Can AI provenance inform archival provenance?

This contribution argues that the practice of data provenance developed in critical research on artificial intelligence (AI), especially around publicly accessible data documentation, could inform a more transparent practice of provenance in archives. To set the ground for this cross-field exchange, I trace frameworks related to provenance, first from archival studies and then from AI/machine learning (ML). I ask how these frameworks overlap and diverge and how they have “flowed” or been assimilated into and adopted by the other field. I show how archival theories have been animated in documenting data in the context of AI and ML, including through documentation frameworks like Datasheets for Datasets, and then reverse that flow of knowledge, arguing that a practice of provenance developed in AI could forward some of the evolving goals of archival provenance, including those related to traceability and transparency.