Diego Morales

In this paper, I introduce a “promises and perils” framework for understanding the “soft” impacts of emerging technology, and argue for a eudaimonic conception of well-being. This eudaimonic conception of well-being, however, presupposes that we have something like stable character traits. I therefore defend this view from the “situationist challenge” and show that instead of viewing this challenge as a threat to well-being, we can incorporate it into how we think about living well with technology. Human beings are susceptible to situational influences and are often unaware of the ways that their social and technological environment influence not only their ability to do well, but even their ability to know whether they are doing well. Any theory that attempts to describe what it means for us to be doing well, then, needs to take these contextual features into account and bake them into a theory of human flourishing. By paying careful attention to these contextual factors, we can design systems that promote human flourishing.

Background Big data and AI applications now play a major role in many health contexts. Much research has already been conducted on ethical and social challenges associated with these technologies. Likewise, there are already some studies that investigate empirically which values and attitudes play a role in connection with their design and implementation. What is still in its infancy, however, is the comparative investigation of the perspectives of different stakeholders. Methods To explore this issue in a multi-faceted manner, we conducted semi-structured interviews as well as focus group discussions with patients and clinicians. These empirical methods were used to gather interviewee’s views on the opportunities and challenges of medical AI and other data-intensive applications. Results Different clinician and patient groups are exposed to medical AI to differing degrees. Interviewees expect and demand that the purposes of data processing accord with patient preferences, and that data are put to effective use to generate social value. One central result is the shared tendency of clinicians and patients to maintain individualistic ascriptions of responsibility for clinical outcomes. Conclusions Medical AI and the proliferation of data with import for health-related inferences shape and partially reconfigure stakeholder expectations of how these technologies relate to the decision-making of human agents. Intuitions about individual responsibility for clinical outcomes could eventually be disrupted by the increasing sophistication of data-intensive and AI-driven clinical tools. Besides individual responsibility, systemic governance will be key to promote alignment with stakeholder expectations in AI-driven and data-intensive health settings.

In this reply to van de Poel’s (Philosophy & Technology, 35(3), 82, 2022) commentary on O’Neill (Philosophy & Technology, 35(79), 2022), I discuss two worries about the general contextual integrity approach to evaluating technological change. First, I address van de Poel’s concern that the general contextual integrity approach will not supply the right guidance in cases where morally problematic technological change poses no threat to contextual integrity. Second, I elaborate on how the approach supplies mechanisms for balancing caution with the need for change.

Throughout scientific history, there have been cases of mainstream science dismissing novel ideas of less prominent researchers. Nowadays, many researchers with different social and academic backgrounds, origins and gender identities work together on topics of crucial importance. Still, it is questionable whether the privileged groups consider the views of underprivileged colleagues with sufficient attention. To profit from the diversity of thoughts, the scientific community first has to be open to minority viewpoints and epistemically include them in mainstream research. Moreover, the idea of inclusive science poses stronger requirements than the paradigm of open science. We argue that the concept of integration of different opinions is insufficient because the process of integration assumes adjusting oneself to the majority view and fitting into the dominant paradigm while contributing only with smaller amendments. Epistemic inclusion, on the other hand, means dynamically changing the research paradigm during the interaction with diverse methods and hypotheses. The process of inclusion preserves marginalized views and increases epistemic justice.

Artificial intelligence is currently changing many areas of society. Especially in health, where critical decisions are made, questions of control must be renegotiated: who is in control when an automated system makes clinically relevant decisions? Increasingly, the concept of meaningful human control (MHC) is being invoked for this purpose. However, it is unclear exactly how this concept is to be understood in health. Through a systematic review, we present the current state of the concept of MHC in health. The results show that there is not yet a robust MHC concept for health. We propose a broader understanding of MHC along three strands of action: enabling, exercising and evaluating control. Taking into account these strands of action and the established rules and processes in the different health sectors, the MHC concept needs to be further developed to avoid falling into two gaps, which we have described as theoretical and labelling gaps.