Core concepts

Fairness methodology

How every number on the fairness report is computed — data sources, outcome-confirmation rules, windows, sample floors, and the limits of what each figure can support.

Why this page exists

The fairness report publishes accuracy, per-rule precision, and review-parity figures. Each is a claim, and a claim you cannot check is just an assertion. Verify this review shows you how to re-run the corpus yourself; this page tells you what the numbers mean — which rows are counted, which are excluded, over what window, and where each figure stops being able to support a conclusion.

Nothing here is a summary of intent. Every rule below is the rule the code implements.

Want the machine-checkable version? npx -p @loopover/mcp loopover-verify recomputes the published commitments and prints a PASS/FAIL table. It needs no credentials. See Verify this review.

Two surfaces answer different questions, because they hold different data (#9940). Aggregate stats and eval-score records come from api.loopover.ai, the default. The anchored decision ledger lives on the Orb that actually reviews — its own hash chain, signed and anchored to Rekor — so ledger and anchor checks need that one:

npx -p @loopover/mcp loopover-verify                                    # stats + eval scores
npx -p @loopover/mcp loopover-verify --base-url https://shots.loopover.ai   # ledger + anchors

The public API's own ledger is empty by design: it aggregates outcomes reported by Orbs rather than holding their chains. A verifier run against it reports the ledger checks as skipped, not failed.

The three data sources are not interchangeable

This is the single most important thing to understand before reading any number: the fairness report draws on three disjoint sources, and figures from different sources cannot be compared or combined.

Source	What it is	Which figures come from it
Decision ledger (`decision_records`)	The hash-chained, anchored record of every verdict	The review-parity block only: re-evaluation rate, re-evaluations by reason, per-author-class parity
Signal history (human-override audit events)	Human verdicts recorded against individual rule firings	The measured accuracy per rule table, and the reproducibility freeze point
Outcome history (auto-actions + reversal events)	What the gate did, and what humans later did about it	Decision accuracy, the per-repository table, and both weekly trends

A consequence worth stating plainly: only the review-parity block is computed from the anchored ledger. Those figures are the ones an outsider holding the ledger export can recompute exactly. The rule-precision and accuracy figures are computed from audit history, which is subject to a 90-day retention bound and is not itself anchored.

Outcome confirmation: what makes a case confirmed or reversed

Per-rule precision

A rule's precision counts human verdicts recorded against that rule's firings. A verdict is written as reversed when a human undid what the gate did, and confirmed when a human let it stand.

Reversal is recorded when:

A contributor reopens a pull request the gate had closed.
A merged PR's body says it reverts an earlier PR — and only if that earlier PR's merge was actually recorded. An uncorroborated "Reverts #N" records nothing.
A merge supersedes a PR the gate had closed.
The repository owner reopens a closed PR and merges it within six hours. A bare owner reopen is deliberately not a reversal on its own — owners reopen for many reasons — so this is a case where a genuine "the gate was wrong" event can go uncounted.

Confirmation is recorded when:

The repository owner closes a PR without merging, agreeing with the gate's close. A contributor closing their own PR records nothing.
For the AI-judgment rules specifically, an explicit hold confirmation is recorded.
For every other rule, a terminal disposition with no reversal records an implicit confirmation.

Implicit and explicit confirmations are pooled. Implicit confirmations are tagged in the underlying data so a consumer could weight them separately — but the published precision does not. A rule whose confirmations are largely implicit ("nobody undid it") is presented identically to one whose confirmations are explicit ("a human affirmed it"). Those are different strengths of evidence, and the published number does not distinguish them.

Excluded from the precision denominator

Unrecognized verdicts — rows whose verdict is neither confirmed nor reversed. Counted separately, and never allowed to inflate precision or help a rule clear its sample floor.
Non-attributable backfill — one historical backfill took its labels verbatim from another rule's human verdicts on the same pull requests. Including it printed one rule's number twice, so it is excluded from both the precision aggregate and the published corpus.
Firings with no human verdict at all. Most firings never receive one. The absence of an override is not evidence in either direction, so it is not counted as either.

Windows

Every window is flat and unweighted. There is no time-decay and no recency weighting anywhere. A verdict from day 89 counts exactly as much as one from yesterday.

Figure	Window
Per-rule precision, and the published eval corpus	90 days
Review-parity block (re-evaluation rate, author-class parity)	7 days
Weekly trends	8 weeks
Fleet decision accuracy	90 days
Underlying audit-event retention	90 days — a hard bound on everything above it

The review-parity window being 7 days while the rule table is 90 is a real difference, not a rounding of the same thing. The page prints the parity window's actual start and end dates.

