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Healing takes a scan’s findings and produces a cured copy of the dataset with the bad samples either fixed or removed. You choose which fixes to apply; every action is captured in the audit trail.

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When to heal

  • After a mislabel or mislabel_broad scan, to relabel suspects and drop OOD samples.
  • After a poisoning scan, to drop tampered samples before training.
  • After a text_analysis scan, to redact secrets and remove injection paragraphs from a fine‑tune corpus.
  • After a bias_shortcut scan, to apply the recommended mitigations (cropping, dropping, rebalancing).

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What the toggles do

Toggles are independent. You can drop poisoned samples without touching mislabels, or relabel mislabels without dropping anything.

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How to heal

1

Open the source

Start from either the dataset detail page (Heal) or the scan detail page (Heal from this scan). Healing from a scan pre‑fills the dialog with that scan’s findings.
2

Pick what to apply

Flip the toggles you want. Set a confidence floor if you only want to act on the highest‑confidence findings.
3

Pick the output

Healing always writes a result. You can choose:
  • New child dataset, the original stays untouched. A lineage edge healed_from links the child to the source.
  • New branch on the source, the cured contents live as a branch you can switch between.
A zip download is always offered alongside.
4

(Optional) Auto‑rescan

Toggle Auto‑rescan after healing to immediately run the same engine on the cured output. Useful for verifying the heal moved the dataset out of CRITICAL.
5

Submit and watch

Healing runs as a background job with its own progress stream. The result appears in the dataset’s lineage graph.
When healing for the first time on a noisy dataset, set the confidence floor high (e.g. 0.9) and inspect the cured branch before relabeling everything. Lower the floor incrementally once you trust the engine’s calls.

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Healing for text

Text healing is different from image healing because the unit of repair is the snippet, not the file. Redacted documents keep their original doc_id and offsets so the audit trail can reconstruct what was changed.

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What you get back

Every healing run produces:
  • A new dataset or branch with the cured contents.
  • A zip download of the result.
  • An action record in the audit trail (who triggered it, what toggles were set, which scan it descended from).
  • A lineage edge linking the cured output back to the source dataset.
  • (Optional) a follow‑up scan if you enabled auto‑rescan.

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Common workflows

  1. Run mislabel_broad and poisoning.
  2. Heal with Remove outliers, Remove poisoned, and Fix mislabeled at confidence floor 0.85.
  3. Enable Auto‑rescan and confirm the result is HEALTHY or UNHEALTHY−.
  4. Train on the cured branch.
  1. Run text_analysis.
  2. Heal with Redact secrets and Strip injections.
  3. Re‑run text_analysis to confirm zero CRITICAL findings remain.
  4. Export the cured corpus.
  1. Run mislabel.
  2. Heal only the top 10% of suspects (high confidence floor) into a new branch.
  3. Have a reviewer compare the original and cured branches side by side.
  4. Lower the confidence floor for the next pass once the reviewer signs off.

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What healing does not do

  • It does not modify the original dataset unless you explicitly pick “main branch” as the output target.
  • It does not run a fresh scan automatically (you have to opt in via Auto‑rescan).
  • It does not delete the source scan, so you can re‑heal with different toggles at any time.