Definition
The model outputs are inaccurate or do not meet the performance thresholds required to ensure fitness for purpose.
Controls & guardrails that address this
12Grouped by control function, with the AI lifecycle stage(s) to apply each and the other risks it addresses. Filter by control category below.
Fine-tune on domain-specific, high-quality data to improve model performance on target tasks. Validate accuracy post fine-tuning.
Apply regularisation (L1/L2, dropout, early stopping) to prevent overfitting and improve generalisation.
Prefer smaller, purpose-built models where accuracy requirements are met, to reduce complexity and maintenance burden.
Verify training data covers all material input segments for the target use case. Augment where coverage gaps are found.
Calibrate model outputs to align stated confidence with actual accuracy. Validate calibration on held-out data.
Define quantitative accuracy acceptance thresholds at design stage calibrated to business impact and regulatory requirements.
Configure output confidence thresholds at deployment to suppress or escalate low-confidence outputs to human review.
Route high-consequence or low-confidence outputs to human review in production. Track override rates and outcomes.
Disclose known accuracy limitations and confidence levels to users at deployment. Update disclosures when model changes.
Define accuracy acceptance criteria before validation. Conduct multi-metric validation against hold-out sets. Block deployment if criteria are not met.
Construct synthetic edge-case evaluation datasets to stress-test model boundaries and identify accuracy failure modes.
Establish a periodic revalidation and improvement cycle using RLHF or user feedback. Retrain when accuracy trends below threshold.