The weekly trend buckets a reversal by the original action's date, not the reversal's, so a past week's figure never shifts retroactively as new reversals arrive.

Precision, accuracy, and coverage

Per-rule precision is confirmed / (confirmed + reversed), rounded to three decimals and published as a percentage to one decimal.

Decision accuracy is reversal-grounded: the share of the gate's merge/close actions that were not later reversed. Its denominator is auto-actions in the retention window; dry-run actions are excluded.

Coverage is computed but is not displayed on the fairness report. Where it appears elsewhere it means verdicts / (verdicts + holds) — the share of decidable pull requests the gate actually decided rather than handing to a person. Policy enforcement actions sit outside both the numerator and the denominator. The per-rule table publishes a decided count and no fired-to-decided ratio, so it does not tell you what share of a rule's firings were ever adjudicated.

Confidence intervals

Where an interval is published it is a Wilson score interval at z = 1.96 (95%). Wilson rather than Wald deliberately: the Wald interval degenerates as a proportion approaches 0 or 1 and would claim near-impossible certainty at, say, 59 of 60 confirmed.

What actually carries an interval on the fairness report: the headline decision-accuracy figure, and only when the deployment is reporting as a single instance. Everything else is a bare point estimate:

Per-rule precision has no interval.
The per-repository accuracy column has no interval.
Neither weekly trend has an interval.
No review-parity figure — re-evaluation rate, close rate, hold rate, findings per PR — has an interval.

Read those as point estimates with unstated uncertainty, and lean on the accompanying counts (decided, verdicts, n=) to judge how much weight any of them can carry.

Sample floors and suppression

A figure computed from too few observations is suppressed rather than published, because a small-sample percentage is worse than no percentage at all.

Figure	Floor	What happens below it
Per-rule precision	10 decided	The row still appears with its count; the percentage reads "insufficient data"
Weekly accuracy trend	3 samples	The whole week is blanked, volume columns included
Fleet weekly trend	5 verdicts	Both the verdict count and the accuracy are blanked
An instance counting toward the fleet	5 decided	The instance is excluded entirely
Anti-gaming comparison	3 eligible instances	Flags caught reads as unknown, not zero

Where a deployment is reported alongside a small number of others, some counts are withheld entirely: at two instances, publishing a total would let a reader recover the other instance's volume by subtraction.

Known limitations

These are the places where the published numbers are weaker than they look. They are listed here because a methodology page that omitted them would be marketing.

An accuracy of "—" means not measurable, not 100%. When a deployment took no auto-actions in a period, there is no action a reversal could have been recorded against. Accuracy is unknown, and is published as unknown.
Reversals are undercounted by construction. A bare owner reopen with no merge inside six hours, and an uncorroborated revert claim, both record nothing. Real "the gate was wrong" events can be missed.
The published corpus is capped and can be a prefix. The downloadable corpus is bounded at 2,000 cases, while the precision figures are computed with an unbounded count. When a rule's corpus is truncated or empty, its checksum commitment is omitted rather than published — a commitment covering a prefix of the cases a number was computed over would mislead a reader re-deriving it.
The corpus and the precision aggregate are built by different code paths. A corpus case requires a firing paired to an override; the precision aggregate counts override rows alone. An override with no matching firing counts toward published precision but has no corpus case. Expect the two to be close, not identical.
Backtest scoring is oriented the other way. The scorer used for calibration treats reversed as its positive class, so its precision is not comparable to the published confirmed / decided.
Reads fail soft. A failed database read yields an empty result rather than an error, so a transient failure under-counts a figure rather than surfacing itself. Compare against the counts and the updatedAt timestamp before drawing conclusions from a single load.
Parity counts evaluations, not pull requests. A re-evaluated head SHA is its own row, so verdicts exceeds the number of distinct PRs whenever anything was re-evaluated.
The unknown author class is reported, never folded. A PR whose author association was never recorded gets its own row rather than being bucketed into maintainer or contributor — bucketing it would bias exactly the comparison the block exists to make.

Where this lives in the code

Kept adjacent to the scoring code it describes, so a change to a definition and a change to this page show up in the same review:

Per-rule precision and the provenance exclusions — src/review/public-rule-precision.ts
Outcome-confirmation writers (what makes a case confirmed or reversed) — src/review/outcomes-wire.ts
Accuracy, totals, and the fleet block — src/review/public-stats.ts
Wilson intervals and the fleet fold — src/orb/analytics.ts
Review-parity rollups — src/review/review-parity-rollups.ts
The published corpus and its checksum — src/review/public-eval-corpus.ts

← PreviousVerify this review Next →What you can verify