Stefaan Verhulst
Article by Theresa Crimmins, Erin Posthumus, and Kathleen Prudic: “The rapid spread of COVID-19 in 2020 disrupted field research and environmental monitoring efforts worldwide. Travel restrictions and social distancing forced scientists to cancel studies or pause their work for months. These limits measurably reduced the accuracy of weather forecasts and created data gaps on issues ranging from bird migration to civil rights in U.S. public schools.
Our work relies on this kind of information to track seasonal events in nature and understand how climate change is affecting them. We also recruit and train citizens for community science – projects that involve amateur or volunteer scientists in scientific research, also known as citizen science. This often involves collecting observations of phenomena such as plants and animals, daily rainfall totals, water quality or asteroids.
Participation in many community science programs has skyrocketed during COVID-19 lockdowns, with some programs reporting record numbers of contributors. We believe these efforts can help to offset data losses from the shutdown of formal monitoring activities….(More)”.
InterAgency Standing Committee: “Data responsibility in humanitarian action is the safe, ethical and effective management of personal and non-personal data for operational response. It is a critical issue for the humanitarian system to address and the stakes are high. Ensuring we ‘do no harm’ while maximizing the benefits of data requires collective action that extends across all levels of the humanitarian system. Humanitarians must be careful when handling data to avoid placing already vulnerable individuals and communities at further risk. This is especially important in contexts where the urgency of humanitarian needs drives pressure for fast, sometimes untested, data solutions, and the politicization of data can have more extreme consequences for people.
The implementation of data responsibility in practice is often inconsistent within and across humanitarian response contexts. This is true despite established principles, norms and professional standards regarding respect for the rights of affected populations; the range of resources on data responsibility available in the wider international data community; as well as significant efforts by many humanitarian organizations to develop and update their policies and guidance in this area. However, given that the humanitarian data ecosystem is inherently interconnected, no individual organization can tackle all these challenges alone.
This system-wide Operational Guidance, which is a first, will ensure concrete steps for data responsibility in all phases of humanitarian action. It is the result of an inclusive and consultative process, involving more than 250 stakeholders from the humanitarian sector. Partners across the system will implement these guidelines in accordance with their respective mandates and the decisions of their governing bodies….(More)”
Paper by Sophie Stalla-Bourdillon, Laura Carmichael and Alexsis Wintour: “Independent data stewardship remains a core component of good data governance practice. Yet, there is a need for more robust independent data stewardship models that are able to oversee data-driven, multi-party data sharing, usage and re-usage, which can better incorporate citizen representation, especially in relation to personal data. We propose that data foundations—inspired by Channel Islands’ foundations laws—provide a workable model for good data governance not only in the Channel Islands, but also elsewhere. A key advantage of this model—in addition to leveraging existing legislation and building on established precedent—is the statutory role of the guardian that is a unique requirement in the Channel Islands, and when interpreted in a data governance model provides the independent data steward. The principal purpose for this paper, therefore, is to demonstrate why data foundations are well suited to the needs of data sharing initiatives. We further examine how data foundations could be established in practice—and provide key design principles that should be used to guide the design and development of any data foundation….(More)”.
Open Access Book by Ken Steif: “… teaches readers how to address complex public policy problems with data and analytics using reproducible methods in R. Each of the eight chapters provides a detailed case study, showing readers: how to develop exploratory indicators; understand ‘spatial process’ and develop spatial analytics; how to develop ‘useful’ predictive analytics; how to convey these outputs to non-technical decision-makers through the medium of data visualization; and why, ultimately, data science and ‘Planning’ are one and the same. A graduate-level introduction to data science, this book will appeal to researchers and data scientists at the intersection of data analytics and public policy, as well as readers who wish to understand how algorithms will affect the future of government….(More)”.
Book edited by Daniel Moeckli, Anna Forgács, and Henri Ibi: “With the rise of direct-democratic instruments, the relationship between popular sovereignty and the rule of law is set to become one of the defining political issues of our time. This important and timely book provides an in-depth analysis of the limits imposed on referendums and citizens’ initiatives, as well as of systems of reviewing compliance with these limits, in 11 European states.
Chapters explore and lay the scientific basis for answering crucial questions such as ‘Where should the legal limits of direct democracy be drawn?’ and ‘Who should review compliance with these limits?’ Providing a comparative analysis of the different issues in the selected countries, the book draws out key similarities and differences, as well as an assessment of the law and the practice at national levels when judged against the international standards contained in the Venice Commission’s Guidelines on the Holding of Referendums.
Presenting an up-to-date analysis of the relationship between popular sovereignty and the rule of law, The Legal Limits of Direct Democracy will be a key resource for scholars and students in comparative and constitutional law and political science. It will also be beneficial to policy-makers and practitioners in parliaments, governments and election commissions, and experts working for international organisations….(More)”.
