Abstract
This paper analyzes the collaboration between healthcare professionals and algorithms in making decisions within the realm of public healthcare. By extending the concept of 'tinkering' from previous research conducted by philosopher Mol (Care in practice. On tinkering in clinics, homes and farms Verlag, Amsterdam, 2010) and anthropologist Pols (Health Care Anal 18: 374-388, 2009), who highlighted the improvisational and adaptive practices of healthcare professionals, this paper reveals that in the context of digitalizing healthcare, both professionals and algorithms engage in what I call 'collaborative tinkering' as they navigate the intricate and unpredictable nature of healthcare situations together. The paper draws upon an idea that is increasingly common in academic literature, namely that healthcare professionals and the algorithms they use can form a hybrid decision-making entity, challenging the conventional notion of agency and intelligence as being exclusively confined to individual humans or machines. Drawing upon an international, ethnographic study conducted in different hospitals around the world, the paper describes empirically how humans and algorithms come to decisions together, making explicit how, in the practice of daily work, agency and intelligence are distributed among a range of actors, including humans, technologies, knowledge resources, and the spaces where they interact. The concept of collaborative tinkering helps to make explicit how both healthcare professionals and algorithms engage in adaptive improvisation. This exploration not only enriches the understanding of collaborative dynamics between humans and AI but also problematizes the individualistic conception of AI that still exists in regulatory frameworks. By introducing empirical specificity through ethnographic insights and employing an anthropological perspective, the paper calls for a critical reassessment of current ethical and policy frameworks governing human-AI collaboration in healthcare, thereby illuminating direct implications for the future of AI ethics in medical practice.