Social Studies of Science

Laboratory sciences crucially depend on the cleanliness of experiments. But what is clean? In this article, we show that the salience of the valuation clean emerges through its relation to a particular ontological repertoire. Our case is the XENONnT experiment in the Gran Sasso Mountains of Italy, designed to detect dark matter in the form of hypothetical WIMPs (Weakly Interacting Massive Particles). In this experiment, dirt presents a significant disruption, as contaminations can mimic the signals of WIMPs, and electronegative molecules risk erasing such signals. The ideosyncratic cleanliness required makes the practice of cleaning the XENONnT detector exceedingly difficult. So far, the ontological question ‘do WIMPs exist?’ remains open, which means that the normative question ‘is the detector clean enough?’ cannot be answered either. In addition, more cleaning will make the detector sensitive to a background of unremovable neutrinos—hence irredeemably dirty. With the normative goal of a ‘clean detector’ out of reach, the ontological question ‘do WIMPs exist?’ is bound to remain open as well. Alternative experiments therefore hunt for different hypothetical dark matter candidates, with different equipment, requiring different kinds of cleanliness. At the same time, the XENONnT experiment must navigate tensions between its own cleanliness goals and rules meant to ensure the environmental cleanliness of the Gran Sasso National Park. Cleaning turns out to be dirty. This leads us to ask: Which goods deserve to be cherished, and, intertwined with that, which realities deserve to be cared for?

The opacity of machine learning (ML) algorithms is a significant concern in academic and regulatory circles. An emergent sociology of algorithms, however, argues that far from opacity being an inherent quality of algorithms, it is socially constructed and contingent upon certain choices and decisions. In this article, we show that a valorization of opacity is a key component of the epistemic culture of ML experts. While earlier campaigns for mechanical objectivity contrasted the inconsistency of human experts with the reliability of procedures and machines, we found that ML experts valorize precisely those moments when complex algorithms ‘surprised’ them with unexpected outcomes. They thereby endowed machines with a mysterious capacity to make predictions based on calculations and factors that humans cannot grasp. In this way, they turned opacity from a problem into an epistemic virtue. We trace this valorization of opacity to the jurisdictional struggles through which ML expertise emerged and differentiated itself from its two competitors: the ‘expert systems’ type of the ‘artificial intelligence’ sub-field of computer science on the one hand and inferential statistics on the other. In the course of these struggles, ML experts absorbed a theory of human expertise as tacit and inarticulable, extended it to include algorithms, and then leveraged this newly acquired version of opacity to dramatize the differences that separated them from statisticians. The analysis is based on sixty in-depth, semi-structured, and open-ended interviews with ML experts and data scientists working today, as well as historical research on the origins of data science.

This paper examines the case of Iruda, an AI chatbot launched in December 2020 by the South Korean startup Scatter Lab. Iruda quickly became the center of a controversy, because of inappropriate remarks and sexual exchanges. As conversations between Iruda and users spread through online communities, the controversy expanded to other issues, including hate speech against minorities and privacy violations. Under public pressure, Scatter Lab quickly suspended Iruda on 12 January 2021. After implementing extensive changes, the company relaunched the chatbot as Iruda 2.0 in October 2022. Notably, this revised version has operated without any major incidents as of mid-2025. This study offers a symmetrical analysis of Iruda’s initial failure and subsequent success in terms of ‘folds’ connecting users, machines, algorithms, and other key elements. We introduce ‘configuration’ as a mode of folding and show how socio-material assemblages—whether harmful or safe—emerge as a result of different configurations. The success of Iruda 2.0 highlights the importance of placing ethics at the core of AI development and implementation strategies. In addition, we introduce the concept of ‘ethics-in-action’ to highlight the critical role of practical interventions and user engagement. By tracing Iruda’s evolution in detail, this study provides practical guidelines for the successful integration of AI systems into society.

