Considerations for addressing the core dimensions of responsible AI for Amazon Bedrock applications

This article discusses considerations for addressing the core dimensions of responsible AI when building applications with Amazon Bedrock, AWS's service for generative AI.

Specifically, the article covers:

• Safety - Using Amazon Bedrock Guardrails to filter toxic content, block denied topics, filter words, and redact sensitive information
• Controllability - Guiding AI behavior through Guardrails and monitoring performance with model evaluation
• Fairness - Evaluating models with datasets like BOLD to assess bias and setting fairness goals
• Transparency - Providing AI Service Cards with model details and invisible watermarking for generated images
• Veracity and robustness - Using techniques like chain-of-thought prompting, retrieval augmented generation, fine-tuning, and contextual grounding
• Explainability - Utilizing retrieval augmented generation to provide source attribution for outputs
• Security and privacy - End-to-end encryption, access control, data localization, and sensitive information filtering
• Governance - Auditing with AWS Audit Manager, monitoring with CloudWatch/CloudTrail, and compliance standards
• Conclusion - Implement measures like model evaluation, Guardrails, prompt engineering, knowledge bases, and logging to build responsible AI applications