Paper by Vasileios Lampos et al: “Previous research has demonstrated that various properties of infectious diseases can be inferred from online search behaviour. In this work we use time series of online search query frequencies to gain insights about the prevalence of COVID-19 in multiple countries. We first develop unsupervised modelling techniques based on associated symptom categories identified by the United Kingdom’s National Health Service and Public Health England. We then attempt to minimise an expected bias in these signals caused by public interest—as opposed to infections—using the proportion of news media coverage devoted to COVID-19 as a proxy indicator. Our analysis indicates that models based on online searches precede the reported confirmed cases and deaths by 16.7 (10.2–23.2) and 22.1 (17.4–26.9) days, respectively. We also investigate transfer learning techniques for mapping supervised models from countries where the spread of the disease has progressed extensively to countries that are in earlier phases of their respective epidemic curves. Furthermore, we compare time series of online search activity against confirmed COVID-19 cases or deaths jointly across multiple countries, uncovering interesting querying patterns, including the finding that rarer symptoms are better predictors than common ones. Finally, we show that web searches improve the short-term forecasting accuracy of autoregressive models for COVID-19 deaths. Our work provides evidence that online search data can be used to develop complementary public health surveillance methods to help inform the COVID-19 response in conjunction with more established approaches….(More)”.
Ray Eitel-Porter at AI and Ethics: “Artificial Intelligence (AI) applications can and do have unintended negative consequences for businesses if not implemented with care. Specifically, faulty or biased AI applications risk compliance and governance breaches and damage to the corporate brand. These issues commonly arise from a number of pitfalls associated with AI development, which include rushed development, a lack of technical understanding, and improper quality assurance, among other factors. To mitigate these risks, a growing number of organisations are working on ethical AI principles and frameworks. However, ethical AI principles alone are not sufficient for ensuring responsible AI use in enterprises. Businesses also require strong, mandated governance controls including tools for managing processes and creating associated audit trails to enforce their principles. Businesses that implement strong governance frameworks, overseen by an ethics board and strengthened with appropriate training, will reduce the risks associated with AI. When applied to AI modelling, the governance will also make it easier for businesses to bring their AI deployments to scale….(More)”.
Jean-Claude Burgelman at Data Intelligence: “This article will document how the European Open Science Cloud (EOSC) emerged as one of the key policy intentions to foster Open Science (OS) in Europe. It will describe some of the typical, non-rational roadblocks on the way to implement EOSC. The article will also argue that the only way Europe can take care of its research data in a way that fits the European specificities fully, is by supporting EOSC.
It is fair to say—note the word FAIR here—that realizing the European Open Science Cloud (EOSC) is now part and parcel of the European Data Science (DS) policy. In particular since EOSC will be from 2021 in the hands of the independent EOSC Association and thus potentially way out of the so-called “Brussels Bubble”.
This article will document the whole story of how EOSC emerged in this “bubble” as one of the policy intentions to foster Open Science (OS) in Europe. In addition, it will describe some of the typical, non-rational roadblocks on the way to implement EOSC. The article will also argue that the only way Europe can take care of its research data in a way that fits the European specificities fully, is by supporting EOSC….(More)”
Paper by Henry Farrell and Abraham L. Newman: “Scholars and policymakers long believed that norms of global information openness and private-sector governance helped to sustain and promote liberalism. These norms are being increasingly contested within liberal democracies. In this article, we argue that a key source of debate over the Liberal International Information Order (LIIO), a sub-order of the Liberal International Order (LIO), is generated internally by “self-undermining feedback effects,” that is, mechanisms through which institutional arrangements undermine their own political conditions of survival over time. Empirically, we demonstrate how global governance of the Internet, transnational disinformation campaigns, and domestic information governance interact to sow the seeds of this contention. In particular, illiberal states converted norms of openness into a vector of attack, unsettling political bargains in liberal states concerning the LIIO. More generally, we set out a broader research agenda to show how the international relations discipline might better understand institutional change as well as the informational aspects of the current crisis in the LIO….(More)”
MIT Technology Review: “Google Street View has become a surprisingly useful way to learn about the world without stepping into it. People use it to plan journeys, to explore holiday destinations, and to virtually stalk friends and enemies alike.
But researchers have found more insidious uses. In 2017 a team of researchers used the images to study the distribution of car types in the US and then used that data to determine the demographic makeup of the country. It turns out that the car you drive is a surprisingly reliable proxy for your income level, your education, your occupation, and even the way you vote in elections.
Now a different group has gone even further. Łukasz Kidziński at Stanford University in California and Kinga Kita-Wojciechowska at the University of Warsaw in Poland have used Street View images of people’s houses to determine how likely they are to be involved in a car accident. That’s valuable information that an insurance company could use to set premiums.
The result raises important questions about the way personal information can leak from seemingly innocent data sets and whether organizations should be able to use it for commercial purposes.
Insurance data
The researchers’ method is straightforward. They began with a data set of 20,000 records of people who had taken out car insurance in Poland between 2013 and 2015. These were randomly selected from the database of an undisclosed insurance company.
Each record included the address of the policyholder and the number of damage claims he or she made during the 2013–’15 period. The insurer also shared its own prediction of future claims, calculated using its state-of-the-art risk model that takes into account the policyholder’s zip code and the driver’s age, sex, claim history, and so on.
The question that Kidziński and Kita-Wojciechowska investigated is whether they could make a more accurate prediction using a Google Street View image of the policyholder’s house….(More)”.