Ralph Hertwig and Till Grüne-Yanoff in Perspectives on Psychological Science: “In recent years, policy makers worldwide have begun to acknowledge the potential value of insights from psychology and behavioral economics into how people make decisions. These insights can inform the design of nonregulatory and nonmonetary policy interventions—as well as more traditional fiscal and coercive measures. To date, much of the discussion of behaviorally informed approaches has emphasized “nudges,” that is, interventions designed to steer people in a particular direction while preserving their freedom of choice. Yet behavioral science also provides support for a distinct kind of nonfiscal and noncoercive intervention, namely, “boosts.” The objective of boosts is to foster people’s competence to make their own choices—that is, to exercise their own agency. Building on this distinction, we further elaborate on how boosts are conceptually distinct from nudges: The two kinds of interventions differ with respect to (a) their immediate intervention targets, (b) their roots in different research programs, (c) the causal pathways through which they affect behavior, (d) their assumptions about human cognitive architecture, (e) the reversibility of their effects, (f) their programmatic ambitions, and (g) their normative implications. We discuss each of these dimensions, provide an initial taxonomy of boosts, and address some possible misconceptions….(More)”.
Crowdsourcing citizen science: exploring the tensions between paid professionals and users
Jamie Woodcock et al in the Journal of Peer Production: “This paper explores the relationship between paid labour and unpaid users within the Zooniverse, a crowdsourced citizen science platform. The platform brings together a crowd of users to categorise data for use in scientific projects. It was initially established by a small group of academics for a single astronomy project, but has now grown into a multi-project platform that has engaged over 1.3 million users so far. The growth has introduced different dynamics to the platform as it has incorporated a greater number of scientists, developers, links with organisations, and funding arrangements—each bringing additional pressures and complications. The relationships between paid/professional and unpaid/citizen labour have become increasingly complicated with the rapid expansion of the Zooniverse. The paper draws on empirical data from an ongoing research project that has access to both users and paid professionals on the platform. There is the potential through growing peer-to-peer capacity that the boundaries between professional and citizen scientists can become significantly blurred. The findings of the paper, therefore, address important questions about the combinations of paid and unpaid labour, the involvement of a crowd in citizen science, and the contradictions this entails for an online platform. These are considered specifically from the viewpoint of the users and, therefore, form a new contribution to the theoretical understanding of crowdsourcing in practice….(More)”.
Open & Shut
Harsha Devulapalli: “Welcome to Open & Shut — a new blog dedicated to exploring the opportunities and challenges of working with open data in closed societies around the world. Although we’ll be exploring questions relevant to open data practitioners worldwide, we’re particularly interested in seeing how civil society groups and actors in the Global South are using open data to push for greater government transparency, and tackle daunting social and economic challenges facing their societies….Throughout this series we’ll be profiling and interviewing organisations working with open data worldwide, and providing do-it-yourself data tutorials that will be useful for beginners as well as data experts. …
What do we mean by the terms ‘open data’ and ‘closed societies’?
It’s important to be clear about what we’re dealing with, here. So let’s establish some key terms. When we talk about ‘open data’, we mean data that anyone can access, use and share freely. And when we say ‘closed societies’, we’re referring to states or regions in which the political and social environment is actively hostile to notions of openness and public scrutiny, and which hold principles of freedom of information in low esteem. In closed societies, data is either not published at all by the government, or else is only published in inaccessible formats, is missing data, is hard to find or else is just not digitised at all.
Iran is one such state that we would characterise as a ‘closed society’. At Small Media, we’ve had to confront the challenges of poor data practice, secrecy, and government opaqueness while undertaking work to support freedom of information and freedom of expression in the country. Based on these experiences, we’ve been working to build Iran Open Data — a civil society-led open data portal for Iran, in an effort to make Iranian government data more accessible and easier for researchers, journalists, and civil society actors to work with.
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..Open & Shut will shine a light on the exciting new ways that different groups are using data to question dominant narratives, transform public opinion, and bring about tangible change in closed societies. At the same time, it’ll demonstrate the challenges faced by open data advocates in opening up this valuable data. We intend to get the community talking about the need to build cross-border alliances in order to empower the open data movement, and to exchange knowledge and best practices despite the different needs and circumstances we all face….(More)“
Inside the Lab That’s Quantifying Happiness
Rowan Jacobsen at Outside: “In Mississippi, people tweet about cake and cookies an awful lot; in Colorado, it’s noodles. In Mississippi, the most-tweeted activity is eating; in Colorado, it’s running, skiing, hiking, snowboarding, and biking, in that order. In other words, the two states fall on opposite ends of the behavior spectrum. If you were to assign a caloric value to every food mentioned in every tweet by the citizens of the United States and a calories-burned value to every activity, and then totaled them up, you would find that Colorado tweets the best caloric ratio in the country and Mississippi the worst.
