AI Ethics Checklist
Evaluate your AI project against ethical best practices
Overall Score
0%
Poor
Fairness & Bias
Transparency
Privacy & Security
Safety & Reliability
Accountability
Environmental
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AI Ethics Checklist: Complete Guide to Responsible AI Development
As AI systems become more powerful and prevalent, ensuring they're developed and deployed ethically is more important than ever. This interactive AI Ethics Checklist helps you evaluate your AI project against established ethical best practices, covering fairness, transparency, privacy, safety, accountability, and environmental considerations.
Whether you're building AI applications, deploying AI in your organization, or evaluating AI vendors, this comprehensive checklist will help you identify potential ethical issues before they become problems. Use it during design, before deployment, and as part of ongoing governance.
Go through each item, check what you've addressed, and use the export feature to share your assessment with stakeholders or include it in project documentation.
Why AI Ethics Matters
AI ethics isn't just about avoiding bad PR — it has real business, legal, and societal implications:
⚖️ Legal Compliance
Regulations like the EU AI Act, GDPR, and sector-specific rules (healthcare, finance) require ethical AI practices. Non-compliance can result in significant fines.
🤝 Trust & Reputation
Users and customers are increasingly aware of AI risks. Demonstrating ethical AI practices builds trust and differentiates you from competitors.
📉 Risk Mitigation
Ethical issues that go unaddressed can lead to biased decisions, privacy breaches, or safety incidents — all of which have serious business consequences.
🌍 Social Responsibility
AI has the power to impact millions of people. Ethical development ensures these impacts are positive and don't perpetuate or amplify existing inequities.
Understanding Severity Levels
Each checklist item is marked with a severity level to help you prioritize:
Critical items that should be addressed before deployment. These represent significant legal, safety, or fairness risks. Incomplete high-priority items should block launch.
Important items that significantly improve trust, reliability, and governance. Should be addressed soon after deployment or during your next iteration cycle.
Good practices that demonstrate ethical leadership and forward thinking. Address these when you have bandwidth — they improve your overall posture but aren't urgent.
Checklist Categories Explained
⚖️ Fairness & Bias
Ensuring your AI doesn't discriminate against protected groups. This includes testing for demographic bias, using representative training data, and having plans to mitigate identified biases.
Key question: Would this system treat all users fairly regardless of race, gender, age, or other protected characteristics?
🔍 Transparency
Being clear about when AI is being used and how it makes decisions. Users should know they're interacting with AI, understand how decisions are made (especially for high-stakes applications), and have channels to ask questions.
Key question: Would a user understand what the AI is doing and why?
🔒 Privacy & Security
Protecting user data and complying with privacy regulations. This includes data minimization, proper security controls, regulatory compliance (GDPR, CCPA), and giving users control over their data.
Key question: Is user data handled with the care and protection it deserves?
🛡️ Safety & Reliability
Ensuring the AI doesn't cause harm and works reliably. This includes content filters, edge case testing, human oversight for critical decisions, and monitoring for model degradation.
Key question: What's the worst thing this AI could do, and have we prevented it?
📋 Accountability
Having clear ownership and recourse mechanisms. Someone should be responsible for the AI system, users should have a way to report problems, and there should be audit trails for important decisions.
Key question: If something goes wrong, who is responsible and how will we make it right?
🌱 Environmental
Considering the environmental impact of AI training and inference. Large AI models consume significant energy — being aware of and optimizing this is increasingly important.
Key question: Have we considered and minimized the environmental footprint of this AI?
Best Practices for Using This Checklist
- Review early and often: Don't wait until deployment — review ethics during design and development phases.
- Involve diverse stakeholders: Get perspectives from legal, compliance, product, engineering, and affected user groups.
- Document everything: Use the export feature to create records of your ethical assessments over time.
- Revisit regularly: AI systems can drift — schedule periodic re-evaluations (quarterly or after major updates).
- Be honest: The checklist only helps if you're honest about what's actually in place vs. aspirational.
Frequently Asked Questions
Is this checklist legally binding?
No, this checklist is a practical guidance tool, not a legal document. However, many items align with regulatory requirements (EU AI Act, GDPR, etc.). Use this as a starting point, but consult with legal counsel for compliance-specific advice.
What score should I aim for?
At minimum, address all "High Priority" items before deployment. A score of 70%+ (Good) is a reasonable goal for most projects, while 90%+ (Excellent) represents industry-leading ethical practices. The right target depends on your application's risk level — higher-stakes applications (healthcare, finance, hiring) should aim higher.
How often should I review this checklist?
We recommend reviewing: (1) during initial design, (2) before deployment, (3) after any major model or data changes, and (4) on a regular schedule (quarterly for high-risk applications, annually for lower-risk). Document each review for audit purposes.
What if I can't complete certain items?
Not all items apply to all projects. If an item genuinely doesn't apply (e.g., environmental impact for a small inference-only application), that's fine. But be careful not to dismiss items too easily — they may apply more than you think. For items that apply but aren't complete, create a roadmap to address them.
How does this align with AI regulations like the EU AI Act?
This checklist covers many areas addressed by regulations, including transparency requirements, bias testing, human oversight, and documentation. However, specific regulatory requirements vary by jurisdiction and application type. Use this checklist as a foundation, then layer on jurisdiction-specific requirements.
Should I share this checklist with external auditors?
Yes, showing completed ethics checklists can demonstrate due diligence to auditors, regulators, customers, and partners. Use the export feature to create dated records. Be aware that sharing also creates accountability — make sure your assessments are accurate.
What's the difference between ethics review and legal review?
Legal review focuses on regulatory compliance and liability. Ethics review goes beyond what's legally required to consider what's right, fair, and beneficial to stakeholders. Many ethical issues become legal issues over time — being ahead of the curve is good practice.
Related AI Ethics Frameworks
This checklist draws from established AI ethics frameworks. Learn more:
- OECD AI Principles — International guidelines adopted by 40+ countries
- EU AI Act — Europe's comprehensive AI regulation (in effect from 2024)
- NIST AI RMF — US framework for managing AI risks
- IEEE Ethically Aligned Design — Technical standards for ethical AI
- Google/Microsoft/IBM AI Principles — Industry practices from major AI companies
Related AI Ethics Tools
Continue your AI ethics journey with these related resources:
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Summary
This AI Ethics Checklist covers 22 essential items across 6 categories: Fairness & Bias, Transparency, Privacy & Security, Safety & Reliability, Accountability, and Environmental. Prioritize high-severity items first, aim for 70%+ completion, and review regularly. Use the export feature to document your assessments and share with stakeholders. Remember: ethical AI is not just about avoiding harm — it's about building systems that are beneficial, trustworthy, and fair.