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Paper short abstract

A discursive analysis of EU gene-editing policy explaining how NGTs became acceptable where GMOs did not, through science-based, apolitical framings of precision and sustainability that support regulatory change while marginalising socioeconomic concerns.

Paper long abstract

The adoption of EU legislation on plants produced by certain new genomic techniques (NGTs) marks a historic shift away from the restrictive GMO regulatory framework that has governed gene-edited crops for the past three decades. This article critically examines how this regulatory transformation became politically possible from a discursive perspective. It analyses the EU debate and legislative process on gene-edited plants to explain why ‘NGTs’ have gained acceptance where ‘GMOs’ previously failed. Drawing on the Political Discourse Theory (PDT) the article examines the logics through which NGTs are framed as precise, science-based, and equivalent to conventionally bred crops. Empirically, the study is based on a document study and 15 semi-structured interviews with EU politicians, civil servants, lobbyists, and NGO representatives in Brussels. Preliminary analysis suggests how promises of a governable, sustainable future through values of control, innovation, efficiency, and profitability underpin an ostensibly “a-political” discourse that facilitates regulatory change by foregrounding scientific equivalence and technical precision. At the same time, the article highlights the political limits of this framing, showing how it marginalises concerns related to patents, corporate concentration, and resistance to the agro-industrial model in European agriculture. By unpacking the black box of science-based policymaking, the article demonstrates how scientific knowledge is actively negotiated and mobilised in EU biotechnology governance.

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What is granular data, and how does it become "precise"? Drawing on ethnographic research in digital agriculture, this paper examines granularity as a practitioner-made achievement, showing how precision depends on trained judgment and comparison with other ways of knowing.

Paper long abstract

Technologies of precision promise to replace uncertainty with accuracy. As in other domains, precision in digital agriculture is now tied to granular data. This data would be fine-grained enough to enable precise knowledge of phenomena, both external to plants and internal, such as temperature or chemical states. Yet, what is granular data, and how does it become precise? To answer these questions, I draw from interviews and ethnographic observations with digital agriculture actors. Inspired by material-semiotic insights from STS (Law, 2008; Mol, 1999; Lien & Law, 2011), I consider granularity as a practitioner-made achievement rather than a technical property alone. In doing so, I examine how granular data is brought into existence through heterogeneous practices, showing how plant lives are rendered "knowable, predictable, and governable through big data analysis". This examination speaks directly to the study of precision(s) through two main observations: first, data practices in producing and interpreting granular data depend on experimental infrastructures, interdisciplinary collaboration, and ongoing human interpretation and expertise, similar to trained judgment (Daston and Galison, 2010), in making sense of granular data. Yet these elements often disappear from claims about granularity and precision. Second, the value of granularity is premised on comparisons with other modes of knowing, such as traditional agronomic knowledge, rather than by empirical demonstrations. While granular data is often framed as an ideal of mechanical objectivity, this framing tends to obscure both the trained judgments on which it depends and the comparative work that establishes its advantage over other ways of knowing.

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Predictive AI tools promise more efficient healthcare but are tightly regulated in the EU. Studying “in-house” regulation around AI implementation in Danish hospitals, this paper shows how compliance with data protection might sideline debates about the epistemic ethics of precision technologies.

Paper long abstract

In healthcare, algorithms developed under the label of “AI” are expected to predict future medical events more accurately than clinicians. As precision technologies, predictive algorithms promise to improve clinical decision-making and enhance the efficiency of healthcare delivery. However, the EU AI Act classifies many such healthcare applications as high-risk, subjecting them to extensive regulatory oversight that clinical environments often experience as an obstacle to research and innovation assumed to benefit patients. In 2023, the European Commission’s Medical Device Coordination Group issued guidance allowing hospitals and other health institutions to manufacture and use medical devices—including AI-based tools—on a non-industrial scale within the same legal entity, exempting these “in-house” devices from the requirements set out in the Medical Device Regulation. Drawing on qualitative interviews with regional employees approving AI-based tools and with clinicians working with a predictive algorithm in Danish healthcare, this paper explores the ethical tensions emerging when predictive tools are deployed under the in-house provision. We show that regional employees orient toward compliance with data protection and privacy legislation to avoid public controversies around data misuse. This compliance-driven focus, we argue, risks sidelining broader debates about the epistemic ethics of precision technologies: the knowledge predictive algorithms produce, the clinical practices they foster, and the purposes this knowledge can legitimately serve. In the absence of institutional spaces for ethical deliberation, employees may resort to collective ignorance, avoiding involvement with predictive tools they suspect could enable ethically problematic practices such as the deprioritization of patients at the margins of life.

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Paper short abstract

The Europe Horizon-funded Intelligent Robotic Endoscopes (IRE for Improved Healthcare Services) develops robots for colonoscopies. A procedure with vast variety of quality among specialized MDs. In collaborating on this project, new, more and different precision is devel-oped and needed to succeed.

Paper long abstract

The Europe Horizon-funded project Intelligent Robotic Endoscopes (IRE for Improved Healthcare Services) aims at developing robots for colonoscopies.

