
The system uses a mixture of proprietary and commercial technology and is based on a “cognitive model” designed to mimic the human thought process — a key design feature influenced by Poindexter’s TIA system. RAHS, itself, doesn’t think. It’s a tool that helps human beings sift huge stores of data for clues on just about everything. It is designed to analyze information from practically any source — the input is almost incidental — and to create models that can be used to forecast potential events. Those scenarios can then be shared across the Singaporean government and be picked up by whatever ministry or department might find them useful. Using a repository of information called an ideas database, RAHS and its teams of analysts create “narratives” about how various threats or strategic opportunities might play out. The point is not so much to predict the future as to envision a number of potential futures that can tell the government what to watch and when to dig further.
The officials running RAHS today are tight-lipped about exactly what data they monitor, though they acknowledge that a significant portion of “articles” in their databases come from publicly available information, including news reports, blog posts, Facebook updates, and Twitter messages. (“These articles have been trawled in by robots or uploaded manually” by analysts, says one program document.) But RAHS doesn’t need to rely only on open-source material or even the sorts of intelligence that most governments routinely collect: In Singapore, electronic surveillance of residents and visitors is pervasive and widely accepted…”

App offers a comprehensive guide to local and real-time parking restrictions

Curated on Posted on by Stefaan Verhulst

Springwise: “Finding a parking space in big cities can be a nightmare, which recently prompted on-demand valet service Caarbon to do the job for drivers. But even when a space is discovered, it’s easy to fall foul of rules and regulations that aren’t clearly marked or are just plain confusing. AppyParking is an app that details paid and non-paid parking spaces in real time, as well as factors such as congestion charges and special restrictions.
The main interface of the app is a map of the local area. Users first select the type of parking space they’re looking for, whether it’s a paid meter bay, car park, disabled parking space or even a vacant resident’s driveway through a partnership with ParkatmyHouse. AppyParking shows the nearest locations, along with details about any rules in place. A green thumbs up means it’s free to park at the current time, but a red thumbs down means that there’s a charge or the space has resident priority at the time, suggesting the driver may want to look elsewhere. The app features up-to-date information on parking restrictions for each individual space, including changes on public holidays. It also alerts users if large events such as soccer games are scheduled to take place that might affect their ability to park.
Watch the video to learn more about the app.
Website: www.appyparking.com“

Request for Proposals: Exploring the Implications of Government Release of Large Datasets

Curated on Posted on by Stefaan Verhulst

“The Berkeley Center for Law & Technology and Microsoft are issuing this request for proposals (RFP) to fund scholarly inquiry to examine the civil rights, human rights, security and privacy issues that arise from recent initiatives to release large datasets of government information to the public for analysis and reuse.  This research may help ground public policy discussions and drive the development of a framework to avoid potential abuses of this data while encouraging greater engagement and innovation.
This RFP seeks to:

    • Gain knowledge of the impact of the online release of large amounts of data generated by citizens’ interactions with government
    • Imagine new possibilities for technical, legal, and regulatory interventions that avoid abuse
    • Begin building a body of research that addresses these issues

