Closeness of outputs to true values; a metric family in ML, a legal duty in the AI Act, a data-protection principle in GDPR.
In newsroom practice, accuracy is claim-level correctness established by verification: every name, number, quote, and causal assertion checked against sources before publication, with corrections issued when checks fail. It is binary per claim and reputational in aggregate. Generative tools enter this regime as unverified drafts — fluent text with no warrant behind any particular claim — so editorial accuracy work shifts to systematic fact-checking of machine output, and no statistical accuracy score substitutes for the check.
In practice: Verify each factual claim in a story or AI draft against an identifiable source before publication, and correct the record visibly when verification fails.
OmniGloss seed synthesis, 2026 (machine-drafted, pending expert validation)
Under model-risk management, accuracy is a statistical property of a model over a portfolio, demonstrated continuously rather than claimed once: discriminatory power (Gini, KS) on out-of-time samples, calibration of predicted default rates against realized outcomes, and back-testing with documented tolerance bands. Individual misclassifications are expected and priced; what triggers escalation is aggregate deterioration beyond thresholds set in the monitoring plan. Accuracy evidence lives in validation reports and ongoing outcomes analysis, owned by an independent function.
In practice: Backtest model predictions against realized outcomes, monitor discrimination and calibration against documented thresholds, and escalate breaches through the model-risk framework.
OmniGloss seed synthesis, 2026 (machine-drafted, pending expert validation)
For compliance functions deploying high-risk AI such as creditworthiness scoring, accuracy is a declared, contractual property: the AI Act obliges providers to achieve an appropriate level of accuracy and to state the levels and metrics achieved in the instructions of use, and obliges the deploying firm to operate the system within that declared envelope throughout its lifecycle. Accuracy here is an artefact of documentation and conformity assessment — a number the firm can be held to — not an internal modelling statistic.
In practice: Verify that declared accuracy levels and metrics accompany a high-risk AI system, and evidence that operational use stays within the declared envelope throughout the lifecycle.
Regulation (EU) 2024/1689 (AI Act), Art. 15
In clinical validation, accuracy is a family of paired measures, never one number: sensitivity and specificity at a chosen operating point, predictive values at realistic prevalence, calibration of risk estimates, and discrimination across the intended population. Which member of the family governs depends on the clinical cost asymmetry — a missed cancer is not a false alarm — and aggregate figures must be broken out by subgroup, since a device accurate on average can be inaccurate for the patients in front of you.
In practice: Select accuracy metrics matched to the clinical cost of each error type, evaluate them at realistic prevalence and by subgroup, and refuse a single aggregate figure as evidence.
OmniGloss seed synthesis, 2026 (machine-drafted, pending expert validation)
In administrative case handling, accuracy is a data-protection principle attached to each record about each person: personal data must be accurate and kept up to date, inaccurate data must be rectified or erased without delay, and the data subject holds a right to demand correction. A register can be statistically excellent and still unlawful for the one citizen whose record is wrong; accuracy obligations bite record by record, enforced through complaints, rectification procedures, and supervisory authorities.
In practice: Check the accuracy of the specific records a decision relies on, execute rectification requests without delay, and document how disputed data was corrected across linked registers.
Regulation (EU) 2016/679 (GDPR), Art. 5(1)(d)
In official statistics, accuracy is a measured quality dimension of published estimates: closeness to the true population value, decomposed into sampling error, reported through confidence intervals and coefficients of variation, and non-sampling error from coverage, nonresponse, and measurement. Revisions policy is part of the operationalization — early estimates carry known accuracy limits, and the size of subsequent revisions is itself published as an accuracy indicator. Accuracy is documented in quality reports, not asserted.
In practice: Quantify and publish sampling and non-sampling error for each estimate, maintain a revisions analysis, and document accuracy in the quality report accompanying the release.
OmniGloss seed synthesis, 2026 (machine-drafted, pending expert validation)
Communities draw the boundary of 'accuracy' around different objects. Model-risk and machine-learning practice defines it as a distributional property of a system over a population — a rate monitored within tolerance bands, under which individual errors are expected and managed. Data-protection practice defines it as a property of each stored record about each person, enforceable individually through rectification rights. A system can satisfy one reading while violating the other.