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
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
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
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
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
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)”.
Questioning Big Data: Crowdsourcing crisis data towards an inclusive humanitarian response
, , , , and The aim of this paper is to critically explore whether crowdsourced Big Data enables an inclusive humanitarian response at times of crisis. We argue that all data, including Big Data, are socially constructed artefacts that reflect the contexts and processes of their creation. To support our argument, we qualitatively analysed the process of ‘Big Data making’ that occurred by way of crowdsourcing through open data platforms, in the context of two specific humanitarian crises, namely the 2010 earthquake in Haiti and the 2015 earthquake in Nepal. We show that the process of creating Big Data from local and global sources of knowledge entails the transformation of information as it moves from one distinct group of contributors to the next. The implication of this transformation is that locally based, affected people and often the original ‘crowd’ are excluded from the information flow, and from the interpretation process of crowdsourced crisis knowledge, as used by formal responding organizations, and are marginalized in their ability to benefit from Big Data in support of their own means. Our paper contributes a critical perspective to the debate on participatory Big Data, by explaining the process of in and exclusion during data making, towards more responsive humanitarian relief….(More)”.
Rethinking Nudge: Libertarian paternalism and classical utilitarianism
Hiroaki Itai, Akira Inoue, and Satoshi Kodama in Special Issue on Nudging of The Tocqueville Review/La revue Tocqueville: “Recently, libertarian paternalism has been intensely debated. It recommends us to employ policies and practices that “nudge” ordinary people to make better choices without forcing them to do so. Nudging policies and practices have penetrated our society, in cases like purchasing life insurance or a residence. They are also used for preventing people from addictive acts that may be harmful to them in the long run, such as having too much sugary or fatty food. In nudging people to act rationally, various kinds of cognitive effects impacting the consumers’ decision-making process should be considered, given the growing influence of consumer advertising. Since libertarian paternalism makes use of such effects in light of the recent development of behavioral economics and cognitive psychology in a principled manner, libertarian paternalism and its justification of nudges attract our attention as an approach providing a normative guidance for our action. …
This paper has two aims: the first is to examine whether libertarian paternalism can give an appropriate theoretical foundation to the idea and practice of nudges. The second is to show that utilitarianism, or, more precisely, the classical version of utilitarianism, treats nudges in a more consistent and plausible manner. To achieve these two aims, first of all, we dwell on how Cass Sunstein—one of the founder of libertarian paternalism—misconceives Mill’s harm principle, and that this may prompt us to see that utilitarianism can reasonably legitimate nudging policies (section one). We then point to two biases that embarrass libertarian paternalism (the scientism bias and the dominant-culture bias), which we believe stem from the fact that libertarian paternalism assumes the informed preference satisfaction view of welfare (section two). We finally argue that classical utilitarianism not only can overcome the two biases, but can also reasonably endorse any system monitoring a choice architect to discharge his or her responsibility (section three)….(More)”
Taking a More Sophisticated Look at Human Beings
Nathan Collins at Pacific Standard: “Are people fundamentally selfish, or are they cooperators? Actually, it’s kind of an odd question—after all, why are those the only options? The answer is that those options are derived in large part from philosophy and classical economic theory, rather than data. In a new paper, researchers have flipped the script, using observations of simple social situations to show that optimism, pessimism, envy, and trust, rather than selfishness and sacrifice, are the basic ingredients of our behavior.
That conclusion advances wider “efforts toward the identification of basic behavioral phenotypes,” or categories of behavior, and the results could be usefully applied in social science, policy, and business, Julia Poncela-Casasnovas and her colleagues write in Science Advances.
Classical economic theory has something of a bad reputation these days, and not without reason. For one thing, most economic theory assumes people are rational, in the sense that they are strategic and maximize their payoffs in all that they do. The list of objections to that approach is long and well-documented, but there’s a counter objection—amid a slew of objections and anecdotes, there’s little in the way of a cohesive alternative theory.
Optimism, pessimism, envy, and trust are the basic ingredients of our behavior.
Poncela-Casasnovas and her colleagues’ experiments are, they hope, a step toward such a theory. Their idea was to put ordinary people in simple social situations with economic tradeoffs, observe how those people act, and then construct a data-driven classification of their behavior…. Using standard statistical methods, the researchers identified four such player types: optimists (20 percent), who always go for the highest payoff, hoping the other player will coordinate to achieve that goal; pessimists (30 percent), who act according to the opposite assumption; the envious (21 percent), who try to score more points than their partners; and the trustful (17 percent), who always cooperate. The remaining 12 percent appeared to make their choices completely at random.