This has been deemed an appropriate target for colonoscopy, given the wide variety in quality among specialized MDs. The primary way of assessing the quality of an endoscopist is the Adenoma detection rate (ADR), and variation in coloscopy withdrawal speed and technique has been shown to influence the ADRs. The overall idea in the research project is that a robot will be able to have an overall better technique and optimal speed for the discovery of polyps compared to the median of endoscopist.

IRE is a collaboration across borders and professions, as several kinds of engineers, data scientists, medical professionals, and anthropologists are developing components and infrastructure to further the final product of a robot for colonoscopies.

Through document analysis as practice, ethnographic fieldwork, and interviews, I have collaborated in and explored how precision demands precision. Both in communication across fields and borders, but also in the knowledge production, as new knowledge is needed when a robot is taught something previously practiced by humans, and robots are excluded from learning as doctors do (on humans).

In collaborating on this project, new, more, and different precision is developed and needed to succeed. As robots need to learn to navigate the colon with precision and engineers and data scientist learns that the pictures of colons in medical books are utopic illustrations and the reality is filled with variety and imprecise shapes.

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This presentation traces how the epistemic logic of precision extends from digital agriculture toward agroecology, producing "precision-agroecology" as a convergence that tends to legitimise a weak, measurable form of agroecology compatible with the logic of precision.

Paper long abstract

Over the past decade, R&D programmes and official documents have increasingly promoted the coupling of digital technology and agro-ecological principles as an obvious solution for the sustainable future of agri-food systems, which can be referred to as “precision-agroecology”. Distinguished from precision agriculture in the late 20th century–criticized for its focus on productivity, the convergence of digital- and agro-ecological agriculture, which still relies on data, sensors, models, and robotics, is expected to make future agri-food systems more knowable, predictable, and governable, therefore desirable. Yet, while studies on agroecology and, separately, digital agriculture abound, few bring the two together into dialogue around the construction of their coupling—from digital and ecological transitions to the digital-ecological transition. How is this coupling produced, narrated, and legitimised? What implications does this construction generate politically and epistemically? To answer these questions, drawing on official documents and interviews with their authors, we trace how the epistemic logic of precision—“observe, measure, intervene at the right time and place”—has extended from precision agriculture toward “precision-agroecology”. We argue that “precision-agroecology” aligns heterogeneous, even conflicting sociotechnical imaginaries of digitalisation and agroecology, through visionary and discursive processes and institutional arrangements—by translating agro-ecology into variables compatible with the logic of precision; tending to legitimise a “weak”, measurable and governable form of agroecology; and by stabilising a horizon for “precision agroecology”. This coupling brings together two partly incompatible imaginaries, with precision agriculture emerging victorious and regaining legitimacy through its agro-ecologisation.

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Precision fermentation seeks to replace animal foods with recombinant proteins made in microbes. Using metabolism as an analytic lens, this paper shows how stakeholders’ perception of microbes both shape and are shaped by the materializing infrastructure and its socioecological entanglements.

Paper long abstract

Precision fermentation (PF) envisions a sustainable food future in which animal-based foods, such as milk and eggs, are replaced by identical recombinant proteins produced in microbial hosts. PF falls under the umbrella of cellular agriculture, a field that is shaped by a powerful imaginary of dematerialized protein production (e.g., Guthman & Biltekoff, 2021). Yet as industrial-scale production is only beginning to materialize, the tension between these imaginaries and the metabolic realities of PF production remains largely unexamined.

This paper analyzes the embedded metabolisms of PF through interviews with Finnish stakeholders. Following Landecker (2023), I approach metabolism not only as an empirical site but also as an analytic heuristic that foregrounds how energy and nutrients circulate through PF infrastructures and wider ecosystems – including microbial bodies whose metabolic labor sustains this production. This approach highlights how stakeholders attempt to optimize the metabolic potentialities of their engineered microbes while negotiating the economic, legal and biological constraints inherent to their mission. I argue that the metabolic lens offers a more nuanced understanding of how emerging PF infrastructures and their socioecological entanglements both shape and are shaped by stakeholders’ interpretations of microbial metabolic potentiality.

In the context of the politics of precision, PF extends industrial spatio-temporalities by intensifying reliance on monocultural feedstocks and generating novel streams of genetically modified microbial waste, thereby enacting food futures that appear efficient and widely controllable while, in reality, they obscure the material dependencies on which such systems are built.

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Paper short abstract

Computed images combine maps and visual imagery but differ from photographs: elements may be absent or generated without visible cues. The paper argues that claims of precision in the context of AI-driven technologies depend on questioning habits of seeing and critically assessing plausibility.

Paper long abstract

Precision has become a central promise in contemporary technological approaches to the efficient and responsible distribution of resources. An important aspect of such systems is the human evaluation of displayed data in constellations, where automated analysis and human judgment are deliberately combined. Increasingly, drones equipped with sensors and AI-based processing are deployed to generate situational images intended to support decision-making.

