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.

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.

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.

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.

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.