Digital data are increasingly framed as essential resources in health and medicine, implicating diverse actors who work to transform them into different forms of value. In this article we focus on the diverse and contingent valuation practices that shaped an artificial intelligence-enabled ‘smart’ health technology and the data it generated at different moments in time, and the corresponding asset forms that were envisioned, developed, tested, and marketed. We also outline the role of assetization as a contested but essential design and marketing activity, and introduce the notion of proxy work as an intermediary between data generation and assetization, where people, infrastructures, and other material devices are arranged in such a way that data become capable of ‘standing in’ for something else, allowing accountable forms of value to be realized across multiple sites. We conclude with a discussion of the consequences of assetization as a dominant lens through which governments, firms, and other actors increasingly understand the value of digital health data, and the different health-related futures those practices make possible.

Despite the social and regulatory expectation that contemporary clinical trials for new pharmaceutical drugs will include a diverse set of research participants, achieving appropriate representation in clinical research remains tenuous. Of ongoing concern is how experts and regulators navigate decisions about drug approvals when presented with clinical studies that have limited demographic data. Drawing on regulatory discussions about Descovy, an HIV-prevention drug that was studied only in cisgender men and transgender women who have sex with men, we analyze a contentious debate over the meaning and impact of including and excluding certain populations from clinical trial design. Extending prior work in science and technology studies on how epistemological frameworks in clinical trials matter for concerns about the production of knowledge and social justice, we show how different conceptualizations of inclusion and equity (specifically, equity in data versus equity in access) come into tension in deliberations over pharmaceutical drug approvals. We argue that the discursive conflict over gender and inclusionary/exclusionary research practices that emerged in the case of Descovy points to an underappreciated feature of equity—temporality—that should be attended to when examining knowledge production in 21st-century clinical and regulatory landscapes.

Through a case study of what are known as ‘relief models’—for example of areas of landscapes—this article approaches representational objects in and as practice. Such an approach implies following the multiplicity of practices that are gathered in representational objects to bring them into and maintain their existence, especially those that often remain unacknowledged by analytical attention. Most discussions of representational objects focus on their representational capacities and properties, paying less attention to the activities that ensure their ontological security as objects. Those activities concern not only the manufacture of representational objects, but also their maintenance—which is placed at the heart of this discussion. The maintenance of relief models manifests itself as a semiotic-material ecology. Entangled here are the ontological tact of the craftsperson, the affordances, resistances, and responsiveness of the materials, and the meaning-makings and stories that articulate and guide maintenance and repair. The practice of maintaining such objects, however, diverges from their production. Their production essentially accommodates metric distance since representation involves transporting a ‘thing’ through chains of reference. On the contrary, their maintenance aims to accommodate multiple temporalities. This involves not only the ways of being in time that are specific to each material that composes the object but also the idealized past of an unused object, its worn present, and its anticipated (repaired) future. By playing with the double meaning of the word ‘representing’, this article speculatively questions the extent to which practices of maintenance of, and care for, representational objects can inform a re-vision and rethinking of the relationships to what they are meant to re-present—that is, to what counts as nature.

Over the past decade, evolutionary biology has seen an unusual number of heated debates centered around the pronouncement of an Extended Evolutionary Synthesis (EES). This theoretical framework brings together researchers from a range of disciplines in biology, such as ecology, developmental biology, and epigenetics, as well as philosophers of science, to challenge some of the key tenets of contemporary evolutionary theory, by arguing for a greater role of the environment and the organism in evolution. In this article, we analyze the EES as a scientific-intellectual movement (SIM) that has emerged under two specific conditions. First, evolutionary biology has always been both scientifically and socially influential and contested. As a field that claims to answer fundamental questions of how life has come to be, evolutionary biology has shaped causal thinking in fields as diverse as biology, psychology, and economics, and has influenced cultural thought and politics. Second, this specific contestation of mainstream evolutionary thinking emerges in the midst of challenges to particular sciences by what are seen as ‘post-truth’ and ‘anti-science’ movements. Drawing on interviews, participant observation, and document analysis, we examine the credibility strategies that EES proponents employ under these conditions, highlighting what happens when opponents of the EES make use of the ‘post-truth’ label to argue against the EES. We argue that this transposition of structures familiar from public and political debate onto contestations within science represents an important topic of study for STS researchers in the current political moment.