Misinformation

Implement confidence scoring to communicate output certainty alongside each result. Calibrate before deployment.

Define model accuracy acceptance criteria aligned to business requirements before validation commences.

Implement counterfactual explanation to show users what changes would alter the model's output.

Communicate model accuracy, known limitations, and uncertainty to users in the production interface at launch.

Monitor production accuracy continuously against the validated baseline. Trigger model review when accuracy degrades.

Specify a RAG architecture at design stage for factual domains. Define grounding requirements and acceptable hallucination thresholds.

Evaluate foundation model candidates on hallucination benchmarks at design stage. Select models with lowest documented rates.

Design system prompts to instruct the model to acknowledge uncertainty, cite sources, and refuse when knowledge is insufficient.

Fine-tune on a curated, domain-specific dataset to improve factual accuracy. Validate hallucination rates pre/post fine-tuning.

Configure conversation controls at deployment to restrict the model to approved topic domains and escalate off-topic queries.

Establish acceptable hallucination rate thresholds and grounding requirements as policy before build. Assign a named risk owner.

Configure tiered HITL review for high-stakes factual outputs with defined trigger criteria and reviewer SLAs.

Calibrate the initial entropy threshold on a knowledge-boundary dataset; approve sampling design and thresholds per risk tier.

Map each fact class to a designated tool, embed the no-ungrounded-assertion prompt, and gate build review on grounding tests passing.

Resolve every emitted citation against the approved corpus and verify span-level entailment before display. Strip or withhold claims with fabricated or non-entailing references.

Mandate AI risk awareness training for all use case sponsors and design team members before project kick-off.

Mandate AI risk training for all build and test personnel. Gate project participation on training completion.

Mandate human verification for high-stakes decisions where over-reliance risk is elevated. Review automation bias incidents quarterly.

Surface AI limitation warnings and over-reliance caveats in every production interaction. Update disclosures when model changes.

Require AI governance training for all personnel involved in data acquisition and processing before project participation.

Verify all deployment, operations, and customer-facing team members have completed AI risk training before launch.

Define AI identity disclosure policy at design stage. Specify when and how the system must identify itself as AI.

Plan consent and AI identity disclosure touchpoints in the user journey at design stage.

Design system prompts to explicitly prevent the model from claiming human-like identity or implying sentience.

Implement persistent AI identity disclosures in the UI (opening banner, inline notifications). Test before deployment.

Verify all AI identity disclosure elements are live, accurate, and prominently visible before go-live.

Monitor production for anthropomorphism incidents. Escalate complaints where users believed they were interacting with a human.

Apply post-training calibration (temperature scaling, isotonic regression) to align confidence scores with accuracy. Validate ECE before deployment.

Classify the use case by consequence-of-error severity at design stage. Define overconfidence risk tolerance accordingly.

Configure output filters at deployment to detect and rewrite responses with overconfidence markers (absolute certainty language).

Design system prompts to require the model to express epistemic uncertainty and qualify confident-sounding claims.

Disclose to users at deployment that outputs may carry unwarranted confidence. Include specific caveat language in the UI.

Identify all groups at risk of adverse impact at use case intake. Register them in the affected group register.

Design separate model segments where adverse impact risk differs materially across population groups.

Set decision thresholds to meet acceptable adverse impact ratios across protected groups. Validate before deployment.

Apply post-processing adjustments (reject-option classification, score recalibration) to meet adverse impact targets.

Ensure HITL review pathways are live and tested for high-impact adverse decisions at go-live.

Maintain HITL review for all AI decisions with material adverse impact potential. Log all interventions and outcomes.

Define and execute a domain-specific hallucination test suite before deployment. Treat hallucination rate above threshold as a blocking defect.

Construct synthetic evaluation datasets for knowledge-boundary scenarios. Use to validate model refusal behaviour.

Calibrate the groundedness threshold against the hallucination test suite pre-release; sign off the threshold in the validation pack.

Use production feedback (user corrections, fact-check failures) to drive periodic RLHF cycles. Update model when error rates trend upward.

Require user-facing interfaces to disclose Gen AI limitations and hallucination risk before go-live.

Score every RAG answer for groundedness before release; block, fall back, or escalate responses below the faithfulness threshold.

Sample multiple generations for high-stakes queries and abstain, fall back, or escalate when semantic entropy exceeds the calibrated threshold.

Execute red team tests targeting adverse impact boundary cases and edge population scenarios.

Collect adverse outcome feedback from affected users. Use reports to trigger model updates when adverse impact exceeds threshold.