Paper by Martin Karlsson, Joachim Åström and Magnus Adenskog: “The Estonian Citizens’ Assembly (ECA) was initiated in late 2012 as a direct consequence of a legitimacy crisis of Estonian political parties and representative institutions. The spark igniting this crisis was the unraveling of a scheme of illegal party financing. The response from governmental institutions took the form of a democratic innovation involving public crowd‐sourcing and deliberative mini‐publics. This study reports on a survey among the participants in the online crowd‐sourcing process of the ECA (n = 847). The study examines how this democratic innovation influenced participants’ social and political trust as well as the impact of participants’ predispositions and level of satisfaction with the ECA on changes in trust. We find that participants that had positive predispositions and who were satisfied with the ECA were more likely to gain trust. Furthermore, we also find that the participants, in general, became more distrustful of political institutions, while their participation fostered increased social trust. This outcome differs from the intentions of the Estonian institutions which organized the ECA and sheds new light on the role of democratic innovations in the context of legitimacy crises. This is an important step forward in the scholarly understanding of the relationship between democratic innovation and trust….(More)”.
Cities, crowding, and the coronavirus: Predicting contagion risk hotspots
Paper by Gaurav Bhardwaj et al: “Today, over 4 billion people around the world—more than half the global population—live in cities. By 2050, with the urban population more than doubling its current size, nearly 7 of 10 people in the world will live in cities. Evidence from today’s developed countries and rapidly emerging economies shows that urbanization and the development of cities is a source of dynamism that can lead to enhanced productivity. In fact, no country in the industrial age has ever achieved significant economic growth without urbanization.
The underlying driver of this dynamism is the ability of cities to bring people together. Social and economic interactions are the hallmark of city life, making people more productive and often creating a vibrant market for innovations by entrepreneurs and investors. International evidence suggests that the elasticity of income per capita with respect to city population is between 3% and 8% (Rosenthal & Strange 2003). Each doubling of city size raises its productivity by 5%.
But the coronavirus pandemic is now seriously limiting social interactions. With no vaccine available, prevention through containment and social distancing, along with frequent handwashing, appear to be, for now, the only viable strategies against the virus. The goal is to slow transmission and avoid overwhelming health systems that have finite resources. Hence non-essential businesses have been closed and social distancing measures, including lockdowns, are being applied in many countries. Will such measures defeat the virus in dense urban areas? In principle, yes. Wealthier people in dense neighborhoods can isolate themselves while having amenities and groceries delivered to them. Many can connect remotely to work, and some can even afford to live without working for a time. But poorer residents of crowded neighborhoods cannot afford such luxuries.
To help city leaders prioritize resources towards places with the highest exposure and contagion risk, we have developed a simple methodology that can be rapidly deployed. This methodology identifies hotspots for contagion and vulnerability, based on:
– The practical inability for keeping people apart, based on a combination of population density and livable floor space that does not allow for 2 meters of physical distancing.
– Conditions where, even under lockdown, people might have little option but to cluster (e.g., to access public toilets and water pumps)…(More)”.
Tool for Surveillance or Spotlight on Inequality? Big Data and the Law
Paper by Rebecca A. Johnson and Tanina Rostain: “The rise of big data and machine learning is a polarizing force among those studying inequality and the law. Big data and tools like predictive modeling may amplify inequalities in the law, subjecting vulnerable individuals to enhanced surveillance. But these data and tools may also serve an opposite function, shining a spotlight on inequality and subjecting powerful institutions to enhanced oversight. We begin with a typology of the role of big data in inequality and the law. The typology asks questions—Which type of individual or institutional actor holds the data? What problem is the actor trying to use the data to solve?—that help situate the use of big data within existing scholarship on law and inequality. We then highlight the dual uses of big data and computational methods—data for surveillance and data as a spotlight—in three areas of law: rental housing, child welfare, and opioid prescribing. Our review highlights asymmetries where the lack of data infrastructure to measure basic facts about inequality within the law has impeded the spotlight function….(More)”.
