Core dimensions of Responsible AI
- Fairness: promote inclusion and prevent discrimination
- Explainability
- Privacy and security: individuals control when and if their data is used
- Transparency
- Veracity and robustness: reliable even in unexpected situations
- Governance: define, implement and enforce responsible AI practices
- Safety: algorithms are safe and beneficial for individuals and society
- Controllability: ability to align to human values and intent
Responsible AI - AWS Services
- Amazon Bedrock: human or automatic model evaluation
- Guardrails for Amazon Bedrock
- Filter content, redact PII, enhanced safety and privacy…
- Block undesirable topics
- Filter harmful content
- SageMaker Clarify
- FM evaluation on accuracy, robustness, toxicity
- Bias detection (ex: data skewed towards middle-aged people)
- SageMaker Data Wrangler: fix bias by balancing dataset
- Ex: Augment the data (generate new instances of data for underrepresented groups)
- SageMaker Model Monitor: quality analysis in production
- Amazon Augmented AI (A2I): human review of ML predictions