Digital Privacy for Reproductive Choice in the Post-Roe Era

Curated on Posted on by Stefaan Verhulst

Paper by Aziz Z. Huq and Rebecca Wexler: “The overruling of Roe v. Wade unleashed a torrent of regulatory and punitive activity restricting lawful reproductive options. The turn to the expansive criminal law and new schemes of civil liability creates new, and quite different, concerns from the pre-Roe landscape a half-century, ago. Reproductive choice, and its nemesis, rests on information. For pregnant people, deciding on a choice of medical care entails a search for advice and services. Information is at a premium for them. Meanwhile, efforts to regulate abortion begin with clinic closings, but quickly will extend to civil actions and criminal indictments of patients, providers, and those who facilitate abortions. Like the pregnant themselves, criminal and civil enforcers depend on information. And in the contemporary context, the informational landscape, and hence access to counseling and services such as medication abortion, is largely digital. In an era when most people use search engines or social media to access information, the digital architecture and data retention policies of those platforms will determine not only whether the pregnant can access medically accurate advice but also whether the mere act of doing so places them in legal peril.

This Article offers the first comprehensive accounting of abortion-related digital privacy after the end of Roe. It demonstrates first that digital privacy for pregnant persons in the United States has suddenly become a tremendously fraught and complex question. It then maps the treacherous social, legal and economic terrain upon which firms, individuals, and states will make privacy related decisions. Building on this political economy, we develop a moral and economic argument to the effect that digital firms should maximize digital privacy for pregnant persons within the scope of the law, and should actively resist restrictionist states’ efforts to instrumentalize them into their war on reproductive choice. We then lay out precise, tangible steps that firms should take to enact this active resistance, explaining in particular a range of powerful yet legal options for firms to refuse cooperation with restrictionist criminal and civil investigations. Finally, we present an original, concrete and immediately actionable proposal for federal and state legislative intervention: a statutory evidentiary privilege to shield abortion-relevant data from restrictionist warrants, subpoenas, court orders, and judicial proceedings…(More)”

The wealth of (Open Data) nations? Open government data, country-level institutions and entrepreneurial activity

Curated on Posted on by Stefaan Verhulst

Paper by Franz Huber, Alan Ponce, Francesco Rentocchini & Thomas Wainwright: “Lately, Open Data (OD) has been promoted by governments around the world as a resource to accelerate innovation within entrepreneurial ventures . However,it remains unclear to what extent OD drives innovative entrepreneurship. This paper sheds light on this open question by providing novel empirical evidence on the relationship between OD publishing and (digital) entrepreneurship at the country-level. We draw upon a longitudinal dataset comprising 90 countries observed over the period 2013–2016. We find a significant and positive association between OD publishing and entrepreneurship at the country level. The results also show that OD publishing and entrepreneurship is strong in countries with high institutional quality. We argue that publishing OD is not sufficient to improve innovative entrepreneurship alone, so states need to move beyond a focus on OD initiatives and promotion, to focus on a broader set of policy initiatives that promote good governance…(More)”.

Income Inequality Is Rising. Are We Even Measuring It Correctly?

Curated on Posted on by Stefaan Verhulst

Article by Jon Jachimowicz et al: “Income inequality is on the rise in many countries around the world, according to the United Nations. What’s more, disparities in global income were exacerbated by the COVID-19 pandemic, with some countries facing greater economic losses than others.

Policymakers are increasingly focusing on finding ways to reduce inequality to create a more just and equal society for all. In making decisions on how to best intervene, policymakers commonly rely on the Gini coefficient, a statistical measure of resource distribution, including wealth and income levels, within a population. The Gini coefficient measures perfect equality as zero and maximum inequality as one, with higher numbers indicating a greater concentration of resources in the hands of a few.

This measure has long dominated our understanding (pdf) of what inequality means, largely because this metric is used by governments around the world, is released by statistics bureaus in multiple countries, and is commonly discussed in news media and policy discussions alike.

In our paper, recently published in Nature Human Behaviour, we argue that researchers and policymakers rely too heavily on the Gini coefficient—and that by broadening our understanding of how we measure inequality, we can both uncover its impact and intervene to more effectively correct It…(More)”.