– BACKGROUND –

 
Governments at all levels are releasing large datasets for analysis by anyone for any purpose—“Open Data.”  Using Open Data, entrepreneurs may create new products and services, and citizens may use it to gain insight into the government.  A plethora of time saving and other useful applications have emerged from Open Data feeds, including more accurate traffic information, real-time arrival of public transportation, and information about crimes in neighborhoods.  Sometimes governments release large datasets in order to encourage the development of unimagined new applications.  For instance, New York City has made over 1,100 databases available, some of which contain information that can be linked to individuals, such as a parking violation database containing license plate numbers and car descriptions.
Data held by the government is often implicitly or explicitly about individuals—acting in roles that have recognized constitutional protection, such as lobbyist, signatory to a petition, or donor to a political cause; in roles that require special protection, such as victim of, witness to, or suspect in a crime; in the role as businessperson submitting proprietary information to a regulator or obtaining a business license; and in the role of ordinary citizen.  While open government is often presented as an unqualified good, sometimes Open Data can identify individuals or groups, leading to a more transparent citizenry.  The citizen who foresees this growing transparency may be less willing to engage in government, as these transactions may be documented and released in a dataset to anyone to use for any imaginable purpose—including to deanonymize the database—forever.  Moreover, some groups of citizens may have few options or no choice as to whether to engage in governmental activities.  Hence, open data sets may have a disparate impact on certain groups. The potential impact of large-scale data and analysis on civil rights is an area of growing concern.  A number of civil rights and media justice groups banded together in February 2014 to endorse the “Civil Rights Principles for the Era of Big Data” and the potential of new data systems to undermine longstanding civil rights protections was flagged as a “central finding” of a recent policy review by White House adviser John Podesta.
The Berkeley Center for Law & Technology (BCLT) and Microsoft are issuing this request for proposals in an effort to better understand the implications and potential impact of the release of data related to U.S. citizens’ interactions with their local, state and federal governments. BCLT and Microsoft will fund up to six grants, with a combined total of $300,000.  Grantees will be required to participate in a workshop to present and discuss their research at the Berkeley Technology Law Journal (BTLJ) Spring Symposium.  All grantees’ papers will be published in a dedicated monograph.  Grantees’ papers that approach the issues from a legal perspective may also be published in the BTLJ. We may also hold a followup workshop in New York City or Washington, DC.
While we are primarily interested in funding proposals that address issues related to the policy impacts of Open Data, many of these issues are intertwined with general societal implications of “big data.” As a result, proposals that explore Open Data from a big data perspective are welcome; however, proposals solely focused on big data are not.  We are open to proposals that address the following difficult question.  We are also open to methods and disciplines, and are particularly interested in proposals from cross-disciplinary teams.

