From an Ethics of Carefulness to an Ethics of Desirability: Going Beyond Current Ethics Approaches to Sustainable AI

The ethics of artificial intelligence systems in healthcare and medicine: from a local to a global perspective, and back

Artificial intelligence systems (ai-systems) (e.g. machine learning, generative artificial intelligence), in healthcare and medicine, have been received with hopes of better care quality, more efficiency, lower care costs, etc. Simultaneously, these systems have been met with reservations regarding their impacts on stakeholders' privacy, on changing power dynamics, on systemic biases, etc. Fortunately, healthcare and medicine have been guided by a multitude of ethical principles, frameworks, or approaches, which also guide the use of ai-systems in healthcare and medicine, in one form or another. Nevertheless, in this article, I argue that most of these approaches are inspired by a local isolationist view on ai-systems, here exemplified by the principlist approach. Despite positive contributions to laying out the ethical landscape of ai-systems in healthcare and medicine, such ethics approaches are too focused on a specific local healthcare and medical setting, be it a particular care relationship, a particular care organisation, or a particular society or region. By doing so, they lose sight of the global impacts ai-systems have, especially environmental impacts and related social impacts, such as increased health risks. To meet this gap, this article presents a global approach to the ethics of ai-systems in healthcare and medicine which consists of five levels of ethical impacts and analysis: individual-relational, organisational, societal, global, and historical. As such, this global approach incorporates the local isolationist view by integrating it in a wider landscape of ethical consideration so to ensure ai-systems meet the needs of everyone everywhere.