Lints a supplied generative-AI training-data disclosure against the 12 datapoint categories required by California AB 2013 (Cal. Bus. & Prof. Code §22757.7, effective 2026-01-01): dataset sources/owners, purpose alignment, datapoint counts and types, IP status, licensing, personal/aggregate-consumer-information inclusion, cleaning/processing description, synthetic-data use, and collection time period/dates. Per-datapoint present/missing findings; DRAFT-PINNED against secondary-source statute summaries, not a primary-text re-read. Asserts the supplied disclosure replays to this coverage finding, never that the developer is AB 2013 compliant.
Copy this paragraph into Claude, OpenClaw, or any MCP-aware agent to run this exact tool, with this sample, and verify the artifact.
Run the AINumbers MCP tool `lint_ab2013_training_data_disclosure`. Task: Lint a supplied generative-AI training-data disclosure against the 12 datapoint categories required by California AB 2013 (eff.
Call it with arguments: {"policy_parameters":{}}
Verify before trusting: call `verify_execution_hash` on mcp.ainumbers.co (https://mcp.ainumbers.co/mcp) with the parameter `claimed_hash` set to the returned `execution_hash`, passing the full artifact the run returned (the object containing `policy_parameters` + `output_payload` + `execution_hash`; equivalently `policy_parameters` + `output_payload` with `claimed_hash`), not the bare hash string.
Return the ledger link https://ledger.ainumbers.co/ so a human can re-verify without contacting us.
PII rule: All inputs are processed locally in your browser. No data is transmitted. Do not enter real personal data — use synthetic or anonymised inputs only.
Open the tool with the sample prefilled: https://ainumbers.co/chaingraph/art-315-ab2013-training-data-disclosure-linter.html#p=v1.H4sIAAAAAAAA_wECAP3_e31Dv6ajAgAAAA