Caveats that Hold Up: How to State Assumptions and Limitations without Losing Clarity

Why Caveats Matter—and What “Clarity-Friendly” Looks Like

Caveats are not disclaimers stapled on at the end; they are the visible logic of your claim. When used well, caveats protect both accuracy and credibility without diluting the message. They tell a reader exactly what the claim covers, what it assumes, where it may not apply, and what risk remains even if everything is correct. This does two crucial jobs. First, it aligns expectations: readers know what confidence to place in the claim and where vigilance is still needed. Second, it creates an audit trail: reviewers can verify that the claim, its conditions, and its evidence fit together.

Clarity-friendly caveats are specific, testable, and concise. They avoid vague hedges like “may” or “could” unless those words are anchored to measurable conditions. They distinguish between what is unknown and what is simply outside the chosen scope. They use consistent metrics so that a reader can compare apples to apples across documents. Above all, they are written so that a non-expert can follow the chain from claim to support without decoding legalese.

To operationalize this, use the SALR mini-template: Scope, Assumptions, Limitations, Residual Risk. SALR ensures that you cover the full surface of responsible qualification without scattering detail. Each element has a single job:

• Scope defines the boundaries of the claim—what is in and what is out.
• Assumptions state the conditions believed to be true when the claim is made.
• Limitations identify where the method, data, or context can fail or underperform.
• Residual Risk explains what could still go wrong after accounting for scope, assumptions, and limitations, and how significant that risk is.

When SALR is applied consistently, readers know exactly where to look for the kind of information they need. Regulators, auditors, and internal stakeholders can check alignment across documents because each caveat element is in a predictable place and uses consistent language. This structure does not lengthen your document; it actually compresses ambiguity by allocating each idea to its logical home.

Calibrated Phrasing for Each SALR Element (with Jurisdictional Nuance)

Precise wording matters. Calibrated language balances clarity and defensibility by using verbs, quantifiers, and qualifiers that reflect the underlying evidence. This section offers phrasing options that are appropriate for US and UK regulatory contexts, noting subtle differences in tone and legal convention. The goal is to match your words to what you can prove, neither overclaiming nor under-informing.

Scope: Draw the Line So Readers Can See It

Scope should be explicit and bounded. Avoid open-ended categories or indefinite terms. State inclusion and exclusion using concrete attributes and time bounds. Useful tools include:

• Quantified ranges (e.g., data time windows, model versions, throughput limits)
• Operational definitions (what counts as “out of distribution,” what “production environment” means)
• Jurisdictional coverage (markets, languages, sectors)

Calibrated phrasing patterns:

• US-oriented: “This statement applies to [defined system/version] operating on [datasets/timeframe] under [operational conditions].”
• UK-oriented: “This assessment relates to [defined system/version], evaluated during [timeframe], under [specified operating conditions].”

Note the tonal difference: US formulations often stress applicability and conditions (“applies to”), while UK formulations often prefer the more neutral “relates to” or “pertains to.” Both are acceptable, but matching local convention reduces friction.

Assumptions: Name the Conditions, Don’t Hide Them

Assumptions are not caveats about weakness; they are statements of the world you expect. They must be observable or verifiable. Use modal verbs and qualifiers sparingly and tie them to specific checks. Strong tools include:

• Verifiable conditions: “Assumes inputs are pre-processed with [method],” “Assumes user age is verified by [process].”
• Trigger conditions: “Assumes network latency ≤ [threshold].”
• Maintenance conditions: “Assumes model parameters unchanged since [date/version].”

Calibrated phrasing patterns:

• US-oriented: “We assume [condition], verified by [control/test], as of [date].”
• UK-oriented: “This conclusion is made on the basis that [condition] holds, as evidenced by [control/test] at [date].”

Modal verbs: Prefer “assumes/contingent on” over “may.” “May” is too elastic; it signals uncertainty without locating it. Use “if/when” clauses to anchor the assumption to a testable state: “If inputs deviate from [X], performance metrics do not apply.”

Limitations: Be Concrete About Where Methods Strain or Break

Limitations should be framed as specific boundary conditions, not generic warnings. Reference the mechanism of failure or degradation when possible. Good techniques:

• Mechanism-linked statements: “Performance degrades with [factor], due to [reason].”
• Quantitative thresholds: “Below [sample size/coverage], confidence intervals exceed [width].”
• Data lineage constraints: “Not validated on [population/domain]; results should not be extrapolated.”

