TDM·AI

TDM·AI is a protocol for attaching machine-readable text-and-data-mining and AI-training preferences to individual digital assets rather than to whole domains. It uses content-derived identifiers and verifiable credentials so declarations can remain tied to a work even when files move between platforms or lose embedded metadata.

The project is especially relevant where domain-level tools like robots.txt are too coarse, but it is still emerging and closely coupled to draft-stage vocabulary work elsewhere in the standards ecosystem.

TDM·AI publishes or proposes a machine-readable preference signal for multimodal material across the collection, training, and fine-tuning stages. It also incorporates protocol standardization. The signal communicates requested conditions; compliance depends on discovery, interpretation, and voluntary support by downstream systems. Public materials describe an in-progress proposal or implementation; the newest dated source in this profile is “Usage vocabulary updated” (November 4, 2025). These details describe the published mechanism and evidence, not a finding about legal validity, adoption, or effectiveness.