AI Trust and the Meaning Layer: A Practice Theory Reframe
For an LLM to be trusted to act as expected, it needs a full understanding of what is being asked and all of the context that is implicit — a meaning layer. The challenge has been how to express that fully in a standardized way. This essay looks to the human social sciences for the answer, drawing on practice theory (Schatzki, Reckwitz, Shove) to introduce the concept of a practice recipe — a text-and-engagement form of apprenticeship through which an LLM is cued into a practice rather than taught it. Unlike a carpenter who serves many clients, an LLM instance is enacted as a unique participant for one user, putting every user in the position of having a private practitioner. The essay closes by sketching an autonomic loop in which the recipe maintains itself through judge-and-smoother practices acting on the enactment history. A follow-up essay will treat the autonomic loop in detail.
Authors
- Monyet Batu (ORCID: https://orcid.org/0009-0007-9002-5381)
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2026-05-23
- DOI
- https://doi.org/10.5281/zenodo.20354505
- Primary Topic
- Ethics and Social Impacts of AI
- Type
- preprint