Guidelines for Responsible AI
What this domain tests
The responsible-AI dimensions - fairness, explainability, privacy, safety, transparency, and human oversight - and which concrete control implements each one.
Core rules
- Bias enters through data, labeling, and feature choice. Measure it with SageMaker Clarify (pre-training and post-training bias metrics) and explain predictions with SHAP.
- Transparency: model cards, Bedrock model cards, and AI Service Cards document intended use, limitations, and evaluation results.
- Privacy: detect and redact PII using Comprehend or Macie; minimize sensitive data in prompts.
- Safety: Bedrock Guardrails filter harmful content, block denied topics, mask sensitive information, apply word filters, and run contextual grounding checks that score whether a response is supported by the retrieved source.
- Human-in-the-loop review for high-stakes decisions; watermark AI-generated images (Titan Image Generator) to signal synthetic origin.
- Oversight means logging, monitoring, and a documented escalation path.
Worked example
A bank wants an LLM to summarize loan files but must not leak account numbers and must not answer investment-advice questions. Reasoning: account numbers are PII, so add a sensitive-information filter or pre-redact; advice requests map to a denied topic; unsupported summaries are caught by a contextual grounding check with a threshold; a human reviews summaries before use. Record all of it in a model card.
Traps
- Treating bias as only a data problem when prompts and evaluation also carry it.
- Assuming guardrails replace human review.
- Confusing transparency (documentation) with explainability (why this output).
- Overlooking toxicity and accessibility in generated content.