Skip to main content

Trust Index Governance

Trust Index limitations & response policies

How Lawnise publishes Trust Index scores, what we name + what we anonymise, how the right-to-reply window works, how entity opt-out is processed, and how corrections are handled. The methodology underwriting every score is at /trust-index/methodology/v1.

About this page

The Lawnise Trust Index publishes how accurately AI systems answer questions about regulated industries. Every score we publish carries a confidence interval, a methodology reference, and an evidence chain. This page explains the limits of what we publish, the policies that govern when entities can be named, and the procedures we follow when someone disagrees with a published claim.

It applies to every Lawnise Trust Index publication — scores, derived reports, embargoed previews, and any artifact that cites a Lawnise Trust Index score externally.

If you are looking for how the Trust Index is calculated, read our methodology. If you want to verify a published score directly, see the verification section below.

What we publish, and what we don't

Per-jurisdiction aggregates

The default unit of a Lawnise Trust Index publication is a per-jurisdiction aggregate. A typical score reads "Malaysian banking-sector AI accuracy, March 2026: 73% ± 6 percentage points (preliminary)." The headline is anchored to a jurisdiction, a sector, and a time period — never to a specific named institution in a way that attributes a low score, a regulatory shortfall, or a factual error to that institution.

We report at three levels of detail in any publication:

  • A jurisdiction-wide headline (the figure most likely to be cited).
  • Sector breakdowns within a jurisdiction (banking, insurance, capital markets) where coverage permits.
  • AI-provider breakdowns within the jurisdiction (showing how different AI systems compare on the same prompt set).

We do not rank jurisdictions against each other in version 1.0 of our methodology. Each jurisdiction is presented on its own terms with its own confidence interval and sample-quality footnotes. Cross-jurisdiction comparisons are valid only at the trend level (for example, "both Malaysia and Singapore showed improving accuracy over the past six months") and never as ranked comparisons.

When we name an entity (factual-preservation carve-out)

We may name a specific entity only when doing so preserves a positive or neutral factual record — for example, confirming that a regulator's published deposit-insurance limit was accurately reflected across all four AI surfaces we scanned, with the institution that holds that policy named for context. This carve-out is narrow and deliberate:

  • it never names an entity in a negative or below-average context;
  • every named callout is reviewed independently by our governance team before publication;
  • the entity has not opted out of Lawnise scanning;
  • it is rare in practice. Our inaugural publication does not use this carve-out at all.

How we keep entity names out of negative claims

The baseline rule

No public Lawnise Trust Index artifact names a specific institution in connection with a negative finding, a below-average score, a regulatory shortfall, or any other adverse outcome. This is a hard rule, not a guideline. It applies uniformly across:

  • the headline /trust-index page,
  • any downloadable Truth Report,
  • press releases and embargoed previews,
  • regulator-facing summaries and citation responses,
  • charts, graphics, and social-media assets.

When a claim risks identifying an entity in a negative context — even indirectly through inference from sector size or market structure — our remediation options are, in order of preference: aggregate the finding to a higher level (sector or jurisdiction), anonymise the entity if the data is still useful, or remove the claim entirely. We never name an entity to make a negative finding more "concrete" or "newsworthy."

Small-market considerations

In a small market, even an "anonymised" distribution can point to a single institution if a careful reader brings external knowledge. Two safeguards address this:

First, we suppress any sector-level distribution where fewer than five entities contribute data for the period. If only three or four institutions in a jurisdiction's sector have been scanned, we publish the jurisdiction-wide figure but withhold the sector breakdown.

Second, we apply extra reviewer scrutiny to any far-outlier score — defined as more than two standard deviations below the sector mean — in a small market. Where re-identification risk cannot be eliminated by aggregation, we withhold that breakdown from publication.

These rules apply equally to extreme positive outliers, even though they do not raise defamation risk, to keep the published distribution self-consistent.

