Epistemic Infrastructure
The background scaffolding of concepts, institutions, and practices that make knowledge possible.
Theory
Epistemic infrastructure denotes the largely invisible arrangements — categories, standards, records, review processes, training pipelines — through which claims come to count as knowledge. It is what makes an assertion assessable at all.
AI systems both depend on and transform epistemic infrastructure: they inherit its categories, exploit its records, and, through deployment, reshape the practices that produced them. This creates a reflexive loop that classical accounts of evidence do not anticipate.
Treating epistemic infrastructure as a first-class object of philosophical analysis reframes debates about bias, generalisation, and trust — moving them from properties of models to properties of the arrangements in which models are trained, deployed, and audited.
Key publications
- Programmatic paper on epistemic infrastructure and AI (AI and Ethics).
- Analysis of infrastructural presuppositions in machine-learning evaluation (Philosophies).
- Historical and theological reflections on institutional knowledge practices (Studia Theologica).
A continually updated record is available through the publications page.
Current work
- An extended argument that AI safety and AI epistemology are inseparable from the maintenance of epistemic infrastructure.
- Empirical mapping of infrastructural dependencies in clinical AI deployments.
- Consultative work with academic institutions on infrastructural conditions for responsible AI research.