Paper by Holger Straßheim: “Some argue that the global rise of behavioral approaches challenges the rationalist tradition in public policy. Others fear that it could undermine deliberation and public reasoning. This paper focuses on the worldwide rise and spread of behavioral expertise and behavioral public policy. It provides a general insight in terms of the role of expertise, the science-policy nexus and the distribution of epistemic competences in public policy. Based on an extensive literature review, the emergence and consequences of behavioral-expert networks are assessed. It will be suggested that it is necessary to break free from the microfocus proposed by behavioral public policy and to pay more attention to the institutional and cultural constellations of knowledge- and decision-making in democracies….(More)”.
Data Sharing in the Context of Health-Related Citizen Science
Paper by Mary A. Majumder and Amy L. McGuire: “As citizen science expands, questions arise regarding the applicability of norms and policies created in the context of conventional science. This article focuses on data sharing in the conduct of health-related citizen science, asking whether citizen scientists have obligations to share data and publish findings on par with the obligations of professional scientists. We conclude that there are good reasons for supporting citizen scientists in sharing data and publishing findings, and we applaud recent efforts to facilitate data sharing. At the same time, we believe it is problematic to treat data sharing and publication as ethical requirements for citizen scientists, especially where there is the potential for burden and harm without compensating benefit…(More)”.
Capturing Citizens’ Information Needs through Analysis of Public Library Circulation Data
Paper by Tomoya Igarashi, Masanori Koizumi and Michael Widdersheim: “The Japanese government has initiated lifelong learning policies to promote lifelong learning to a super-aging society. It is said that lifelong learning contributes to a richer and more fulfilling life. It is within this context that public libraries have been identified as ideal facilities for promoting lifelong learning. To support lifelong learning successfully, libraries must accurately grasp citizens’ needs, all while working within limited budgets. To understand citizens’ learning needs, this study uses public library circulation data. This study is significant because such data use is often unavailable in Japan. This data was used to clarify citizens’ learning interests. Circulation data was compared from two libraries in Japan: Koto District Library in Tokyo and Tahara City Library in Aichi Prefecture. The data was used to identify general learning needs while also accounting for regional differences. The methodology and results of this research are significant for the development of lifelong learning policy and programming….(More)”.
Digital tools against COVID-19: Framing the ethical challenges and how to address them
Paper by Urs Gasser et al: “Data collection and processing via digital public health technologies are being promoted worldwide by governments and private companies as strategic remedies for mitigating the COVID-19 pandemic and loosening lockdown measures. However, the ethical and legal boundaries of deploying digital tools for disease surveillance and control purposes are unclear, and a rapidly evolving debate has emerged globally around the promises and risks of mobilizing digital tools for public health. To help scientists and policymakers navigate technological and ethical uncertainty, we present a typology of the primary digital public health applications currently in use. Namely: proximity and contact tracing, symptom monitoring, quarantine control, and flow modeling. For each, we discuss context-specific risks, cross-sectional issues, and ethical concerns. Finally, in recognition of the need for practical guidance, we propose a navigation aid for policymakers made up of ten steps for the ethical use of digital public health tools….(More)”.
How to Make the Perfect Citizen? Lessons from China’s Model of Social Credit System
Paper by Liav Orgad and Wessel Reijers: “The COVID19 crisis has triggered a new wave of digitalization of the lives of citizens. To counter the devastating effects of the virus, states and corporations are experimenting with systems that trace citizens as an integral part of public life. In China, a comprehensive sociotechnical system of citizenship governance has already in force with the implementation of the Social Credit System—a technology-driven project that aims to assess, evaluate, and steer the behavior of Chinese citizens.
After presenting social credit systems in China’s public and private sectors (Part I), the article provides normative standards to distinguish the Chinese system from comparable Western systems (Part II). It then shows the manner in which civic virtue is instrumentalized in China, both in content (“what” it is) and in form (“how” to cultivate it) (Part III), and claims that social credit systems represent a new form of citizenship governance, “cybernetic citizenship,” which implements different conceptions of state power, civic virtue, and human rights (Part V). On the whole, the article demonstrates how the Chinese Social Credit System redefines the institution of citizenship and warns against similar patterns that are mushrooming in the West.
The article makes three contributions: empirically, it presents China’s Social Credit Systems and reveals their data sources, criteria used, rating methods, and attached sanctions and rewards. Comparatively, it shows that, paradoxically, China’s Social Credit System is not fundamentally different than credit systems in Western societies, yet indicates four points of divergence: scope, authority, regulation, and regime. Normatively, it claims that China’s Social Credit System creates a form of cybernetic citizenship governance, which redefines the essence of citizenship….(More)”
Rethinking Nudge: An Information-Costs Theory of Default Rules
Paper by Oren Bar-Gill and Omri Ben-Shahar: “Policymakers and scholars – both lawyers and economists – have long been pondering the optimal design of default rules. From the classic works on “mimicking” defaults for contracts and corporations to the modern rush to set “sticky” default rules to promote policies as diverse as organ donations, retirement savings, consumer protection, and data privacy, the optimal design of default rules has featured as a central regulatory challenge. The key element driving the design is opt-out costs—how to minimize them, or alternatively how to raise them to make the default sticky. Much of the literature has focused on “mechanical” opt-out costs—the effort people incur to select a non-default alternative. This focus is too narrow. A more important factor affecting opt-out is information—the knowledge people must acquire to make informed opt-out decisions. But, unlike high mechanical costs, high information costs need not make defaults stickier; they may instead make the defaults “slippery.”