Sure, you’d be forgiven for doubting people’s honesty on Twitter. On those rare occasions when I destroy an entire pint of Ben and Jerry’s, I most assuredly do not tweet about it. Likewise, I don’t reach for my phone every time I strap on a pair of skis.
And yet there’s this: Mississippi has the worst rate of diabetes and heart disease in the country and Colorado has the best. Mississippi has the second-highest percentage of obesity; Colorado has the lowest. Mississippi has the worst life expectancy in the country; Colorado is near the top. Perhaps we are being more honest on social media than we think. And perhaps social media has more to tell us about the state of the country than we realize.
That’s the proposition of Peter Dodds and Chris Danforth, who co-direct the University of Vermont’s Computational Story Lab, a warren of whiteboards and grad students in a handsome brick building near the shores of Lake Champlain. Dodds and Danforth are applied mathematicians, but they would make a pretty good comedy duo. When I stopped by the lab recently, both were in running clothes and cracking jokes. They have an abundance of curls between them and the wiry energy of chronic thinkers. They came to UVM in 2006 to start the Vermont Complex Systems Center, which crunches big numbers from big systems and looks for patterns. Out of that, they hatched the Computational Story Lab, which sifts through some of that public data to discern the stories we’re telling ourselves. “It took us a while to come up with the name,” Dodds told me as we shotgunned espresso and gazed into his MacBook. “We were going to be the Department of Recreational Truth.”
This year, they teamed up with their PhD student Andy Reagan to launch the Lexicocalorimeter, an online tool that uses tweets to compute the calories in and calories out for every state. It’s no mere party trick; the Story Labbers believe the Lexicocalorimeter has important advantages over slower, more traditional methods of gathering health data….(More)”.
Where’s the ‘Civic’ in CivicTech?
Blog by Pius Enywaru: “The ideology of community participation and development is a crucial topic for any nation or community seeking to attain sustainable development. Here in Uganda, oftentimes when the opportunity for public participation either in local planning or in holding local politicians to account — the ‘don’t care’ attitude reigns….
What works?
Some of these tools include Ask Your Government Uganda, a platform built to help members of the public get the information they want about from 106 public agencies in Uganda. U-Report developed by UNICEF provides an SMS-based social monitoring tool designed to address issues affecting the youth of Uganda. Mentioned in a previous blog post, Parliament Watchbrings the proceedings of the Parliament of Uganda to the citizens. The organization leverages technology to share live updates on social media and provides in-depth analysis to create a better understanding on the business of Parliament. Other tools used include citizen scorecards, public media campaigns and public petitions. Just recently, we have had a few calls to action to get people to sign petitions, with somewhat lackluster results.
What doesn’t work?
Although the usage of these tools have dramatically grown, there is still a lack of awareness and consequently, community participation. In order to understand the interventions which the Government of Uganda believes are necessary for sustainable urban development, it is important to examine the realities pertaining to urban areas and their planning processes. There are many challenges in deploying community participation tools based on ICT such as limited funding and support for such initiatives, low literacy levels, low technical literacy, a large digital divide, low rates of seeking input from communities in developing these tools, lack of adequate government involvement and resistance/distrust of change by both government and citizens. Furthermore, in many of these initiatives, a large marketing or sensitization push is needed to let citizens know that these services exist for their benefit.
There are great minds who have brilliant ideas to try and bring literally everyone on board though civic engagement. When you have a look at their ideas, you will agree that indeed they might make a reputable service and bring about remarkable change in different communities. However, the biggest question has always been, “How do these ideas get executed and adopted by these communities that they target”? These ideas suffer a major setback of lack of inclusivity to enhance community participation. This still remains a puzzle for most folks that have these ideas….(More)”.
We need a safe space for policy failure
Catherine Althaus & David Threlfall in The Mandarin: “Who remembers Google Schemer, the Apple Pippin, or Microsoft Zune? No one — and yet such no-go ideas didn’t hold back these prominent companies. In IT, such high profile failures are simply steps on the path to future success. When a start-up or major corporate puts a product onto the market they identify the kinks in their invention immediately, design a fix, and release a new version. If the whole idea falls flat — and who ever listened to music on a Zune instead of an iPod? — the next big thing is just around the corner. Learning from failure is celebrated as a key feature of innovation.