    • To what extent does existing Open Data made available by city and state governments affect individual profiling?  Do the effects change depending on the level of aggregation (neighborhood vs. cities)?  What releases of information could foreseeably cause discrimination in the future? Will different groups in society be disproportionately impacted by Open Data?
    • Should the use of Open Data be governed by a code of conduct or subject to a review process before being released? In order to enhance citizen privacy, should governments develop guidelines to release sampled or perturbed data, instead of entire datasets? When datasets contain potentially identifiable information, should there be a notice-and-comment proceeding that includes proposed technological solutions to anonymize, de-identify or otherwise perturb the data?
    • Is there something fundamentally different about government services and the government’s collection of citizen’s data for basic needs in modern society such as power and water that requires governments to exercise greater due care than commercial entities?
    • Companies have legal and practical mechanisms to shield data submitted to government from public release.  What mechanisms do individuals have or should have to address misuse of Open Data?  Could developments in the constitutional right to information policy as articulated in Whalen and Westinghouse Electric Co address Open Data privacy issues?
    • Collecting data costs money, and its release could affect civil liberties.  Yet it is being given away freely, sometimes to immensely profitable firms.  Should governments license data for a fee and/or impose limits on its use, given its value?
    • The privacy principle of “collection limitation” is under siege, with many arguing that use restrictions will be more efficacious for protecting privacy and more workable for big data analysis.  Does the potential of Open Data justify eroding state and federal privacy act collection limitation principles?   What are the ethical dimensions of a government system that deprives the data subject of the ability to obscure or prevent the collection of data about a sensitive issue?  A move from collection restrictions to use regulation raises a number of related issues, detailed below.
    • Are use restrictions efficacious in creating accountability?  Consumer reporting agencies are regulated by use restrictions, yet they are not known for their accountability.  How could use regulations be implemented in the context of Open Data efficaciously?  Can a self-learning algorithm honor data use restrictions?
    • If an Open Dataset were regulated by a use restriction, how could individuals police wrongful uses?   How would plaintiffs overcome the likely defenses or proof of facts in a use regulation system, such as a burden to prove that data were analyzed and the product of that analysis was used in a certain way to harm the plaintiff?  Will plaintiffs ever be able to beat first amendment defenses?
    • The President’s Council of Advisors on Science and Technology big data report emphasizes that analysis is not a “use” of data.  Such an interpretation suggests that NSA metadata analysis and large-scale scanning of communications do not raise privacy issues.  What are the ethical and legal implications of the “analysis is not use” argument in the context of Open Data?
    • Open Data celebrates the idea that information collected by the government can be used by another person for various kinds of analysis.  When analysts are not involved in the collection of data, they are less likely to understand its context and limitations.  How do we ensure that this knowledge is maintained in a use regulation system?
    • Former President William Clinton was admitted under a pseudonym for a procedure at a New York Hospital in 2004.  The hospital detected 1,500 attempts by its own employees to access the President’s records.  With snooping such a tempting activity, how could incentives be crafted to cause self-policing of government data and the self-disclosure of inappropriate uses of Open Data?
    • It is clear that data privacy regulation could hamper some big data efforts.  However, many examples of big data successes hail from highly regulated environments, such as health care and financial services—areas with statutory, common law, and IRB protections.  What are the contours of privacy law that are compatible with big data and Open Data success and which are inherently inimical to it?
    • In recent years, the problem of “too much money in politics” has been addressed with increasing disclosure requirements.  Yet, distrust in government remains high, and individuals identified in donor databases have been subjected to harassment.  Is the answer to problems of distrust in government even more Open Data?
    • What are the ethical and epistemological implications of encouraging government decision-making based upon correlation analysis, without a rigorous understanding of cause and effect?  Are there decisions that should not be left to just correlational proof? While enthusiasm for data science has increased, scientific journals are elevating their standards, with special scrutiny focused on hypothesis-free, multiple comparison analysis. What could legal and policy experts learn from experts in statistics about the nature and limits of open data?…
      To submit a proposal, visit the Conference Management Toolkit (CMT) here.
      Once you have created a profile, the site will allow you to submit your proposal.
      If you have questions, please contact Chris Hoofnagle, principal investigator on this project.”

Towards Timely Public Health Decisions to Tackle Seasonal Diseases With Open Government Data

Curated on Posted on by Stefaan Verhulst
Paper by Vandana Srivastava and Biplav Srivastava for the Workshops at the Twenty-Eighth AAAI Conference on Artificial Intelligence : “Improving public health is a major responsibility of any government, and is of major interest to citizens and scientific communities around the world. Here, one sees two extremes. On one hand, tremendous progress has been made in recent years in the understanding of causes, spread and remedies of common and regularly occurring diseases like Dengue, Malaria and Japanese Encephalistis (JE). On the other hand, public agencies treat these diseases in an ad hoc manner without learning from the experiences of previous years. Specifically, they would get alerted once reported cases have already arisen substantially in the known disease season, reactively initiate a few actions and then document the disease impact (cases, deaths) for that period, only to forget this learning in the next season. However, they miss the opportunity to reduce preventable deaths and sickness, and their corresponding economic impact, which scientific progress could have enabled. The gap is universal but very prominent in developing countries like India.
In this paper, we show that if public agencies provide historical disease impact information openly, it can be analyzed with statistical and machine learning techniques, correlated with best emerging practices in disease control, and simulated in a setting to optimize social benefits to provide timely guidance for new disease seasons and regions. We illustrate using open data for mosquito-borne communicable diseases; published results in public health on efficacy of Dengue control methods and apply it on a simulated typical city for maximal benefits with available resources. The exercise helps us further suggest strategies for new regions that may be anywhere in the world, how data could be better recorded by city agencies and what prevention methods should medical community focus on for wider impact.
Full Text: PDF“

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:
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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.
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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….”