The ethics of artificial intelligence in the life sciences: Universality, cultural diversity and an architecture of care

2026-08-05

AI Summary

This work addresses the unique ethical challenges posed by the application of artificial intelligence in the life sciences, arguing for a governance framework that moves beyond traditional “constraint”-based paradigms. Integrating insights from neuroscience, cognitive psychology, and AI ethics, the study proposes a novel “nurturing”-oriented governance model grounded in the Global Neuronal Workspace theory and a non-maximizing affective reward mechanism. This approach emulates the human cognitive-affective cycle of “desire–affection–satisfaction,” balancing ethical universality with moral diversity. The framework introduces a low-computational-cost architecture alongside a care-centered institutional design, offering both a theoretical foundation and a set of critical open questions to guide the ethical development and societal integration of AI systems in the life sciences.

Abstract

The life sciences and health research have started to benefit from artificial intelligence, which raises ethical concerns that are real but, we argue, not special. Any science should be governed by values that rest on how the human brain is built and socialised rather than anything distinct to artificial intelligence. Importantly, the human brain has a different, much less costly computational architecture than these machines. This is achieved through the orchestration of a global neuronal workspace, and through reward best described not as a quantity to be maximised but as a continuous cycle of wanting, liking and satiety. As such, this creates the deep tension running through the ethics of the human person, between the universality of ethical judgement and the diversity of morals. The brain networks of the global workspace and emotion are universally shared, but the diversity of content is shaped by epigenetic appropriation of the particulars of the physical, social and cultural world, which makes every person unique. Still, if we were to build machines on these principles rather than the present unaffordable reward maximisers, the question of their governance would change from restraint to upbringing. We set out the institutions such a future would require, together with the questions that remain open.

Problem

• artificial intelligence
• ethics
• cultural diversity
• life sciences
• governance

Innovation

• global neuronal workspace
• reward cycle
• epigenetic appropriation
• architecture of care
• AI ethics