Designing the Next Generation of Open Data Policy

Curated on Posted on by Stefaan Verhulst

Andrew Young and Stefaan Verhulst at the Open Data Charter Blog: “The international Open Data Charter has emerged from the global open data community as a galvanizing document to place open government data directly in the hands of citizens and organizations. To drive this process forward, and ensure that the outcomes are both systemic and transformational, new open data policy needs to be based on evidence of how and when open data works in practice. To support this work, the GovLab, in collaboration with Omidyar Network, has recently completed research which provides vital evidence of open data projects around the world, including an analysis of 19 in-depth, impact-focused case studies and a key findings paper. All of the research is now available in an eBook published by O’Reilly Media.

The research found that open data is making an impact in four core ways, including:…(More)”

Responsible Data in Agriculture

Curated on Posted on by Stefaan Verhulst

Report by Lindsay Ferris and Zara Rahman for GODAN: “The agriculture sector is creating increasing amounts of data, from many different sources. From tractors equipped with GPS tracking, to open data released by government ministries, data is becoming ever more valuable, as agricultural business development and global food policy decisions are being made based upon data. But the sector is also home to severe resource inequality. The largest agricultural companies make billions of dollars per year, in comparison with subsistence farmers growing just enough to feed themselves, or smallholder farmers who grow enough to sell on a year-by-year basis. When it comes to data and technology, these differences in resources translate to stark power imbalances in data access and use. The most well resourced actors are able to delve into new technologies and make the most of those insights, whereas others are unable to take any such risks or divert any of their limited resources. Access to and use of data has radically changed the business models and behaviour of some of those well resourced actors, but in contrast, those with fewer resources are receiving the same, limited access to information that they always have.

In this paper, we have approached these issues from a responsible data perspective, drawing upon the experience of the Responsible Data community1 who over the past three years have created tools, questions and resources to deal with the ethical, legal, privacy and security challenges that come from new uses of data in various sectors. This piece aims to provide a broad overview of some of the responsible data challenges facing these actors, with a focus on the power imbalance between actors, and looking into how that inequality affects behaviour when it comes to the agricultural data ecosystem. What are the concerns of those with limited resources, when it comes to this new and rapidly changing data environment? In addition, what are the ethical grey areas or uncertainties that we need to address in the future? As a first attempt to answer these questions, we spoke to 14 individuals with various perspectives on the sector to understand what the challenges are for them and for the people they work with. We also carried out desk research to dive deeper into these issues, and we provide here an analysis of our findings and responsible data challenges….(More)”

Crowdsourcing: It Matters Who the Crowd Are

Curated on Posted on by Stefaan Verhulst

Paper by Alexis Comber, Peter Mooney, Ross S. Purves, Duccio Rocchini, and Ariane Walz: “Volunteered geographical information (VGI) and citizen science have become important sources data for much scientific research. In the domain of land cover, crowdsourcing can provide a high temporal resolution data to support different analyses of landscape processes. However, the scientists may have little control over what gets recorded by the crowd, providing a potential source of error and uncertainty. This study compared analyses of crowdsourced land cover data that were contributed by different groups, based on nationality (labelled Gondor and Non-Gondor) and on domain experience (labelled Expert and Non-Expert). The analyses used a geographically weighted model to generate maps of land cover and compared the maps generated by the different groups. The results highlight the differences between the maps how specific land cover classes were under- and over-estimated. As crowdsourced data and citizen science are increasingly used to replace data collected under the designed experiment, this paper highlights the importance of considering between group variations and their impacts on the results of analyses. Critically, differences in the way that landscape features are conceptualised by different groups of contributors need to be considered when using crowdsourced data in formal scientific analyses. The discussion considers the potential for variation in crowdsourced data, the relativist nature of land cover and suggests a number of areas for future research. The key finding is that the veracity of citizen science data is not the critical issue per se. Rather, it is important to consider the impacts of differences in the semantics, affordances and functions associated with landscape features held by different groups of crowdsourced data contributors….(More)”

How Citizen Attachment to Neighborhoods Helps to Improve Municipal Services and Public Spaces

Curated on Posted on by Stefaan Verhulst

Paper by Daniel O’Brien, Dietmar Offenhuber, Jessica Baldwin-Philippi, Melissa Sands, and Eric Gordon: “What motivates people to contact their local governments with reports about street light outages, potholes, graffiti, and other deteriorations in public spaces? Current efforts to improve government interactions with constituents operate on the premise that citizens who make such reports are motivated by broad civic values. In contrast, our recent research demonstrates that such citizens are primarily motivated by territoriality – that is, attachments to the spaces where they live. Our research focuses on Boston’s “311 system,” which provides telephone hotlines and web channels through which constituents can request non-emergency government services.

