[Notes] Fine-Grained Traceability for Transparent ML Pipelines

Fine-Grained Traceability for Transparent ML Pipelines (Chen, Liu, Fayek)

The key idea in FG-Trac is the distinction between explainability and operational traceability. Influence methods such as TracInCP can retrospectively estimate how training samples affect predictions, but they do not establish when, where, and under which model state those samples actually participated. FG-Trac instruments the ML pipeline itself, recording sample lifecycle events and binding checkpoint-based influence scores to those events.

These records are committed through Merkle trees, with detailed encrypted logs kept off-chain and only constant-sized roots anchored on-chain. This lets a user trace a pseudonymous sample through training, inspect its positive or negative influence, and cryptographically verify that the resulting history has not been modified.

The important limitation is that the commitment proves integrity of the recorded history, not completeness or truthfulness of its creation; a malicious operator could bypass instrumentation before commitment. Hardware-backed attestation or TEEs could strengthen that boundary.

For a related approach combining hardware-backed attestation and transparency logs to authenticate ML transformation history, see my notes on Atlas.