But in the world of public policy, this approach is only now creeping into our collective consciousness. We tread ever so lightly.
Drug policy, childcare reform, or information technology initiatives are areas where innovation could provide policy improvements, but who is going to be a first-mover innovator in this policy area without fearing potential retribution should anything go wrong?…
Public servants don’t have the luxury of ‘making a new version’ without fear of blame or retribution. Critically, their process often lacks the ability to test assumptions before delivery….
The most persuasive or entertaining narrative often trumps the painstaking work — and potential missteps — required to build an evidence base to support political and policy decisions. American academics Elizabeth Shanahan, Mark McBeth and Paul Hathaway make a remarkable claim regarding the power of narrative in the policy world: “Research in the field of psychology shows that narratives have a stronger ability to persuade individuals and influence their beliefs than scientific evidence does.” If narrative and stories overtake what we normally accept as evidence, then surely we ought to be taking more notice of what the narratives are, which we choose and how we use them…
Failing the right way
Essential policy spheres such as health, education and social services should benefit from innovative thinking and theory testing. What is necessary in these areas is even more robust attention to carefully calibrated and well-thought through experimentation. Rewards need to outweigh risks, and risks need to be properly managed. This has always been the case in clinical trials in medicine. Incredible breakthroughs in medical practice made throughout the 20th century speak to the success of this model. Why should policymaking suffer from a timid inertia given the potential for similar success?
An innovative approach, focused on learning while failing right, will certainly require a shift in thinking. Every new initiative will need to be designed in a holistic way, to not just solve an issue but learn from every stage of the design and delivery process. Evaluation doesn’t follow implementation but instead becomes part of the entire cycle. A small-scale, iterative approach can then lead to bigger successes down the track….(More)”.
Ireland Opens E-Health Open Data Portal
Adi Gaskell at HuffPost: “… an open data portal has been launched by eHealth Ireland. The portal aims to bring together some 300 different open data sources into one place, making it easier to find data from across the Irish Health Sector.
The portal includes data from a range of sources, including statistics on hospital day and inpatient cases, waiting list statistics and information around key new digital initiatives.
Open data
The resource features datasets from both the Department of Health and HealthLink, so the team believe that the data is of the highest quality, and also compliant with the Open Health Data Policy. This ensures that the approach taken with the release of data is consistent and in accordance with national and international guidelines.
“I am delighted to welcome the launch of the eHealth Ireland Open Data Portal today. The aim of Open Data is twofold; on the one hand facilitating transparency of the Public Sector and on the other providing a valuable resource that can drive innovation. The availability of Open Data can empower citizens and support clinicians, care providers, and researchers make better decisions, spur new innovations and identify efficiencies while ensuring that personal data remains confidential,” Richard Corbridge, CIO at the Health Service Executive says.
Data from both HealthLink and the National Treatment Purchase Fund (NTPF) will be uploaded to the portal each month, with new datasets due to be added on a regular basis….
The project follows a number of clearly defined Open Health Data Principles that are designed to support the health service in the provision of better patient care and in the support of new innovations in the sector, all whilst ensuring that patient data is secured and governed appropriately…(More)”.
Artificial Intelligence for Citizen Services and Government
Paper by Hila Mehr: “From online services like Netflix and Facebook, to chatbots on our phones and in our homes like Siri and Alexa, we are beginning to interact with artificial intelligence (AI) on a near daily basis. AI is the programming or training of a computer to do tasks typically reserved for human intelligence, whether it is recommending which movie to watch next or answering technical questions. Soon, AI will permeate the ways we interact with our government, too. From small cities in the US to countries like Japan, government agencies are looking to AI to improve citizen services.
While the potential future use cases of AI in government remain bounded by government resources and the limits of both human creativity and trust in government, the most obvious and immediately beneficial opportunities are those where AI can reduce administrative burdens, help resolve resource allocation problems, and take on significantly complex tasks. Many AI case studies in citizen services today fall into five categories: answering questions, filling out and searching documents, routing requests, translation, and drafting documents. These applications could make government work more efficient while freeing up time for employees to build better relationships with citizens. With citizen satisfaction with digital government offerings leaving much to be desired, AI may be one way to bridge the gap while improving citizen engagement and service delivery.
Despite the clear opportunities, AI will not solve systemic problems in government, and could potentially exacerbate issues around service delivery, privacy, and ethics if not implemented thoughtfully and strategically. Agencies interested in implementing AI can learn from previous government transformation efforts, as well as private-sector implementation of AI. Government offices should consider these six strategies for applying AI to their work: make AI a part of a goals-based, citizen-centric program; get citizen input; build upon existing resources; be data-prepared and tread carefully with privacy; mitigate ethical risks and avoid AI decision making; and, augment employees, do not replace them.