Calibrated phrasing patterns:

• US-oriented: “This method is limited by [factor]; outside [range], estimated error increases by [magnitude].”
• UK-oriented: “The analysis is limited in respect of [factor]; beyond [range], the expected error increases by approximately [magnitude].”

Safe-harbour qualifiers: In the US, it is common to include forward-looking safe-harbour clauses that distinguish expectations from guarantees (“are expected to,” “subject to change based on,” “based on information currently available”). In the UK, a balanced, evidence-led tone is often preferred, avoiding promotional or speculative language and favouring precise, past- or present-tense descriptions supported by references.

Residual Risk: Name What’s Left and How Big It Is

Residual risk is what remains even if your scope, assumptions, and limitations are all satisfied. It should be characterized in terms of likelihood and impact, ideally with quantitative anchors or bands. Useful tools:

• Frequency descriptors mapped to probabilities: “rare (~<1%), occasional (~1–5%), likely (~>5–20%), frequent (>20%).”
• Impact tiers: operational disruption, legal exposure, safety harm, financial loss—each tied to a scale.
• Risk control references: monitoring cadence, alert thresholds, fallback procedures.

Calibrated phrasing patterns:

• US-oriented: “Residual risks include [risk], with an estimated likelihood of [band] and impact of [tier]. These are monitored by [control] at [cadence].”
• UK-oriented: “Residual risks comprise [risk], with likelihood assessed at [band] and impact assessed as [tier]; these are monitored via [control] at [frequency].”

Jurisdiction note: Avoid implying zero risk. Both US and UK regulators scrutinise claims of certainty. Prefer calibrated probability bands and link them to documented methods of estimation. Where quantification is not yet possible, state the qualitative rationale and any planned measurement improvements.

Language Toolkit: Modals, Qualifiers, Quantifiers, and Jurisdiction-Sensitive Phrases

• Modal verbs: Use “is expected to,” “is designed to,” “is supported by,” “is contingent on,” rather than “will.” “Will” reads as a guarantee and can misstate uncertainty.
• Safe-harbour qualifiers: “Based on information available as of [date],” “Subject to change following [event/update],” “Forward-looking estimates reflect current assumptions and may be revised.” These frame time-boundedness and update intent.
• Quantifiers: Tie words like “most,” “rare,” “significant,” to numeric ranges, and disclose the denominator (sample size, period, population).
• Jurisdiction-sensitive phrases: In the US, terms like “material,” “reasonable basis,” “best available data” carry legal resonance. In the UK, phrases such as “to the extent reasonably practicable,” “having exercised due skill, care and diligence,” and “on the evidence reviewed” are commonly used. Choose phrasing that aligns with your legal counsel’s guidance and the regulatory regime you face.

Placement, Formatting, and Evidence-Linking for Readability and Defensibility

Caveats fail if nobody sees them or if they are too dense to understand. Placement and formatting should make caveats visible at the point of decision without overwhelming the main narrative. Your objective is dual: make caveats noticed and make them auditable.

Placement principles:

• Proximity to claim: Put the SALR caveat immediately after or adjacent to the claim it qualifies. Avoid relegating core caveats to appendices only. If space is tight, use a concise inline SALR and link to a detailed annex.
• Layered disclosure: Use progressive detail—summary SALR in the main text, with hyperlinks or references to technical annexes, data cards, or model cards containing full metrics and methods.
• Consistency across artifacts: Use the same SALR headings and ordering across reports, dashboards, and release notes. Consistency speeds comprehension and reduces misalignment.

Formatting techniques:

• Clear headings: Label each element explicitly: “Scope,” “Assumptions,” “Limitations,” “Residual Risk.” Headings make scanning easy and reduce misinterpretation.
• Bullet lists with parallel structure: Keep items brief and start with the variable or condition. Parallelism helps readers compare points quickly.
• Metric-forward phrasing: Include key numbers where readers expect them—after the claim or at the start of the caveat line—so they don’t need to hunt.
• Versioning and dating: Display model/version IDs, dataset snapshots, and last-updated dates near the SALR block. This enables evidence traceability.