Nudging Science Towards Fairer Evaluations: Evidence From Peer Review

Curated on Posted on by Stefaan Verhulst

Paper by Inna Smirnova, Daniel M. Romero, and Misha Teplitskiy: “Peer review is widely used to select scientific projects for funding and publication, but there is growing evidence that it is biased towards prestigious individuals and institutions. Although anonymizing submissions can reduce prestige bias, many organizations do not implement anonymization, in part because enforcing it can be prohibitively costly. Here, we examine whether nudging but not forcing authors to anonymize their submissions reduces prestige bias. We partnered with IOP Publishing, one of the largest academic publishers, which adopted a policy strongly encouraging authors to anonymize their submissions and staggered the policy rollout across its physics journal portfolio. We examine 156,015 submissions to 57 peer-reviewed journals received between January 2018 and February 2022 and measure author prestige with citations accrued at submission time. Higher prestige first authors were less likely to anonymize. Nevertheless, for low-prestige authors, the policy increased positive peer reviews by 2.4% and acceptance by 5.6%. For middle- and high-prestige authors, the policy decreased positive reviews (1.8% and 1%) and final acceptance (4.6% and 2.2%). The policy did not have unintended consequences on reviewer recruitment or the characteristics of submitting authors. Overall, nudges are a simple, low-cost, and effective method to reduce prestige bias and should be considered by organizations for which enforced-anonymization is impractical…(More)”.

Blue Spoons: Sparking Communication About Appropriate Technology Use

Curated on Posted on by Stefaan Verhulst

Paper by Arun G. Chandrasekhar, Esther Duflo, Michael Kremer, João F. Pugliese, Jonathan Robinson & Frank Schilbach: “An enduring puzzle regarding technology adoption in developing countries is that new technologies often diffuse slowly through the social network. Two of the key predictions of the canonical epidemiological model of technology diffusion are that forums to share information and higher returns to technology should both spur social transmission. We design a large-scale experiment to test these predictions among farmers in Western Kenya, and we fail to find support for either. However, in the same context, we introduce a technology that diffuses very fast: a simple kitchen spoon (painted in blue) to measure out how much fertilizer to use. We develop a model that explains both the failure of the standard approaches and the surprising success of this new technology. The core idea of the model is that not all information is reliable, and farmers are reluctant to develop a reputation of passing along false information. The model and data suggest that there is value in developing simple, transparent technologies to facilitate communication…(More)”.

A journey toward an open data culture through transformation of shared data into a data resource

Curated on Posted on by Stefaan Verhulst

Paper by Scott D. Kahn and Anne Koralova: “The transition to open data practices is straightforward albeit surprisingly challenging to implement largely due to cultural and policy issues. A general data sharing framework is presented along with two case studies that highlight these challenges and offer practical solutions that can be adjusted depending on the type of data collected, the country in which the study is initiated, and the prevailing research culture. Embracing the constraints imposed by data privacy considerations, especially for biomedical data, must be emphasized for data outside of the United States until data privacy law(s) are established at the Federal and/or State level…(More).”

A little good goes an unexpectedly long way: Underestimating the positive impact of kindness on recipients.

Curated on Posted on by Stefaan Verhulst

Paper by Kumar, A., & Epley, N. : “Performing random acts of kindness increases happiness in both givers and receivers, but we find that givers systematically undervalue their positive impact on recipients. In both field and laboratory settings (Experiments 1a through 2b), those performing an act of kindness reported how positive they expected recipients would feel and recipients reported how they actually felt. From giving away a cup of hot chocolate in a park to giving away a gift in the lab, those performing a random act of kindness consistently underestimated how positive their recipients would feel, thinking their act was of less value than recipients perceived it to be. Givers’ miscalibrated expectations are driven partly by an egocentric bias in evaluations of the act itself (Experiment 3). Whereas recipients’ positive reactions are enhanced by the warmth conveyed in a kind act, givers’ expectations are relatively insensitive to the warmth conveyed in their action. Underestimating the positive impact of a random act of kindness also leads givers to underestimate the behavioral consequences their prosociality will produce in recipients through indirect reciprocity (Experiment 4). We suggest that givers’ miscalibrated expectations matter because they can create a barrier to engaging in prosocial actions more often in everyday life (Experiments 5a and 5b), which may result in people missing out on opportunities to enhance both their own and others’ well-being…(More)”

Nowcasting daily population displacement in Ukraine through social media advertising data