This counterintuitive claim is due to the phenomenon of uninformed opt-out, which we identify and characterize. Indeed, the importance of uninformed opt-out requires a reassessment of the conventional wisdom about Nudge and asymmetric or libertarian paternalism. We also show that different defaults provide different incentives to acquire the information necessary for informed optout. With the ballooning use of default rules as a policy tool, our information-costs theory provides valuable guidance to policymakers….(More)”.
COVID-19 Outbreak Prediction with Machine Learning
Paper by Sina F. Ardabili et al: “Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models needs to be improved.
This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to SIR and SEIR models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP, and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. Paper further suggests that real novelty in outbreak prediction can be realized through integrating machine learning and SEIR models….(More)”.
Conceptualizing Data‐Deliberation: The Starry Sky Beetle, Environmental System Risk, and Habermasian CSR in the Digital Age
Paper by Mario Schultz and Peter Seele: “Building on an illustrative case of a systemic environmental threat and its multi‐stakeholder response, this paper draws attention to the changing political impacts of corporations in the digital age. Political Corporate Social Responsibility (PCSR) theory suggests an expanded sense of politics and corporations, including impacts that may range from voluntary initiatives to overcome governance gaps, to avoiding state regulation via corporate political activity. Considering digitalization as a stimulus, we explore potential responsibilities of corporations toward public goods in contexts with functioning governments. We show that digitalization—in the form of transparency, surveillance, and data‐sharing—offers corporations’ scope for deliberative public participation.
The starry sky beetle infestation endangering public and private goods is thereby used to illustrate the possibility of expanding the political role of corporations in the digital sphere. We offer a contribution by conceptualizing data‐deliberation as a Habermasian variation of PCSR, defined as the (a) voluntary disclosure of corporate data and its transparent, open sharing with the public sector (b) along with the cooperation with governmental institutions on data analytics methods for examining large‐scale datasets (c) thereby complying with existing national and international regulations on data protection, in particular with respect to privacy and personal data….(More)”.
The tricky math of lifting coronavirus lockdowns
James Temple at MIT Technology Review: “…A crucial point of the work—which Steinhardt and MIT’s Andrew Ilyas wrote up in a draft paper that hasn’t yet been published or peer-reviewed—is that communities need to get much better at tracking infections. “With the data we currently have, we actually just don’t know what the level of safe mobility is,” Steinhardt says. “We need much better mechanisms for tracking prevalence in order to do any of this safely.”
The analysis relies on other noisy and less-than-optimal measurements as well, including using hospitalization admissions and deaths to estimate disease prevalence before the lockdowns. They also had to make informed assumptions, which others might disagree with, about how much shelter-in-place rules have altered the spread of the disease. Much of the overall uncertainty is due to the spottiness of testing to date. If case counts are rising, but so is testing, it’s difficult to decipher whether infections are still increasing or a greater proportion of infected people are being evaluated.
This produces some confusing results in the study for any policymaker looking for clear direction. Notably, in Los Angeles, the estimated growth rate of the disease since the shelter-in-place order went into effect ranges from negative to positive. This suggests either that the city could start loosening restrictions or that it needs to tighten them further.
Ultimately, the researchers stress that communities need to build up disease surveillance measures to reduce this uncertainty, and strike an appropriate balance between reopening the economy and minimizing public health risks.
They propose several ways to do so, including conducting virological testing on a random sample of some 20,000 people per day in a given area; setting up wide-scale online surveys that ask people to report potential symptoms, similar to what Carnegie Mellon researchers are doing through efforts with both Facebook and Google; and potentially testing for the prevalence of viral material in wastewater, a technique that has “sounded the alarm” on polio outbreaks in the past.
A team of researchers affiliated with MIT, Harvard, and startup Biobot Analytics recently analyzed water samples from a Massachusetts treatment facility, and detected levels of the coronavirus that were “significantly higher” than expected on the basis of confirmed cases in the state, according to a non-peer-reviewed paper released earlier this month….(More)”.
Crowdsourcing a crisis response for COVID-19 in oncology
Aakash Desai et al in Nature Medicine: “Crowdsourcing efforts are currently underway to collect and analyze data from patients with cancer who are affected by the COVID-19 pandemic. These community-led initiatives will fill key knowledge gaps to tackle crucial clinical questions on the complexities of infection with the causative coronavirus SARS-Cov-2 in the large, heterogeneous group of vulnerable patients with cancer…(More)”