Stefaan Verhulst
OECD Working Paper: “This working paper introduces the key concepts and features of anticipatory innovation governance– i.e. the structures and mechanisms to allow and promote anticipatory innovation alongside other types of innovation in the public sector. This paper draws on academic literature and OECD work on a range of areas including public sector innovation, foresight, anticipatory governance and emerging technologies. The paper starts outlining an emerging framework to guide policy making in complex and uncertain contexts and sets out some questions for further research in the area of anticipatory innovation governance….(More)”
Paper by Shomik Jain, Davide Proserpio, Giovanni Quattrone, and Daniele Quercia: “There is a rumbling debate over the impact of gentrification: presumed gentrifiers have been the target of protests and attacks in some cities, while they have been welcome as generators of new jobs and taxes in others. Census data fails to measure neighborhood change in real-time since it is usually updated every ten years. This work shows that Airbnb data can be used to quantify and track neighborhood changes. Specifically, we consider both structured data (e.g. number of listings, number of reviews, listing information) and unstructured data (e.g. user-generated reviews processed with natural language processing and machine learning algorithms) for three major cities, New York City (US), Los Angeles (US), and Greater London (UK). We find that Airbnb data (especially its unstructured part) appears to nowcast neighborhood gentrification, measured as changes in housing affordability and demographics. Overall, our results suggest that user-generated data from online platforms can be used to create socioeconomic indices to complement traditional measures that are less granular, not in real-time, and more costly to obtain….(More)”.
James Hookway at the Wall Street Journal: “Switzerland’s system of direct democracy will be put to the test again later this year, this time with a referendum on whether to roll back the government’s powers to impose lockdowns and other measures to slow the Covid-19 pandemic.
The landlocked Alpine nation of 8.5 million people is unusual in providing its people a say on important policy moves by offering referendums if enough people sign a petition for a vote. Last year, Swiss voted on increasing the stock of low-cost housing, tax allowances for children and hunting wolves.
The idea is to provide citizens a check on the power of the federal government, and it is a throwback to the fiercely independent patchwork of cantons, or districts, that were meshed in the medieval period.
Now, the country is set for a referendum on whether to remove the government’s legal authority to order lockdowns and other pandemic restrictions after campaigners submitted a petition of some 86,000 signatures this week—higher than the 50,000 required—triggering a nationwide vote to repeal last year’s Covid-19 Act….(More)”.
Conference paper by Joshua Gelhaar et al: “In the increasingly interconnected business world, economic value is less and less created by one company alone but rather through the combination and enrichment of data by various actors in so-called data ecosystems. The research field around data ecosystems is, however, still in its infancy. With this study, we want to address this issue and contribute to a deeper understanding of data ecosystems. Therefore, we develop a taxonomy for data ecosystems which is grounded both theoretically through the linkage to the scientific knowledge base and empirically through the analyses of data ecosystem use cases. The resulting taxonomy consists of key dimensions and characteristics of data ecosystems and contributes to a better scientific understanding of this concept. Practitioners can use the taxonomy as an instrument to further understand, design and manage the data ecosystems their organizations are involved in….(More)”.
Essay by Jesse Hirsh: “The COVID-19 pandemic is accelerating global digital transformation and the adoption of digital technologies. It is also enacting a political and cultural shift toward a quantified society: a society in which measurement and predictive modelling dominate (political) decision making, and where surveillance is expansive and pervasive.
While viruses and disease have always been with us, what’s changing is our ability to measure and understand them. This ability comes at a time when globalization (and, by extension, climate change) has transformed the kinds of viruses and diseases we will face.
The knowledge of what can kill us — or is killing us — compels governments and health authorities to both take action in response and gather more data to understand the threat. Like many disasters or other globally impactful events, the COVID-19 pandemic is accelerating the development and implementation of quantification technologies.
Health researchers are now measuring the spread of a virus across the population in ways not previously possible, through the use of a set of data that is ever-growing, especially in countries such as China that have less regard for personal privacy. Canada and the United States are not yet conducting tracking and tracing of infections at a level that would enable containment. This level, however, is due to inadequate staffing rather than insufficient data. Still, the desire for more information remains.
As a result, our ability to measure human health and disease transmission is set to reach new records and capabilities. Through sources ranging from individuals’ use of digital health tools to contact tracing records, health-related data is amassing at a prodigious rate.
What are the impacts or consequences of this dramatic increase in both health data and the perceived value or urgency of that data?…(More)”.
Image credit: Kayla Velasquez/Unsplash.
Selected Readings by Fiona Cece, Uma Kalkar, and Stefaan Verhulst: “The mob attack on the US Congress was alarming and the result of various efforts to undermine the trust in and legitimacy of longstanding democratic processes and institutions. In particular, the use of inaccurate data, half-truths, and disinformation to spread hate and division is considered a key driver behind last week’s attack. Altering data to support conspiracy theories or challenging and undermining the credibility of trusted data sources to allow for alternative narratives to flourish, if left unchallenged, has consequences — including the increased acceptance and use of violence both off-line and on-line.