This paper explores the various types of AI applications, and current and future uses of AI in government delivery of citizen services, with a focus on citizen inquiries and information. It also offers strategies for governments as they consider implementing AI….(More)”
Algorithmic regulation: A critical interrogation
Karen Yeung in Regulation and Governance: “Innovations in networked digital communications technologies, including the rise of “Big Data,” ubiquitous computing, and cloud storage systems, may be giving rise to a new system of social ordering known as algorithmic regulation. Algorithmic regulation refers to decisionmaking systems that regulate a domain of activity in order to manage risk or alter behavior through continual computational generation of knowledge by systematically collecting data (in real time on a continuous basis) emitted directly from numerous dynamic components pertaining to the regulated environment in order to identify and, if necessary, automatically refine (or prompt refinement of) the system’s operations to attain a pre-specified goal. This study provides a descriptive analysis of algorithmic regulation, classifying these decisionmaking systems as either reactive or pre-emptive, and offers a taxonomy that identifies eight different forms of algorithmic regulation based on their configuration at each of the three stages of the cybernetic process: notably, at the level of standard setting (adaptive vs. fixed behavioral standards), information-gathering and monitoring (historic data vs. predictions based on inferred data), and at the level of sanction and behavioral change (automatic execution vs. recommender systems). It maps the contours of several emerging debates surrounding algorithmic regulation, drawing upon insights from regulatory governance studies, legal critiques, surveillance studies, and critical data studies to highlight various concerns about the legitimacy of algorithmic regulation….(More)”.
Journal tries crowdsourcing peer reviews, sees excellent results
Chris Lee at ArsTechnica: “Peer review is supposed to act as a sanity check on science. A few learned scientists take a look at your work, and if it withstands their objective and entirely neutral scrutiny, a journal will happily publish your work. As those links indicate, however, there are some issues with peer review as it is currently practiced. Recently, Benjamin List, a researcher and journal editor in Germany, and his graduate assistant, Denis Höfler, have come up with a genius idea for improving matters: something called selected crowd-sourced peer review….
My central point: peer review is burdensome and sometimes barely functional. So how do we improve it? The main way is to experiment with different approaches to the reviewing process, which many journals have tried, albeit with limited success. Post-publication peer review, when scientists look over papers after they’ve been published, is also an option but depends on community engagement.
But if your paper is uninteresting, no one will comment on it after it is published. Pre-publication peer review is the only moment where we can be certain that someone will read the paper.
So, List (an editor for Synlett) and Höfler recruited 100 referees. For their trial, a forum-style commenting system was set up that allowed referees to comment anonymously on submitted papers but also on each other’s comments as well. To provide a comparison, the papers that went through this process also went through the traditional peer review process. The authors and editors compared comments and (subjectively) evaluated the pros and cons. The 100-person crowd of researchers was deemed the more effective of the two.
The editors found that it took a bit more time to read and collate all the comments into a reviewers’ report. But it was still faster, which the authors loved. Typically, it took the crowd just a few days to complete their review, which compares very nicely to the usual four to six weeks of the traditional route (I’ve had papers languish for six months in peer review). And, perhaps most important, the responses were more substantive and useful compared to the typical two-to-four-person review.
So far, List has not published the trial results formally. Despite that, Synlett is moving to the new system for all its papers.
Why does crowdsourcing work?
Here we get back to something more editorial. I’d suggest that there is a physical analog to traditional peer review, called noise. Noise is not just a constant background that must be overcome. Noise is also generated by the very process that creates a signal. The difference is how the amplitude of noise grows compared to the amplitude of signal. For very low-amplitude signals, all you measure is noise, while for very high-intensity signals, the noise is vanishingly small compared to the signal, even though it’s huge compared to the noise of the low-amplitude signal.
Our esteemed peers, I would argue, are somewhat random in their response, but weighted toward objectivity. Using this inappropriate physics model, a review conducted by four reviewers can be expected (on average) to contain two responses that are, basically, noise. By contrast, a review by 100 reviewers may only have 10 responses that are noise. Overall, a substantial improvement. So, adding the responses of a large number of peers together should produce a better picture of a scientific paper’s strengths and weaknesses.
Didn’t I just say that reviewers are overloaded? Doesn’t it seem that this will make the problem worse?
Well, no, as it turns out. When this approach was tested (with consent) on papers submitted to Synlett, it was discovered that review times went way down—from weeks to days. And authors reported getting more useful comments from their reviewers….(More)”.