Understanding Technology-Induced Value Change: a Pragmatist Proposal.

We propose a pragmatist account of value change that helps to understand how and why values sometimes change due to technological developments. Inspired by John Dewey's writings on value, we propose to understand values as evaluative devices that carry over from earlier experiences and that are to some extent shared in society. We discuss the various functions that values fulfil in moral inquiry and propose a conceptual framework that helps to understand value change as the interaction between three manifestations of value distinguished by Dewey, i.e., "immediate value," "values as the result of inquiry" and "generalized values." We show how this framework helps to distinguish three types of value change: value dynamism, value adaptation, and value emergence, and we illustrate these with examples from the domain of technology. We argue that our account helps to better understand how technology may induce value change, namely through the creation of what Dewey calls indeterminate situations, and we show how our account can integrate several insights on (techno)moral change offered by other authors.

Regulating AI in Health Care: The Challenges of Informed User Engagement.

The European Union's proposed Artificial Intelligence Act is a welcome, ambitious law on the regulation of AI systems. However, it underestimates the responsibilities placed on individual users to navigate the implementation of AI. Focusing on the health care sector, this policy piece examines challenges that the proposed law bypasses. First, effective human-AI collaboration in the diagnostic process hinges on the acknowledgment of AI's mediating role in this process, on forming a diagnostic dialogue between humans and AI. Second, with AI in this mediating role, the meaning of responsibility is changed to accommodate the broadened scope of clinician and patient duties, modified clinical workflows, and emergent medical norms. Finally, the challenge of media literacy concerns both the issues of access to knowledge and the ability to make informed choices regarding human-AI interaction. This policy piece suggests that embracing the complexity of the use practices is essential to achieving an effective human-AI partnership, in the medical sector and at large.

Co-designing diagnosis: Towards a responsible integration of Machine Learning decision-support systems in medical diagnostics.

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.

What is morally at stake when using algorithms to make medical diagnoses? Expanding the discussion beyond risks and harms.

In this paper, we examine the qualitative moral impact of machine learning-based clinical decision support systems in the process of medical diagnosis. To date, discussions about machine learning in this context have focused on problems that can be measured and assessed quantitatively, such as by estimating the extent of potential harm or calculating incurred risks. We maintain that such discussions neglect the qualitative moral impact of these technologies. Drawing on the philosophical approaches of technomoral change and technological mediation theory, which explore the interplay between technologies and morality, we present an analysis of concerns related to the adoption of machine learning-aided medical diagnosis. We analyze anticipated moral issues that machine learning systems pose for different stakeholders, such as bias and opacity in the way that models are trained to produce diagnoses, changes to how health care providers, patients, and developers understand their roles and professions, and challenges to existing forms of medical legislation. Albeit preliminary in nature, the insights offered by the technomoral change and the technological mediation approaches expand and enrich the current discussion about machine learning in diagnostic practices, bringing distinct and currently underexplored areas of concern to the forefront. These insights can contribute to a more encompassing and better informed decision-making process when adapting machine learning techniques to medical diagnosis, while acknowledging the interests of multiple stakeholders and the active role that technologies play in generating, perpetuating, and modifying ethical concerns in health care.

Accounting for the Moral Significance of Technology: Revisiting the Case of Non-Medical Sex Selection.

This article explores the moral significance of technology, reviewing a microfluidic chip for sperm sorting and its use for non-medical sex selection. I explore how a specific material setting of this new iteration of pre-pregnancy sex selection technology-with a promised low cost, non-invasive nature and possibility to use at home-fosters new and exacerbates existing ethical concerns. I compare this new technology with the existing sex selection methods of sperm sorting and Prenatal Genetic Diagnosis. Current ethical and political debates on emerging technologies predominantly focus on the quantifiable risk-and-benefit logic that invites an unequivocal "either-or" decision on their future and misses the contextual ethical impact of technology. The article aims to deepen the discussion on sex selection and supplement it with the analysis of the new technology's ethical potential to alter human practices, perceptions and the evaluative concepts with which we approach it. I suggest that the technological mediation approach (Verbeek, 2005, 2011) can be useful to ethically contextualize technologies and highlight the value of such considerations for the informed deliberation regarding their use, design and governance.