Right to reply

What it covers

If your entity is named in a Lawnise Trust Index publication, or you believe your entity is identifiable from an anonymised aggregate, you have the right to reply. The right to reply covers:

  • a factual error in the reference data Lawnise used about your entity (for example, an outdated deposit-insurance limit);
  • a published claim about your entity that you dispute on factual grounds;
  • an anonymised aggregate finding from which you believe your entity can be re-identified, especially in small markets.

It does not cover:

  • disagreement with the Lawnise methodology itself — methodology changes follow the methodology versioning process and are not handled through right-to-reply;
  • requests to suppress an unfavourable score that is factually accurate;
  • pre-publication preview rights, advance notice of upcoming publications, or veto over publication timing.

Timing

We commit to:

  • Acknowledge receipt of a legitimate right-to-reply within 5 business days of intake.
  • Publish your response — once you submit one and it passes our reply-review process — within 30 calendar days of receiving your formal response.

What we offer to you:

  • A 15-business-day window after our acknowledgement to submit a formal response. If you need more time, ask before the window closes — we consider extensions case by case based on the complexity of the matter and the documentation required.

We do not silently edit a published claim. When we publish your reply, it appears as a new record alongside the original claim, with its own publication date and audit reference. The original record remains visible. This applies whether the reply confirms our finding, disputes it, or supplements it with additional context.

How to submit

Please include your name and role, the entity you represent, what is being disputed, and (where applicable) the specific claim or score reference. We classify legitimate intake within one business day. The 5-business-day acknowledgement clock is measured from receipt.

Entity opt-out

What opt-out does

If your entity does not want to participate in future Lawnise Trust Index branded scanning, you can opt out. Once we verify the request, we exclude your entity from all future branded scans within 30 calendar days of verified intake. Your entity name and per-entity scores never appear in future public Trust Index output.

Opt-out applies to our entity-level benchmark stream (the Market Accuracy Benchmark, which uses branded prompts about specific institutions). It does not apply to industry-level Trust Index reporting, which uses non-branded prompts and industry-level reference facts and does not include named entities in its inputs. The industry-level stream does not need opt-out because your entity is not in it.

Opt-out is reversible. You can request reinstatement at any time, and we will apply the same verification process and reinstate your entity in the next scan dispatch after we verify.

How we verify the requester

Because opt-out is durable, we apply two-factor verification before we act:

1. Email domain match — the request arrives from an email address on your entity's official domain. Personal or generic email addresses do not satisfy this factor. 2. Public-role attestation — your role at the entity is confirmed via a public source such as LinkedIn or your company's official website, and is one with reasonable authority to make this request (typically head of compliance, head of communications, legal counsel, or comparable).

If we cannot satisfy both factors within 10 business days of asking, we close the request and let you know. You can resubmit at any time with the additional verification.

Historical data: what we preserve and why

When we honour an opt-out, we retire your entity's internal reference facts and branded benchmark prompts. We do not delete or anonymise historical scan results or reference data. Those records remain unchanged in our internal database for three reasons:

1. Past published scores must remain verifiable against the inputs we actually used at publication time. If we rewrote the historical inputs, the score chain that auditors and regulators rely on would no longer reconcile. 2. Our hash-chain integrity checks would break if the underlying data were edited after the fact. Hash-chain integrity is a public commitment in our methodology, and Lawnise verifies the published score chain operationally on a scheduled basis. 3. Our methodology promises auditors and regulators a fixed historical record. We honour that promise even when it would be simpler to scrub.

This historical preservation is internal-only. Lawnise's public Trust Index outputs do not attribute negative performance to named entities; any positive or neutral factual-preservation callout (the narrow carve-out described above) is reviewer-gated, and we remove the entity from any such forward-looking callout as part of honouring an opt-out. The effect of opt-out, externally, is that your entity is excluded from all future scans and from any future named callout.

To request opt-out, see How to contact us.

How we correct errors

When a published score or claim turns out to be wrong, we issue a correction as a new record. We do not silently edit the original. Three patterns govern our response:

Calculation correction — a bug in our scoring or aggregation logic produced a wrong number. We publish the corrected score with a note explaining what went wrong, what changed, and how the new figure relates to the old one. The original record remains visible with a correction marker.