Although our study focuses on 311 users in Boston, it holds broader implications for more than 400 U.S. municipalities that administer similar systems. And our results encourage a closer look at the drivers of citizen participation in many “coproduction programs” – programs that involve people in the design and implementation of government services. Currently, 311 is just one example of government efforts to use technology to involve constituents in joint efforts.

Territorial Ties and Civic Engagement

The concept of territoriality originated in studies of animal behavior – such as bears marking trees in the forest or lions and hyenas fighting over a kill. Human beings also need to manage the ownership of objects and spaces, but social psychologists have demonstrated that human territoriality, whether at home, the workplace, or a neighborhood, entails more than the defense of objects or spaces against others. It includes maintenance and caretaking, and even extends to items shared with others….(More)”

Law in the Future

Curated on Posted on by Stefaan Verhulst

Paper by Benjamin Alarie, Anthony Niblett and Albert Yoon: “The set of tasks and activities in which humans are strictly superior to computers is becoming vanishingly small. Machines today are not only performing mechanical or manual tasks once performed by humans, they are also performing thinking tasks, where it was long believed that human judgment was indispensable. From self-driving cars to self-flying planes; and from robots performing surgery on a pig to artificially intelligent personal assistants, so much of what was once unimaginable is now reality. But this is just the beginning of the big data and artificial intelligence revolution. Technology continues to improve at an exponential rate. How will the big data and artificial intelligence revolutions affect law? We hypothesize that the growth of big data, artificial intelligence, and machine learning will have important effects that will fundamentally change the way law is made, learned, followed, and practiced. It will have an impact on all facets of the law, from the production of micro-directives to the way citizens learn of their legal obligations. These changes will present significant challenges to human lawmakers, judges, and lawyers. While we do not attempt to address all these challenges, we offer a short and positive preview of the future of law: a world of self-driving law, of legal singularity, and of the democratization of the law…(More)”

For Quick Housing Data, Hit Craigslist

Curated on Posted on by Stefaan Verhulst

Tanvi Misra at CityLab: “…housing researchers can use the Internet bulletin board for a more worthy purpose: as a source of fairly accurate, real-time data on the U.S. rental housing market.

A new paper in the Journal of Planning Education and Research analyzed 11 million Craigslist rental listings posted between May and July 2014 across the U.S. and found a treasure trove of information on regional and local housing trends. “Being able to track rental listings data from Craigslist is really useful for urban planners to take the pulse of [changing neighborhoods] much more quickly,” says Geoff Boeing, a researcher at University of California at Berkeley’s Urban Analytics Lab, who co-authored the paper with Paul Waddell, a Berkeley professor of planning and design.

Here are a couple of big takeaways from their deep dive down the CL rabbit hole:

Overall, Craigslist listings track with HUD data (except when they don’t)

The researchers compared median rents in different Craigslist domains (metropolitan areas, essentially) to the corresponding Housing and Urban Development median rents. In New Orleans and Oklahoma City, the posted and the official rents were very similar. But in other metros, they diverged significantly. In Las Vegas, for example, the Craigslist median rent was lower than the HUD median rent, but in New York, it was much, much higher.

“That’s important for local planners to be careful with because there are totally different cultures and ways that Craigslist is used in different cities,” Boeing explains. “The economies of the cities could very much affect how rentals are being posted. If they’re posting it higher [on Craigslist], they may negotiate down eventually. Or, if they’re posting it low, they could be expecting a bidding war with a bunch of tenants coming in.” …(More)”

Encouraging and Sustaining Innovation in Government: Technology and Innovation in the Next Administration

Curated on Posted on by Stefaan Verhulst

New report by Beth Simone Noveck and Stefaan Verhulst: “…With rates of trust in government at an all-time low, technology and innovation will be essential to achieve the next administration’s goals and to deliver services more effectively and efficiently. The next administration must prioritize using technology to improve governing and must develop plans to do so in the transition… This paper provides analysis and a set of concrete recommendations, both for the period of transition before the inauguration, and for the start of the next presidency, to encourage and sustain innovation in government. Leveraging the insights from the experts who participated in a day-long discussion, we endeavor to explain how government can improve its use of using digital technologies to create more effective policies, solve problems faster and deliver services more effectively at the federal, state and local levels….

