In the past few years, the ethical ramifications of AI technologies have been at the center of intense debates. Considerable attention has been devoted to understanding how a morally responsible practice of data science can be promoted and which values have to shape it. In this context, ethics and moral responsibility have been mainly conceptualized as compliance to widely shared principles. However, several scholars have highlighted the limitations of such a principled approach. Drawing from microethics and the virtue theory tradition, (...) in this paper, we formulate a different approach to ethics in data science which is based on a different conception of “being ethical” and, ultimately, of what it means to promote a morally responsible data science. First, we develop the idea that, rather than only compliance, ethical decision-making consists in using certain moral abilities, which are cultivated by practicing and exercising them in the data science process. An aspect of virtue development that we discuss here is moral attention, which is the ability of data scientists to identify the ethical relevance of their own technical decisions in data science activities. Next, by elaborating on the capability approach, we define a technical act as ethically relevant when it impacts one or more of the basic human capabilities of data subjects. Therefore, rather than “applying ethics”, data scientists should cultivate ethics as a form of reflection on how technical choices and ethical impacts shape one another. Finally, we show how this microethical framework concretely works, by dissecting the ethical dimension of the technical procedures involved in data understanding and preparation of electronic health records. (shrink)

In this paper, we analyse the relation between the use of environmental data in contemporary health sciences and related conceptualisations and operationalisations of the notion of environment. We consider three case studies that exemplify a different selection of environmental data and mode of data integration in data-intensive epidemiology. We argue that the diversification of data sources, their increase in scale and scope, and the application of novel analytic tools have brought about three significant conceptual shifts. First, we discuss the EXPOsOMICS (...) project, an attempt to integrate genomic and environmental data which suggests a reframing of the boundaries between external and internal environments. Second, we explore the MEDMI platform, whose efforts to combine health, environmental and climate data instantiate a reframing and expansion of environmental exposure. Third, we illustrate how extracting epidemiological insights from extensive social data collected by the CIDACS institute yields innovative attributions of causal power to environmental factors. Identifying these shifts highlights the benefits and opportunities of new environmental data, as well as the challenges that such tools bring to understanding and fostering health. It also emphasises the constraints that data selection and accessibility pose to scientific imagination, including how researchers frame key concepts in health-related research. (shrink)

We analyse insufficient epistemic pluralism and associated problems in science-based policy advice during the COVID-19 pandemic drawing on specific arguments in Paul Feyerabend’s philosophy. Our goal is twofold: to deepen our understanding of the epistemic shortcomings in science-based policy during the pandemic, and to assess the merits and problems of Feyerabend’s arguments for epistemic pluralism as well as their relevance for policy-making. We discuss opportunities and challenges of integrating a plurality of viewpoints from within and outside science into policy advice (...) thus contributing to discussions about normative issues concerning evidence and expertise in policy-making. (shrink)

Balancing speed and quality during crises pose challenges for ensuring the value and utility of data in social science research. The COVID-19 pandemic in particular underscores the need for high-quality data and rapid dissemination. Given the importance of behavioural measures and compliance with measures to contain the pandemic, social science research has played a key role in policymaking during this global crisis. This study addresses two key research questions: How FAIR ( findable, accessible, interoperable and reusable) are social science data (...) on the COVID-19 pandemic? Which study features are related to the level of FAIRness scores of datasets? We assess the FAIRness of n = 1131 articles, retrieved through a keyword search in the Web of Science database, employing both automated and manual coding methods. Our study inclusion criteria encompass empirical studies on the COVID-19 pandemic published between 2019 and 2023 with a social science focus and explicit reference to the underlying dataset(s). Our analysis of n = 45 datasets reveals substantial differences in FAIRness for different types of research on the COVID-19 pandemic. The overall FAIRness of data is acceptable, although particularly Reusability scores fall short, in both the manual and the automatic assessment. Further, articles explicitly linked to the Social Science concept in the OpenAlex database exhibit a higher mean overall FAIRness value. Based on these results, we derive recommendations for balancing ethical obligations and the potential tradeoff between speed and data (sharing) quality in social-scientific crisis research. (shrink)

“Follow the science” was commonly repeated during debates on COVID-19-related policy. The phrase “follow the science” raises questions that are central to our theories of knowledge and the application of scientific knowledge to maximize the wellbeing of our society. The purpose of this study was to (1) perform a scoping review of literature discussing “follow the science” and COVID-19, and (2) consider “follow the science” in the context of pediatric health. A comprehensive search of 14 databases was performed on May (...) 23, 2023. Articles were included if they used terms such as “follow the science”, “follow the scientists”, “listen to science” or “listen to scientists”, and discussed COVID-19. There were 24 articles included in the final review. Existing literature on “follow the science” (1) differentiates between scientific knowledge and policy decisions; (2) emphasizes the importance of social sciences in policy making; (3) calls for more transparency in the knowledge synthesis and policy generating process; and (4) finds that scientific advisors see their role as advising on science rather than policy decision making. There was no definitional, epistemological, or philosophical intellectual defense of “follow the science” in the peer reviewed literature. Policy requires (1) reliable data and (2) agreement on what to do considering those empirical facts by appealing to values, ethics, morality, and law. A review of school shutdowns is used as an example of the inadequacy of “follow the science” as a guiding principle for public policy. (shrink)