Those results don’t yet add up to anything like a theory of human behavior, but they do “open the door to making relevant advances in a number of directions,” the authors write. In particular, the researchers hope their results will help explain behavior in other simple games, and aid those hoping to understand how people may respond to new policy initiatives….(More)”
Open Data for Developing Economies
Scan of the literature by Andrew Young, Stefaan Verhulst, and Juliet McMurren: This edition of the GovLab Selected Readings was developed as part of the Open Data for Developing Economies research project (in collaboration with WebFoundation, USAID and fhi360). Special thanks to Maurice McNaughton, Francois van Schalkwyk, Fernando Perini, Michael Canares and David Opoku for their input on an early draft. Please contact Stefaan Verhulst ([email protected]) for any additional input or suggestions.
Open data is increasingly seen as a tool for economic and social development. Across sectors and regions, policymakers, NGOs, researchers and practitioners are exploring the potential of open data to improve government effectiveness, create new economic opportunity, empower citizens and solve public problems in developing economies. Open data for development does not exist in a vacuum – rather it is a phenomenon that is relevant to and studied from different vantage points including Data4Development (D4D), Open Government, the United Nations’ Sustainable Development Goals (SDGs), and Open Development. The below-selected readings provide a view of the current research and practice on the use of open data for development and its relationship to related interventions.
Selected Reading List (in alphabetical order)
- Open Data and Open Government for Development
- Solomon Benjamin, R. Bhuvaneswari, P. Rajan, Manjunatha – Bhoomi: ‘E-Governance’, or, An Anti-Politics Machine Necessary to Globalize Bangalore? – a paper offering a critical take on digitization and transparency efforts, particularly in Bangalore.
- Rosie McGee and Duncan Edwards – Introduction: Opening Governance – Change, Continuity and Conceptual Ambiguity – an introduction to a special issue of IDS bulletin on open government for development.
- Open Data and Data 4 Development
- 3rd International Open Data Conference (IODC) – Enabling the Data Revolution: An International Open Data Roadmap – a summary report of the third International Open Data Conference offering a roadmap for leveraging open data for sustainable development.
- Martin Hilbert – Big Data for Development: A Review of Promises and Challenges – an article offering a conceptual framework on the opportunities and threats of leveraging data for international development.
- International Development Research Centre, World Wide Web Foundation, and Berkman Center at Harvard University – Fostering a Critical Development Perspective on Open Government Data – a paper assessing how the real-world impact of open data, particularly in the Global South, are or are not meeting expectations.
- Open Data for Development – Open Data for Development: Building an Inclusive Data Revolution – a report providing an overview of the Open Data for Development (OD4D) program and its early findings.
- Elizabeth Stuart, Emma Samman, William Avis, Tom Berliner – The Data Revolution: Finding the Missing Millions – a report outlining the challenge of using data for development when many people are not represented in official databases.
- United Nations Independent Expert Advisory Group on a Data Revolution for Sustainable Development – A World That Counts, Mobilizing the Data Revolution – a report examining the opportunities and risks for using data for sustainable development.
- The World Bank – Digital Dividends: World Development Report 2016 – a report on the use of digital technologies, including big and open data, to improve development efforts.
- Open Data and Open Development…
- Open Data and Development Goals
- Evangelia Berdou – Mediating Voices and Communicating Realities: Using Information Crowdsourcing Tools, Open Data Initiatives and Digital Media to Support and Protect the Vulnerable and Marginalised – a report exploring how crowdsourcing, mapping and open data can generate and publicly share information that could benefit vulnerable and marginalized communities.
- Michael Canares, Satyarupa Shekhar – Open Data and Sub-national Governments: Lessons from Developing Countries – a synthesis paper providing lessons learned regarding sub-national open data from the Open Data in Developing Countries research project.
- Tim Davies – Open Data in Developing Countries – Emerging Insights from Phase I – a report offering 15 central insights from 13 countries studied in the Exploring the Emerging Impacts of Open Data in Developing Countries research network.
- Tim Davies, Duncan Edwards – Emerging Implications of Open and Linked Data for Knowledge Sharing Development – a study and collection of case studies examining how open and linked data can benefit development.
- Tim Davies, Fernando Perini, and Jose Alonso – Researching the Emerging Impacts of Open Data – a paper providing a conceptual framework and comparative theory of change for open data, with a particular focus on developing countries.
- Elise Montano and Diogo Silva – Exploring the Emerging Impacts of Open Data in Developing Countries (ODDC): ODDC1 Follow-up Outcome Evaluation Report – a report summarizing the findings of a project on open data’s impact on governance in developing countries.
- Fiona Smith, William Gerry, Emma Truswell – Supporting Sustainable Development with Open Data – a report describing the the benefits and challenges of using open data to achieve the SDGs.
- Open Data and Developing Countries (National Case Studies)….(More)”