Rationale: This paper aims to show how the focus on eradicating bias from Machine Learning decision-support systems in medical diagnosis diverts attention from the hermeneutic nature of medical decision-making and the productive role of bias. We want to show how an introduction of Machine Learning systems alters the diagnostic process. Reviewing the negative conception of bias and incorporating the mediating role of Machine Learning systems in the medical diagnosis are essential for an encompassing, critical and informed medical decision-making. Methods: This paper presents a philosophical analysis, employing the conceptual frameworks of hermeneutics and technological mediation, while drawing on the case of Machine Learning algorithms assisting doctors in diagnosis. This paper unravels the non-neutral role of algorithms in the doctor's decision-making and points to the dialogical nature of interaction not only with the patients but also with the technologies that co-shape the diagnosis. Findings: Following the hermeneutical model of medical diagnosis, we review the notion of bias to show how it is an inalienable and productive part of diagnosis. We show how Machine Learning biases join the human ones to actively shape the diagnostic process, simultaneously expanding and narrowing medical attention, highlighting certain aspects, while disclosing others, thus mediating medical perceptions and actions. Based on that, we demonstrate how doctors can take Machine Learning systems on board for an enhanced medical diagnosis, while being aware of their non-neutral role. Conclusions: We show that Machine Learning systems join doctors and patients in co-designing a triad of medical diagnosis. We highlight that it is imperative to examine the hermeneutic role of the Machine Learning systems. Additionally, we suggest including not only the patient, but also colleagues to ensure an encompassing diagnostic process, to respect its inherently hermeneutic nature and to work productively with the existing human and machine biases.

Purpose: This paper aims to show how the production of meaning is a matter of people interacting with technologies, throughout their appropriation and in co-performances. The researchers rely on the case of household-based voice assistants that endorse speaking as a primary mode of interaction with technologies. By analyzing the ethical significance of voice assistants as co-producers of moral meaning intervening in the material and socio-cultural space of the home, the paper invites their informed and critical use as a form of (re-)empowerment while acknowledging their productive role in human values. Design/methodology/approach: This paper presents an empirically informed philosophical analysis. Using the conceptual frameworks of technological appropriation and human–technological performances, while drawing on the interviews with voice assistants’ users and literature studies, this paper unravels the meaning-making processes in relation to these technologies in the household use. It additionally draws on a Wittgensteinian perspective to attend to the productive role of language and link to wider cultural meanings. Findings: By combining two approaches, appropriation and technoperformances, and analyzing the themes of privacy, power and knowledge, the paper shows how voice assistants help to shape a specific moral subject: embodied in space and made as it performatively responds to the device and makes sense of it together with others. Originality/value: The researchers show how through making sense of technologies in appropriation and performatively responding to them, people can change and intervene in the power structures that technologies suggest.

This article explores the understanding of values in Responsible Research and Innovation (RRI). First, it analyses how two mainstream RRI approaches, the largely substantial one by Von Schomberg and the procedural one by Stilgoe and colleagues, identify and conceptualize values. We argue that by treating values as relatively stable entities, directly available for reflection, both fall into an ‘entity trap’. As a result, the hermeneutic work required to identify values is overlooked. We therefore seek to bolster a practice-based take on values, which approaches values as the evolving results of valuing processes. We highlight how this approach views values as lived realities, interactive and dynamic, discuss methodological implications for RRI, and explore potential limitations. Overall, the strength of this approach is that it enables RRI scholars and practitioners to better acknowledge the complexities involved in valuing.