Complete the governance instrument family: DPP, IAE, attribution-FAR - #250
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Add three instruments under `flywheel gov`, each re-derivable and shipping its does-not-prove line: - DPP (dataset provenance): verify a dataset's declared shard hashes and origins against the shards an evaluation observed; flag hash mismatch, undeclared and unknown-origin shards, and duplicate content. MATCH / DRIFT / UNVERIFIABLE. - IAE (intervention ablation): the paired-ablation test of an alignment intervention against its ablated counterpart; reports the misalignment-rate effect and a REDUCES / NO_EFFECT / INCREASES verdict, so a backfiring or inert intervention is a finding, not a silence. - attribution-FAR: check the checker; measure how often an attribution method accepts a known-null control. RELIABLE / UNRELIABLE over the control corpus. Wire all three into the CLI with verdict-shaped exit codes and cover them with unit and CLI tests. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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What
Three re-derivable instruments under
flywheel gov, each shipping its does-not-prove line and covered by unit + CLI tests.dataset_provenance.py,gov dpp) — verify a dataset's declared shard hashes and origins against the shards an evaluation actually observed; flag hash mismatch, undeclared shards, unknown-origin shards, and duplicate content.MATCH / DRIFT / UNVERIFIABLE. Motivated by the "distilled frontier data of unknown origin" gap and the local-first enterprise data-as-asset market.intervention_ablation.py,gov iae) — the paired-ablation test (an alignment intervention vs its ablated counterpart, misalignment judged per trajectory by a separate check); reports the misalignment-rate effect and aREDUCES / NO_EFFECT / INCREASESverdict, so a backfiring or inert intervention is a finding, not a silence. This is Daniel Tan's experiment as an instrument.attribution_far.py,gov far) — check the checker: measure how often an attribution method accepts a known-null control (false accept).RELIABLE / UNRELIABLEover the control corpus. The false-accept-corpus discipline applied to attribution.Why
These complete the model-level instrument family (EIM, CED, NCEC, ERG, IGAP, SMP, ICP + these three). They keep the shared discipline: deterministic given the input, a verdict lattice, honest does-not-prove bounds, and a false-success control (compliance/misalignment/attribution judged by a separate signal, never self-confirming). attribution-FAR in particular makes the family's own attribution claims checkable — robust to the conflict-of-interest and capture concerns that motivate independent, re-derivable evaluation.
Tests
tests/test_dataset_provenance.py,tests/test_intervention_ablation.py,tests/test_attribution_far.py: verdict paths, computations, both-condition / min-control requirements, malformed inputs, threshold ranges, and thegov dpp|iae|farCLI (exit codes + dispatch throughcli_entry). Full slice green; file-size gate clean; writing lint clean.🤖 Generated with Claude Code