Paula Ziethmann

As artificial intelligence (AI) becomes increasingly integrated into clinical environments, questions of trust, transparency, and shared decision-making come to the fore. This article examines how public perspectives can influence the ethical and technical development of AI tools in medicine, drawing on empirical insights from an interdisciplinary project focused on developing AI to support the diagnosis and treatment of skin cancer. Rather than treating ethical concerns as external to technical design, we argue that they must be addressed from within the development process. In our case, this was achieved by integrating citizen feedback into iterative design loops within our interdisciplinary team, fostering closer alignment between AI functionalities and public values. Through focus group discussions with citizens and a constructivist grounded theory approach, we identified three key areas of concern: the evolving doctor–patient relationship, patient agency in AI-supported care, and the influence of specific medical contexts on public evaluations of AI. This article illustrates how these citizen perspectives can be meaningfully connected with the medical and technical considerations shaping the development of AI.

In the realm of technology, the deployment of artificial intelligence (AI) has the potential to profoundly alter both the perception and allocation of responsibility. The complexity of this challenge is further exacerbated by the advent of AI. In this paper, we delve into the underlying reasons for this transformation, drawing upon insights from both the fields of philosophy of technology and technical definitions of AI. Within this context, the theoretical framework of Multi-Actor Responsibility emerges as a pivotal concept, poised to mitigate the diffusion of responsibility commonly associated with the utilization of AI. To illustrate this concept, we employ a case study involving Clinical Decision Support Systems (CDSS). In our exploration, we emphasize the critical necessity not only of scrutinizing intricate individual procedural elements but also of delineating the various levels of responsibility. Furthermore, we underscore the paramount role of explainability in elucidating the boundaries of responsibility within AI systems.