The Principle of Self-Selection in Crowdsourcing Contests – Theory and Evidence
Paper by Nikolaus Franke, Kathrin Reinsberger and Philipp Topic: “Self-selection has been portrayed to be one of the core reasons for the stunning success of crowdsourcing. It is widely believed that among the mass of potential problem solvers particularly those individuals decide to participate who have the best problem-solving capabilities with regard to the problem at question. Extant research assumes that this self-selection effect is beneficial based on the premise that self-selecting individuals know more about their capabilities and knowledge than the publisher of the task – which frees the organization from costly and error-prone active search.
However, the effectiveness of this core principle has hardly been analyzed, probably because it is extremely difficult to investigate characteristics of those individuals who self-select out. In a unique research design in which we overcome these difficulties by combining behavioral data from a real crowdsourcing contest with data from a survey and archival data, we find that self-selection is actually working in the right direction. Those with particularly strong problem-solving capabilities tend to self-select into the contest and those with low capabilities tend to self-select out. However, this self-selection effect is much weaker than assumed and thus much potential is being lost. This suggests that much more attention needs to be paid to the early stages of crowdsourcing contests and particularly to those the hitherto almost completely overlooked individuals who could provide great solutions but self-select out.”…(More)”.
Public perceptions on data sharing: key insights from the UK and the USA
Paper by Saira Ghafur, Jackie Van Dael, Melanie Leis and Ara Darzi, and Aziz Sheikh: “Data science and artificial intelligence (AI) have the potential to transform the delivery of health care. Health care as a sector, with all of the longitudinal data it holds on patients across their lifetimes, is positioned to take advantage of what data science and AI have to offer. The current COVID-19 pandemic has shown the benefits of sharing data globally to permit a data-driven response through rapid data collection, analysis, modelling, and timely reporting.
Despite its obvious advantages, data sharing is a controversial subject, with researchers and members of the public justifiably concerned about how and why health data are shared. The most common concern is privacy; even when data are (pseudo-)anonymised, there remains a risk that a malicious hacker could, using only a few datapoints, re-identify individuals. For many, it is often unclear whether the risks of data sharing outweigh the benefits.
A series of surveys over recent years indicate that the public holds a range of views about data sharing. Over the past few years, there have been several important data breaches and cyberattacks. This has resulted in patients and the public questioning the safety of their data, including the prospect or risk of their health data being shared with unauthorised third parties.
We surveyed people across the UK and the USA to examine public attitude towards data sharing, data access, and the use of AI in health care. These two countries were chosen as comparators as both are high-income countries that have had substantial national investments in health information technology (IT) with established track records of using data to support health-care planning, delivery, and research. The UK and USA, however, have sharply contrasting models of health-care delivery, making it interesting to observe if these differences affect public attitudes.
Willingness to share anonymised personal health information varied across receiving bodies (figure). The more commercial the purpose of the receiving institution (eg, for an insurance or tech company), the less often respondents were willing to share their anonymised personal health information in both the UK and the USA. Older respondents (≥35 years) in both countries were generally less likely to trust any organisation with their anonymised personal health information than younger respondents (<35 years)…
Despite the benefits of big data and technology in health care, our findings suggest that the rapid development of novel technologies has been received with concern. Growing commodification of patient data has increased awareness of the risks involved in data sharing. There is a need for public standards that secure regulation and transparency of data use and sharing and support patient understanding of how data are used and for what purposes….(More)”.
The Shortcomings of Transparency for Democracy
Paper by Michael Schudson: “Transparency” has become a widely recognized, even taken for granted, value in contemporary democracies, but this has been true only since the 1970s. For all of the obvious virtues of transparency for democracy, they have not always been recognized or they have been recognized, as in the U.S. Freedom of Information Act of 1966, with significant qualifications. This essay catalogs important shortcomings of transparency for democracy, as when it clashes with national security, personal privacy, and the importance of maintaining the capacity of government officials to talk frankly with one another without fear that half-formulated ideas, thoughts, and proposals will become public. And when government information becomes public, that does not make it equally available to all—publicity is not in itself democratic, as public information (as in open legislative committee hearings) is more readily accessed by empowered groups with lobbyists able to attend and monitor the provision of the information. Transparency is an element in democratic government, but it is by no means a perfect emblem of democracy….(More)”.
