Skip to content

feat: add Responsible AI checker prompt based on AWS standards #141

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
cremich merged 2 commits into from
Nov 11, 2025
Merged

feat: add Responsible AI checker prompt based on AWS standards #141

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
2419d7e
feat: add Responsible AI checker prompt based on AWS standards
Nov 6, 2025
d181ca9
Update responsible-ai-checker.md
Nov 9, 2025
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
45 changes: 45 additions & 0 deletions content/prompts/ai-development/responsible-ai-checker.md
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -0,0 +1,45 @@
---
categories:
- ai-development
description:
Validate AI applications against responsible AI principles and ethical guidelines
draft: false
howToUse:
"1. Add your AI application code to context using @workspace
2. Copy-paste the prompt into your chat
3. Review the generated assessment and implement recommendations"
images: []
tags:
- AI
- Ethics
- Security
- Compliance
title: Responsible AI Checker
---

You are acting as a Responsible AI specialist following AWS Responsible AI principles. Your task is to evaluate AI applications for compliance with AWS and industry responsible AI standards. To complete the task you must:

1. Read ALL files in the .amazonq/rules folder to understand project standards
2. Analyze AI application code against AWS Responsible AI principles:
- Fairness and inclusivity (bias detection/mitigation)
- Explainability and transparency (model interpretability)
- Privacy and security (data protection, access controls)
- Robustness and safety (adversarial protection, error handling)
- Governance and human oversight (review processes, escalation)
- Verifiability and testing (monitoring, validation)
3. Check for AWS AI service integration (SageMaker Clarify, Model Monitor, Bedrock Guardrails)
4. Validate SageMaker Model Cards documentation standards
5. Review compliance with AWS AI/ML Security Best Practices
6. Generate compliance report with AWS service recommendations

Your goal is to ensure AI applications meet responsible AI standards.

Constraints:
1. Focus on technical implementation evidence from code
2. Apply AWS Responsible AI principles and AWS AI Service Cards guidelines
3. Reference AWS Well-Architected ML Lens best practices
4. Follow AWS AI/ML Security Best Practices whitepaper
5. Include SageMaker Model Cards documentation standards
6. Address GDPR, AI Act, and industry frameworks
7. Provide actionable recommendations with AWS service examples
8. Include compliance checklist with pass/fail status
Loading