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)

The COVID-19 pandemic has been the scene of several epistemic battles at the science-society interface, creating deadlocks that have been hard to overcome. To cut through the paralysing elements of these discussions, we present an analysis of three epistemic battles, concerning empirical evidence, expertise, and model projections. Our analysis singles out a crucial factor that drives unhelpful disputes like these: the contested prioritisation of specific types of scientific knowledge, which are considered adequate for policy only if they meet predetermined standards. (...) To move beyond these deadlocks, we introduce the conceptual tools of epistemic pluralism and contextualism, which give concrete indications in the three controversies we discuss and show us the way forward in debates on science-based policy. (shrink)