The Open Innovation in Science research field: a collaborative conceptualisation approach
Paper by Susanne Beck et al: “Openness and collaboration in scientific research are attracting increasing attention from scholars and practitioners alike. However, a common understanding of these phenomena is hindered by disciplinary boundaries and disconnected research streams. We link dispersed knowledge on Open Innovation, Open Science, and related concepts such as Responsible Research and Innovation by proposing a unifying Open Innovation in Science (OIS) Research Framework. This framework captures the antecedents, contingencies, and consequences of open and collaborative practices along the entire process of generating and disseminating scientific insights and translating them into innovation. Moreover, it elucidates individual-, team-, organisation-, field-, and society‐level factors shaping OIS practices. To conceptualise the framework, we employed a collaborative approach involving 47 scholars from multiple disciplines, highlighting both tensions and commonalities between existing approaches. The OIS Research Framework thus serves as a basis for future research, informs policy discussions, and provides guidance to scientists and practitioners….(More)”.
How behavioural sciences can promote truth, autonomy and democratic discourse online
Philipp Lorenz-Spreen, Stephan Lewandowsky, Cass R. Sunstein & Ralph Hertwig in Nature: “Public opinion is shaped in significant part by online content, spread via social media and curated algorithmically. The current online ecosystem has been designed predominantly to capture user attention rather than to promote deliberate cognition and autonomous choice; information overload, finely tuned personalization and distorted social cues, in turn, pave the way for manipulation and the spread of false information. How can transparency and autonomy be promoted instead, thus fostering the positive potential of the web? Effective web governance informed by behavioural research is critically needed to empower individuals online. We identify technologically available yet largely untapped cues that can be harnessed to indicate the epistemic quality of online content, the factors underlying algorithmic decisions and the degree of consensus in online debates. We then map out two classes of behavioural interventions—nudging and boosting— that enlist these cues to redesign online environments for informed and autonomous choice….(More)”.
Measuring Movement and Social Contact with Smartphone Data: A Real-Time Application to Covid-19
Paper by Victor Couture et al: “Tracking human activity in real time and at fine spatial scale is particularly valuable during episodes such as the COVID-19 pandemic. In this paper, we discuss the suitability of smartphone data for quantifying movement and social contact. We show that these data cover broad sections of the US population and exhibit movement patterns similar to conventional survey data. We develop and make publicly available a location exposure index that summarizes county-to-county movements and a device exposure index that quantifies social contact within venues. We use these indices to document how pandemic-induced reductions in activity vary across people and places….(More)”.
Monitoring Corruption: Can Top-down Monitoring Crowd-Out Grassroots Participation?
Paper by Robert M Gonzalez, Matthew Harvey and Foteini Tzachrista: “Empirical evidence on the effectiveness of grassroots monitoring is mixed. This paper proposes a previously unexplored mechanism that may explain this result. We argue that the presence of credible and effective top-down monitoring alternatives can undermine citizen participation in grassroots monitoring efforts. Building on Olken’s (2009) road-building field experiment in Indonesia; we find a large and robust effect of the participation interventions on missing expenditures in villages without an audit in place. However, this effect vanishes as soon as an audit is simultaneously implemented in the village. We find evidence of crowding-out effects: in government audit villages, individuals are less likely to attend, talk, and actively participate in accountability meetings. They are also significantly less likely to voice general problems, corruption-related problems, and to take serious actions to address these problems. Despite policies promoting joint implementation of top-down and bottom-up interventions, this paper shows that top-down monitoring can undermine rather than complement grassroots efforts….(More)”.