Can machine learning make naturalism about health truly naturalistic? A reflection on a data-driven concept of health

Abstract

Through hypothetical scenarios, this paper analyses whether machine learning (ML) could resolve one of the main shortcomings present in Christopher Boorse’s Biostatistical Theory of health (BST). In doing so, it foregrounds the boundaries and challenges of employing ML in formulating a naturalist (i.e., prima facie value-free) definition of health. The paper argues that a sweeping dataist approach cannot fully make the BST truly naturalistic, as prior theories and values persist. It also points out that supervised learning introduces circularity, rendering it incompatible with a naturalistic perspective. Additionally, it underscores the need for pre-existing auxiliary theories to assess results from unsupervised learning. It emphasizes the importance of understanding the epistemological entanglements between data and data processing methods to manage expectations about what data patterns can predict. In conclusion, the paper argues against delegating the final authority for defining complex concepts like health to AI systems, as it necessitates ethical judgment and capacities for deliberation that AI currently lacks. It also warns against granting creators and deployers of AI systems the discretionary authority to determine these definitions outside the wider social discussion, advocating for ongoing public engagement on normative notions. Failure to do so risks limiting individuals and collectives’ ability to shape a just digital future and diminishes their fundamental epistemic agency.