Everyone working on data and information needs to be aware of the implications of altering or misusing data (including election results) to support malicious objectives. The January 6th riot is unfortunately not a unique event, nor is it contained to the US. Below, we provide a curation of findings and readings that illustrate the global danger of inaccurate data, half-truths, and willful disinformation….(Readings)”.
World Economic Forum: “…Human-centricity is a game changer in terms of unleashing the latent potential in data to empower societies. A human-centric shift in viewpoint is a simple, yet profound, change in thinking. It offers ground-breaking consequences for the availability, usability and quality of data for any person or organization, public or private. Far from being in conflict with the aims of business, human-centricity can drive new paradigms and ideas for innovation, capitalizing on the possibilities that better data availability can unleash for societies…
Organization-centric approaches turn data into resources for companies (business), control what organizations can and cannot do with data (regulation) and aim to enable the most efficient possible data utilization for data-controlling organizations (technology) — without fully taking into account the interests of people of society. In contast, a human-centric approach looks at data as it relates to people about whom that
data exists — while still accounting for the interests of business. Human-centric business models (be they B2C, B2B, or B2G) take people’s and societies’ interests as a guiding principle; regulation is designed in a way that assumes and guarantees that data about people is being used for their and their communities’ benefit, and technology prioritizes humane values (including social and ecological ones)….(More)”.
Brief by Frida Polli et al: “The notion of studying people in jobs as a science—in fields such as human resource management, people analytics, and industrial-organizational psychology—dates back to at least the early 20th century. In 1919, Yale psychologist Henry Charles Link wrote, “The application of science to the problem of employment is just beginning to receive serious attention,” at last providing an alternative to the “hire and fire” methods of 19th-century employers. A year later, prominent organizational theorists Ordway Teal and Henry C. Metcalf claimed, “The new focus in administration is to be the human element. The new center of attention and solicitude is the individual person, the worker.” The overall conclusion at the time was that various social and psychological factors governed differences in employee productivity and satisfaction….This Brief Proceeds in Five Sections:
● First, we review the limitations of traditional approaches to people science. In particular, we focus on four needs of the modern employer that are not satisfied by the status quo: job fit, soft skills, fairness, and flexibility.
● Second, we present the foundations of a new people science by explaining how advancements in fields like cognitive science and neuroscience can be used to understand the individual differences between humans.
● Third, we describe four best practices that should govern the application of the new people science theories to real-world employment contexts.
● Fourth, we present a case study of how one platform company has used the new people science to create hiring models for five high-growth roles.● Finally, we explain how the type of insights presented in Section IV can be made actionable in the context of retraining employees for the future of work….(More)”.
Book edited by John McDaniel and Ken Pease: “This edited text draws together the insights of numerous worldwide eminent academics to evaluate the condition of predictive policing and artificial intelligence (AI) as interlocked policy areas. Predictive and AI technologies are growing in prominence and at an unprecedented rate. Powerful digital crime mapping tools are being used to identify crime hotspots in real-time, as pattern-matching and search algorithms are sorting through huge police databases populated by growing volumes of data in an eff ort to identify people liable to experience (or commit) crime, places likely to host it, and variables associated with its solvability. Facial and vehicle recognition cameras are locating criminals as they move, while police services develop strategies informed by machine learning and other kinds of predictive analytics. Many of these innovations are features of modern policing in the UK, the US and Australia, among other jurisdictions.
AI promises to reduce unnecessary labour, speed up various forms of police work, encourage police forces to more efficiently apportion their resources, and enable police officers to prevent crime and protect people from a variety of future harms. However, the promises of predictive and AI technologies and innovations do not always match reality. They often have significant weaknesses, come at a considerable cost and require challenging trade- off s to be made. Focusing on the UK, the US and Australia, this book explores themes of choice architecture, decision- making, human rights, accountability and the rule of law, as well as future uses of AI and predictive technologies in various policing contexts. The text contributes to ongoing debates on the benefits and biases of predictive algorithms, big data sets, machine learning systems, and broader policing strategies and challenges.
Written in a clear and direct style, this book will appeal to students and scholars of policing, criminology, crime science, sociology, computer science, cognitive psychology and all those interested in the emergence of AI as a feature of contemporary policing….(More)”.
Paper by David Andrew Logan: “New York Times v. Sullivan (1964) is an iconic decision, foundational to modern First Amendment theory, and in a string of follow-on decisions the Court firmly grounded free speech theory and practice in the need to protect democratic discourse. To do this the Court provided broad and deep protections to the publishers of falsehoods. This article recognizes that New York Times and its progeny made sense in the “public square” of an earlier era, but the justices could never have foreseen the dramatic changes in technology and the media environment in the years since, nor predict that by making defamation cases virtually impossible to win they were harming, rather than helping self-government. In part because of New York Times, the First Amendment has been weaponized, frustrating a basic requirement of a healthy democracy: the development of a set of broadly agreed-upon facts. Instead, we are subject to waves of falsehoods that swamp the ability of citizens to effectively self-govern. As a result, and despite its iconic status, New York Times needs to be reexamined and retooled to better serve our democracy….(More)”