Responsible AI deployment requires addressing risks across data privacy, fairness, transparency, safety, security, and compliance — before and after launch. This checklist covers 50+ items organized by category, with priority levels to help teams focus on what matters most.
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How to Use the AI Safety Checklist
Deploying AI responsibly requires systematic review across multiple domains. This checklist structures the review process to ensure teams address critical risks before launch and establish ongoing monitoring afterward.
Work Through Categories Systematically
Each category represents a distinct risk domain. Data & Privacy ensures your training and inference data is handled lawfully and safely. Bias & Fairness checks that the model doesn't systematically disadvantage protected groups. Safety & Alignment addresses output filtering and adversarial robustness. Address all critical items before launching any production deployment.
Prioritize by Risk Level
Use the priority badges to triage your work. Critical items (red) are showstoppers — deploying without addressing them exposes your organization to legal liability, security vulnerabilities, or serious user harm. Important items (yellow) should be addressed before public launch. Recommended items (green) represent best practices for mature AI systems.
Track Progress and Export for Reviews
Your progress is automatically saved to your browser. Use the Export button to generate a text summary suitable for team reviews, audit documentation, or stakeholder reports. Paste the exported checklist into documents for sign-off workflows.
Key Frameworks This Checklist Draws From
This checklist incorporates requirements and best practices from: NIST AI RMF (AI Risk Management Framework), EU AI Act compliance requirements for high-risk AI systems, Google PAIR guidelines, OWASP LLM Top 10 security risks, ISO/IEC 42001 AI management systems standard, and Anthropic's responsible AI deployment recommendations.
When to Run This Checklist
Run the full checklist before any production AI deployment. Revisit the Monitoring & Maintenance section quarterly. Re-run the Bias & Fairness section whenever you retrain or fine-tune the model. Run the Security section whenever you change infrastructure or access controls. The Data & Privacy section should be reviewed annually or whenever data handling practices change.
FAQ
What is responsible AI deployment?
Responsible AI deployment means deploying AI systems in a way that is fair, transparent, safe, secure, and compliant with regulations. It involves assessing and mitigating risks before launch (bias audits, security review, compliance checks) and after (monitoring for drift, performance degradation, and misuse). This checklist covers the key areas practitioners should address.
Who should use this checklist?
This checklist is designed for AI engineers, data scientists, product managers, and compliance teams deploying AI systems in production. It covers both technical requirements (security, monitoring) and organizational requirements (governance, legal review). The critical and important items should be addressed for any production deployment.
What do the priority levels mean?
Critical items are showstoppers — deploying without addressing them creates serious legal, safety, or security risks. Important items should be addressed before launch but are not absolute blockers. Recommended items represent best practices that improve quality and reduce long-term risk, but are optional for MVP deployments.
Is my checklist progress saved?
Yes. Your checked items are saved to your browser's localStorage and persist between visits. The save is automatic — no button to press. Clearing browser data or using a different device will reset your progress.
Can I export my checklist results?
Yes. Use the 'Export' button to copy a text summary of your checked and unchecked items to the clipboard. You can paste this into a document or email for team reviews.
Is this checklist free?
Yes, completely free. Use, export, and share the checklist without signing up.
Is my data private?
Yes. All checklist state is saved only in your browser's localStorage. No data is sent to any server.