Core Principles
- Fairness: AI systems should treat all groups equitably.
- Transparency: users should understand how AI makes decisions.
- Explainability: AI outputs should be interpretable and justifiable.
- Accountability: clear ownership and responsibility for AI outcomes.
- Privacy: protect personal data used in AI systems.
- Safety: AI systems should not cause harm.
Types of Bias
- Data bias: training data doesn't represent the target population.
- Selection bias: biased sampling during data collection.
- Measurement bias: systematic errors in how features are recorded.
- Algorithmic bias: model architecture amplifies existing biases.
- Confirmation bias: evaluators favor results that match expectations.
Mitigation Strategies
- Diverse and representative training data.
- Regular bias audits and fairness testing.
- Use Bedrock Guardrails for content safety.
- Human-in-the-loop for high-stakes decisions.
- Model cards to document capabilities, limitations, and intended use.
- Monitoring model outputs in production for drift and bias.
AWS Responsible AI Tools
- Amazon Bedrock Guardrails: content filters, denied topics, PII handling.
- SageMaker Clarify: bias detection and model explainability.
- SageMaker Model Cards: document model details and intended use.
- SageMaker Model Monitor: detect data drift in production.
- Amazon Augmented AI (A2I): human review workflows.
Practice Responsible AI Questions
Put your knowledge to the test with practice questions.