Short abstract

In this talk I discuss synthetic data as an emerging scientific application of generative artificial, frame it as the extension of surrogative forms of reasoning and discuss their possible radicalisation into a new phenomenon we need to address: artificialisation.

Long abstract

In the sciences, synthetic data are increasingly presented as a way to fill in epistemological and as well as ethical gaps. For instance, in the health context increasing contributions present synthetic data as fixes to critical issue of health datasets and science such as bias, lack of representativity, high costs. There are various epistemological implications of synthetic data, but in this talk I will focus on a more general framing: I argue that we should frame these and other applications of generative artificial intelligence and machine learning as an extension of surrogative forms of reasoning in science.

Surrogative reasoning has significant grounding in recent scientific practice. For instance, the scientific study of health has a history of using technologies and tools that mediate between scientific interests and world phenomena and work as surrogative systems that are more directly accessible or easier to manipulate experimentally than the target systems under investigation. Synthetic data give us a clear opportunity to study new tools for surrogate reasoning, but they also push towards a new radicalisation that I call 'artificialisation', an extension of surrogative reasoning where generative artificial intelligence and machine learning are used to study phenomena with indirect tools and technologies. Artificialisation raises issues we need to tackle – to understand if artificialisation presents a problematic decoupling of data generation from empirical observation and whether it produces recursive scientific forms of practices, with the risk of generating 'artificious' science.