Which THREE practices are recommended for responsible AI when deploying foundation models? (Choose three.)
Human review for high-stakes decisions satisfies the accountability constraint by keeping a person responsible for consequential outcomes, rather than delegating judgement to a probabilistic model. Foundation models can produce confident but wrong outputs, so oversight catches harmful errors before they affect people, aligning with responsible AI principles.
Why this answer
Option B is correct because responsible AI deployment requires human-in-the-loop oversight for consequential decisions (e.g., hiring, lending, medical triage), so that a qualified person can validate or override model output before it affects someone's rights or safety. Option C is correct because guardrails — input/output filters, content moderation classifiers, and policy enforcement layers — are a standard control to prevent foundation models from generating harmful, unsafe, or policy-violating content. Option D is correct because foundation models can degrade or shift behavior over time due to data drift, concept drift, or upstream model updates, so continuous monitoring of outputs (with metrics, alerts, and retraining triggers) is essential to maintain reliability and fairness.
Option A is not recommended: collecting user feedback is a valuable signal for identifying bias and improving models, so avoiding it would reduce accountability rather than enhance responsibility. Option E is not recommended: opaque 'black box' secrecy undermines transparency, explainability, and auditability, which are core responsible AI principles; model internals and documentation should be appropriately disclosed to stakeholders.
Exam trap
A common misconception is that avoiding user feedback reduces bias, when in fact it starves the system of data needed to detect and correct bias, making it a harmful anti-pattern. AWS recommends continuous feedback and monitoring for responsible AI.