Explainable AI
Implement techniques to ensure AI decisions are traceable and explainable
Fairness & Bias Prevention
Develop systems that are fair and unbiased across all demographics
Robust Security
Build resilient systems that can withstand attacks and abnormalities
Getting Started
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Assess Risk Level
Determine your AI system's risk category and applicable regulations
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Plan for Transparency
Design systems with explainability in mind from the start
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Implement Safeguards
Build in security measures and bias prevention mechanisms
Documentation
Maintain comprehensive documentation of model architecture and decisions
Testing
Implement rigorous testing protocols for accuracy, bias, and security
Monitoring
Continuous monitoring of model performance and potential issues