The broad recommendations are:

  • Scale Data Driven Governance: Platforms such as data.gov represent initial steps in the direction of enabling data-driven governance. Much more can be done, however, to open-up data and for the agencies to become better consumers of data, to improve decision-making and scale up evidence-based governance. This includes better use of predictive analytics, more public engagement; and greater use of cutting-edge methods like machine learning.
  • Scale Collaborative Innovation: Collaborative innovation takes place when government and the public work together, thus widening the pool of expertise and knowledge brought to bear on public problems. The next administration can reach out more effectively, not just to the public at large, but to conduct targeted outreach to public officials and citizens who possess the most relevant skills or expertise for the problems at hand.
  • Promote a Culture of Innovation: Institutionalizing a culture of technology-enabled innovation will require embedding and institutionalizing innovation and technology skills more widely across the federal enterprise. For example, contracting, grants and personnel officials need to have a deeper understanding of how technology can help them do their jobs more efficiently, and more people need to be trained in human-centered design, gamification, data science, data visualization, crowdsourcing and other new ways of working.
  • Utilize Evidence-Based Innovation: In order to better direct government investments, leaders need a much better sense of what works and what doesn’t. The government spends billions on research in the private and university sectors, but very little experimenting with, testing, and evaluating its own programs. The next administration should continue developing an evidence-based approach to governance, including a greater use of methods like A/B testing (a method of comparing two versions of a webpage or app against each other to determine which one performs the best); establishing a clearinghouse for success and failure stories and best practices; and encouraging overseers to be more open to innovation.
  • Make Innovation a Priority in the Transition: The transition period represents a unique opportunity to seed the foundations for long-lasting change. By explicitly incorporating innovation into the structure, goals and activities of the transition teams, the next administration can get a fast start in implementing policy goals and improving government operations through innovation approaches….(More)”

Designing Serious Games for Citizen Engagement in Public Service Processes

Curated on Posted on by Stefaan Verhulst

Paper by Nicolas Pflanzl , Tadeu Classe, Renata Araujo, and Gottfried Vossen: “One of the challenges envisioned for eGovernment is how to actively involve citizens in the improvement of public services, allowing governments to offer better services. However, citizen involvement in public service design through ICT is not an easy goal. Services have been deployed internally in public organizations, making it difficult to be leveraged by citizens, specifically those without an IT background. This research moves towards decreasing the gap between public services process opacity and complexity and citizens’ lack of interest or competencies to understand them. The paper discusses game design as an approach to motivate, engage and change citizens’ behavior with respect to public services improvement. The design of a sample serious game is proposed; benefits and challenges are discussed using a public service delivery scenario from Brazil….(More)”

The risks of relying on robots for fairer staff recruitment

Curated on Posted on by Stefaan Verhulst

Sarah O’Connor at the Financial Times: “Robots are not just taking people’s jobs away, they are beginning to hand them out, too. Go to any recruitment industry event and you will find the air is thick with terms like “machine learning”, “big data” and “predictive analytics”.

The argument for using these tools in recruitment is simple. Robo-recruiters can sift through thousands of job candidates far more efficiently than humans. They can also do it more fairly. Since they do not harbour conscious or unconscious human biases, they will recruit a more diverse and meritocratic workforce.

This is a seductive idea but it is also dangerous. Algorithms are not inherently neutral just because they see the world in zeros and ones.

For a start, any machine learning algorithm is only as good as the training data from which it learns. Take the PhD thesis of academic researcher Colin Lee, released to the press this year. He analysed data on the success or failure of 441,769 job applications and built a model that could predict with 70 to 80 per cent accuracy which candidates would be invited to interview. The press release plugged this algorithm as a potential tool to screen a large number of CVs while avoiding “human error and unconscious bias”.

But a model like this would absorb any human biases at work in the original recruitment decisions. For example, the research found that age was the biggest predictor of being invited to interview, with the youngest and the oldest applicants least likely to be successful. You might think it fair enough that inexperienced youngsters do badly, but the routine rejection of older candidates seems like something to investigate rather than codify and perpetuate. Mr Lee acknowledges these problems and suggests it would be better to strip the CVs of attributes such as gender, age and ethnicity before using them….(More)”

Nudges That Fail

Curated on Posted on by Stefaan Verhulst

Paper by Cass R. Sunstein: “Why are some nudges ineffective, or at least less effective than choice architects hope and expect? Focusing primarily on default rules, this essay emphasizes two reasons. The first involves strong antecedent preferences on the part of choosers. The second involves successful “counternudges,” which persuade people to choose in a way that confounds the efforts of choice architects. Nudges might also be ineffective, and less effective than expected, for five other reasons. (1) Some nudges produce confusion on the part of the target audience. (2) Some nudges have only short-term effects. (3) Some nudges produce “reactance” (though this appears to be rare) (4) Some nudges are based on an inaccurate (though initially plausible) understanding on the part of choice architects of what kinds of choice architecture will move people in particular contexts. (5) Some nudges produce compensating behavior, resulting in no net effect. When a nudge turns out to be insufficiently effective, choice architects have three potential responses: (1) Do nothing; (2) nudge better (or different); and (3) fortify the effects of the nudge, perhaps through counter-counternudges, perhaps through incentives, mandates, or bans….(More)”.