Curated on Posted on by Stefaan Verhulst

Pre-Publication Paper by Douglas R. Leasure et al: “In times of crisis, real-time data mapping population displacements are invaluable for targeted humanitarian response. The Russian invasion of Ukraine on February 24, 2022 forcibly displaced millions of people from their homes including nearly 6m refugees flowing across the border in just a few weeks, but information was scarce regarding displaced and vulnerable populations who remained inside Ukraine. We leveraged near real-time social media marketing data to estimate sub-national population sizes every day disaggregated by age and sex. Our metric of internal displacement estimated that 5.3m people had been internally displaced away from their baseline administrative region by March 14. Results revealed four distinct displacement patterns: large scale evacuations, refugee staging areas, internal areas of refuge, and irregular dynamics. While this innovative approach provided one of the only quantitative estimates of internal displacement in virtual real-time, we conclude by acknowledging risks and challenges for the future…(More)”.

Collective Intelligence

Curated on Posted on by Stefaan Verhulst

Editorial to the Inaugural Issue by Jessica Flack et al: “It is easy to see the potential of collective intelligence research to serve as a unifying force in the sciences. Its “nuts and bolts” methodological and conceptual questions apply across scales – how to characterize minimal and optimal algorithms for aggregating and storing information; how to derive macroscopic collective outputs from microscopic inputs; how to measure the robustness and vulnerability of collective outcomes, the design of algorithms for information aggregation; the role of diversity in forecasting and estimation; the dynamics of problem-solving in groups; team dynamics and complementary and synergistic roles; open innovation processes, and, more recently, the practical options for combining artificial and collective intelligence.

Despite this potential, the collective intelligence scholarly community is currently distributed over somewhat independent clusters of fields and research groups. We hope to bring these groups together. In this spirit, we will provide space for cross-cutting research aimed at principles of collective intelligence but also for field-specific research.

How should we understand the objectives of collective intelligence in different contexts? These can include identifying an object, making predictions, solving a problem, taking action, achieving an outcome, surviving in a dynamic environment, or a combination of these. Clarity on objectives is essential to measure or evaluate collective intelligence.

What can we learn about how collective intelligence addresses different types of problems, such as the characteristics of static, stochastic, and dynamic environments? For example, if stochastic, is the distribution of states best described as coming from a fixed distribution, as produced by a Markov Process, or as deeply uncertain? If a multi-agent system, to what extent do those entities cooperate or compete? What combinations of hierarchies and various forms of self-organization–such as markets, democracies, and communities–can align goals and coordinate actions?

What causes collective intelligence? How are the core processes needed for intelligence–such as sensing, deciding, and learning–performed in very different types of collective systems? What precisely is the relationship between diversity and collective intelligence (where the patterns are much more complex than often assumed)? Or the roles of synchrony and synergy in teams? What are some non-obvious patterns, such as how a slow learning rate among some population members maintains memory? What is the role of noise (as discussed in our first published dialogue), which, while harmful to the individual, can be potentially beneficial for the collective? When can a propensity for mistakes be helpful?

How should we understand the relationships between levels? For example, can aggregate or macroscale variables be derived from microscale interactions and mechanisms, or vice-versa?

Where does collective intelligence reside, and how is it “stored”—in individual heads, encoded in interaction networks and circuits, or embodied in the interaction of a group with its environment?

How are trade-offs handled in different contexts–speed and accuracy, focus and peripheral vision, exploration and exploitation?

These–and dozens of related questions–are relevant to many disciplines, and each may benefit from insights derived from others, particularly if we can develop common principles and concepts…(More)”.

Citizen science in environmental and ecological sciences

Curated on Posted on by Stefaan Verhulst

Paper by Dilek Fraisl et al: “Citizen science is an increasingly acknowledged approach applied in many scientific domains, and particularly within the environmental and ecological sciences, in which non-professional participants contribute to data collection to advance scientific research. We present contributory citizen science as a valuable method to scientists and practitioners within the environmental and ecological sciences, focusing on the full life cycle of citizen science practice, from design to implementation, evaluation and data management. We highlight key issues in citizen science and how to address them, such as participant engagement and retention, data quality assurance and bias correction, as well as ethical considerations regarding data sharing. We also provide a range of examples to illustrate the diversity of applications, from biodiversity research and land cover assessment to forest health monitoring and marine pollution. The aspects of reproducibility and data sharing are considered, placing citizen science within an encompassing open science perspective. Finally, we discuss its limitations and challenges and present an outlook for the application of citizen science in multiple science domains…(More)”.