The interpretation of these images involves a distinctive visual configuration. They combine familiar representational formats—most notably maps and photographic imagery—with a comparatively new form of technical output: computed images. Rather than direct recordings, these images are produced through algorithmic processing and inference, translating heterogeneous sensor data into visual outputs that appear immediately readable.

Computed images differ in a crucial way from camera-based photographs. There, absences or anomalies often produce visual irritations that invite closer scrutiny. In computed imagery, by contrast, information may simply not appear without any visible indication that something is missing—for example when areas are left blank or when the system lacks data. Conversely, computed images may display objects or patterns that are not present in that form in the observed environment but emerge from the system’s calculations and models.

The paper argues that the emphasis on precision cannot be understood solely as a property of technical systems. It must be coupled with the human capacity to critically assess plausibility and to question established habits of technological seeing. Otherwise, the promise of precision risks obscuring errors and undermining the reliability such systems claim to provide.

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Paper short abstract

Drawing on ethnographic research among plant scientists, this paper explores how precision gene editing is used to create plants for future agricultural environments. We argue that attending to the materiality of precision technologies complicates the promissory narratives that surround them.

Paper long abstract

CRISPR-based genome editing is enabling novel plant breeding strategies that aim to replace stochasticity with precision. With the de novo domestication approach, plant scientists seek to transform wild or semi-wild species into resilient, high-yielding, domesticated crops by precisely targeting a small number of key genes. STS studies on gene editing in agriculture have predominantly focused on the controversies and politics surrounding these technologies. Based on ethnographic fieldwork among plant scientists in Denmark, we instead explore how de novo domestication, as a precision technology, is practically enacted in laboratory work with quinoa cultivars. We show that plant scientists seek to inscribe future climates, values, and strategies into the biological matter of laboratory plants. In doing so they aim to make agricultural futures predictable and manageable by combining CRISPR-based technologies with big-data genomics. This allows for new governmental strategies that focus increasingly on the environmental properties of life forms rather than on plants in themselves, tightening the weave between genes and environments. At the same time, we show that some quinoa cultivars resist gene editing, limiting the performative power of scientists’ imaginaries of future precision agriculture. Attending to these difficulties helps illuminate what happens when precision technologies, and the futures they promise, encounter opposition not only from political actors, but from the plants themselves.

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We examine how social roles influence evaluations of the trade-off between explainability and accuracy in AI-based health care. We find that participants adopting the perspective of an affected patient rather than a neutral observer exhibit stronger preferences for accuracy over explainability.

Paper long abstract

Artificial intelligence (AI) systems are increasingly adopted in healthcare. Many of the most accurate models operate as “black boxes”. This creates a tension between maximizing predictive performance and ensuring explainability. Explainability is emphasized as a prerequisite for patient autonomy because it ensures transparency and human oversight in high-risk applications. However, research shows that laypeople value interpretability of AI but are willing to sacrifice it for accuracy, especially in high-stakes contexts and health care settings. This raises the question of whether patient autonomy in the design of AI systems can be pursued solely through a focus on explainability. In an online survey experiment, we investigate whether people’s evaluations of the trade-off with accuracy differ when they consider it from the perspective of (1) a neutral observer, (2) an affected patient, or (3) a physician using AI-based risk assessments. We find that participants who adopt the perspective of an affected patient exhibit stronger preferences for accuracy over explainability of an AI system than neutral observers. Our findings suggest that individuals’ willingness to prioritize explainability over accuracy may depend strongly on how they are affected by the AI system’s assessments. An empathy gap may exist between the actors that lead societal discussions about AI systems (i.e., those not directly affected) and affected patients. This raises concerns about value-based patient autonomy. Clinical AI systems may need to be tailored to ensure that both transparency and patient values are meaningfully balanced and integrated into AI-mediated risk assessments.

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The presentation unpacks how digital therapeutics are envisioned as leading to a more accurate understanding of mental health through self-tracking, based on qualitative interviews with developers.

Paper long abstract

“Apps on prescription” have become part of the “digital psychiatry” (Pickersgill 2019) in Germany since 2020. These certified apps can be used by patients with a diagnosed illness such as depression or anxiety disorder. In this presentation, I unpack how, among other promises, digital therapeutics are presented as a tool to create a more precise picture of one’s own mental health as a user and for practitioners. Based on qualitative interviews with developers, I show how this promise is connected to a vision to deepen an understanding of mental illnesses by analysing aggregated user data for research in the future.

Digital self-tracking features are seen as easily accessible, usable daily and from everywhere empowering patients to continuously track one’s own symptoms and mood and visualising their mental health. In this narrative, a more accurate overview of one’s mental health is achieved as (1) it enables more consistency in data collection in comparison to analogue documents (quantity of data) and (2) it increases the quality of the data as data points are collected in the moment, instead of recollecting them days or weeks later. Developers also aim to fill in the blanks of the “black box” of the patient’s mental health between doctor’s appointments. Here tracking and showing one’s data is seen as a solution to let practitioners “see” how patients are doing in their daily lives and thus fill missing data points to create more quality time in meetings and better treatment.