Input correction — a fact in our reference data was wrong (an outdated regulatory limit, a misclassified product, a transcription error in a source document). We update the reference data, re-run the affected calculation, and publish the corrected score with the same kind of disclosure.

Methodology correction — a flaw in the methodology itself. This is the most significant pattern. We retract affected scores under the current methodology version and publish corrected scores under a new methodology version. Both the retraction notice and the new methodology are public; the corresponding bridge document explains what changed, why, and how the new scores compare to the old.

Every correction and every retraction is published publicly. Retracted scores remain visible with a retraction notice — they are never deleted from the public record. The audit trail behind each correction is preserved for internal governance review and for legal or audit review where appropriate.

Sample size, confidence, and "preliminary" status

Every published Trust Index score includes a confidence interval that reflects how much data we have. The wider the interval, the more cautious the interpretation should be. But interval width is only part of the picture: whether a score qualifies as definitive or preliminary also depends on how many observations contributed to it, how many AI providers and sectors were covered, how many distinct scan sessions and prompts went into the measurement, and how many observations were excluded for infrastructure-failure reasons. Our methodology defines specific thresholds across all of these dimensions, and a score only earns definitive status when every threshold is met. Falling short on any one of them — even with a tight confidence interval — keeps the score at preliminary or below.

Our verification engine itself has known precision limits, which we disclose with every publication:

> _Scores reflect automated verification against curated reference facts. Engine accuracy on the development benchmark is approximately 87%. Results should be interpreted as directional indicators, not absolute measures._

This disclosure is verbatim from our methodology and is mandatory on every Trust Index publication. It means that even a high-confidence Trust Index score is a measurement, not a final truth claim — and our public record is honest about that.

A worked example. A preliminary score might read: "Malaysian banking-sector AI accuracy, March 2026: 73% ± 8 percentage points (preliminary). Based on 28 scored observations across 2 AI providers." A reader should treat this as evidence of where AI accuracy is trending in this sector, not as a precise measurement. The same headline score could be eligible for definitive status if the underlying sample grew substantially in every dimension — more scored observations, broader coverage across AI providers and sectors, more distinct scan sessions and prompts, a low ratio of excluded observations, and a narrower confidence interval. The full thresholds for each status are published in our methodology.

A score we cannot publish with enough confidence to mark preliminary is calculated for internal trend-tracking but is never published externally.

What our public verification does (and doesn't) cover

You can verify any published Trust Index score independently. For our inaugural publication, the public verification surface is score-level: visit verify.lawnise.com to see the latest published Trust Index scores by jurisdiction (Malaysia, Singapore), or follow the jurisdiction-specific verification URL published with each score, such as verify.lawnise.com/index/MY. The verification page returns the latest published score, its confidence interval, and a link to the methodology version that produced it.

We do not yet expose per-claim or per-row verification URLs for inaugural. Per-claim verification is planned for a future iteration of our public verification surface and is not a Lawnise Trust Index inaugural capability.

If a claim in a Truth Report or other publication is challenged, the underlying scan rows and reference facts are reviewer-verified internally before the claim is published. We retain the full evidence trail under our hash-chain integrity discipline, which we verify on a scheduled basis against the published score chain. This means a claim's evidence is reproducible internally even where it is not yet exposed via a public verification URL.

How to contact us

For any Lawnise Trust Index governance matter — right-to-reply, opt-out request, factual correction, or methodology question:

We classify every legitimate inbound message within 1 business day of receipt and confirm the relevant service-level clock with you when we acknowledge. If your matter is urgent — for example, a published claim that you believe is materially harmful and time-sensitive — write "URGENT" in the subject line and we will route the request immediately.

When you write to us, please include the entity you represent, the specific claim or score reference, what is being disputed or requested, and your contact details. The more concrete the initial message, the faster we can confirm scope and start the relevant clock.

For general Lawnise enquiries that are not about Trust Index governance, see our main contact page.

Document version + last reviewed

If you find an error on this page, please email trust-index@lawnise.com referencing "page error" in the subject line, and we will route the correction through our standard publication-error workflow.