Evidence-linking:

• Direct citations to evidence: Link metrics to their sources (evaluation reports, test scripts, datasets). Use stable identifiers or document IDs, not just URLs, to survive content moves.
• Method traceability: Briefly name the evaluation protocol (e.g., “stratified holdout test,” “red-team scenario set v3”) and the governance process that approved it. This creates a chain from claim to method to oversight.
• Control linkage: For residual risks, point to monitoring dashboards, alert thresholds, playbooks, and issue trackers. This shows that risk is being watched, not merely named.

Make caveats scannable without sacrificing substance. Readers should be able to answer in seconds: What is being claimed? Under what conditions? Where does it struggle? What could still go wrong, and how are we watching it? If they can’t, the caveat needs restructuring.

Practice Through Transformation: From Overconfidence to Regulator-Ready

Turning an overconfident claim into a regulator-ready statement is a structured task. SALR provides the checklist; calibrated language supplies the words. The process is iterative and evidence-driven.

First, identify the kernel of the claim: what outcome, to whom, under what use case. Then map SALR:

• Scope: Specify the dataset, time window, model or system version, user group, and operational conditions that define the claim’s validity. If your product operates across regions, add jurisdictional scope and any language or legal constraints.
• Assumptions: Document the conditions you rely on—data pre-processing, user behaviour bounds, infrastructure reliability, and model configuration stability. For each assumption, link a verification method or control that confirms it holds at the time of the claim.
• Limitations: State the failure modes and performance cliffs with quantified thresholds where possible. Identify any externalities (e.g., data drift, novel attack patterns) that your evaluation did not—or could not—cover.
• Residual Risk: Characterise the remaining risk in likelihood and impact terms, based on observed frequencies or expert-elicited ranges, and state how it is monitored. If mitigations exist, name them and their activation triggers.

Calibrated language choices are then applied to each SALR element. Avoid categorical verbs that over-promise; prefer verbs that reflect design intent or evidence-supported expectations. Replace undefined comparatives (“better,” “robust,” “safe”) with metrics and benchmarks. Use quantifiers and date-stamps to show when and where the data applies.

Finally, check placement and formatting. The SALR block should sit immediately under the claim or as an inset box alongside it, with links to the technical annex and monitoring references. Ensure headings are present, bullets are parallel, and each bullet has either a number, a threshold, or a link to one.

To keep future updates efficient, standardise a SALR template within your documentation system. Include fields for version, date, dataset ID, protocol ID, and monitoring link. This reduces the chance of drift between claims and evidence over time.

Quick Self-Audit Rubric: Are Your Caveats Regulator-Ready?

Use this rubric before publishing. If any answer is “no,” revise until it is “yes.”

• Alignment

  • Does the claim’s wording match the evidence and metrics you have? Are all numbers traceable to a current, identifiable source?
  • Are units, denominators, and timeframes consistent across the document?

• SALR Completeness

  • Scope: Is it clear what is included and excluded, with concrete boundaries and dates?
  • Assumptions: Are conditions explicit, verifiable, and linked to controls?
  • Limitations: Are failure modes and thresholds identified without vague language?
  • Residual Risk: Is remaining risk described with likelihood/impact and monitoring plans?

• Calibrated Language

  • Are modal verbs and qualifiers proportionate to the evidence? Are safe-harbour phrases correctly time-bounded?
  • Are quantifiers defined numerically or by clear bands? Are jurisdiction-sensitive phrases appropriate to the audience?

• Placement and Formatting

  • Is the SALR caveat co-located with the claim, not buried in an appendix?
  • Are headings, bullets, and version/date stamps present and consistent?

• Evidence Linkage

  • Do all key assertions link to methods, datasets, or controls with stable identifiers?
  • Is there a clear path for auditors to reproduce or verify metrics?

• Consistency and Maintenance

  • Do claim versions, model versions, and dataset snapshots align? Are update triggers and dates documented?
  • Are monitoring dashboards and playbooks referenced and accessible to reviewers?

By treating caveats as structured, visible logic rather than afterthoughts, you avoid two common failure modes: under-qualification that erodes trust and over-qualification that buries meaning. The SALR template disciplines your thinking; calibrated language right-sizes your promises; careful placement and linkage make your statements both readable and defensible. Together, these practices produce caveats that hold up—under scrutiny from regulators, in the hands of stakeholders, and across the lifecycle of models and decisions.