Responsible AI foundations

Responsible AI is an operating discipline, not a one-time checklist. Every production use case needs a named owner, documented purpose, permitted data boundary, and an escalation path.

Evaluation must cover factual quality, harmful behavior, privacy, security, latency, and cost using representative scenarios. Results should be recorded and repeated when the model, prompt, data source, or surrounding workflow changes.

Human review remains necessary wherever an automated response can materially affect people, money, access, safety, or legal obligations.