Ethical Considerations in AI Agents
Build responsible AI agents that respect human values, promote fairness, and operate transparently
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0 / 5 completedFairness & Bias Mitigation
AI bias occurs when systems produce systematically unfair outcomes for certain groups. These biases can emerge from training data, algorithm design, or deployment contexts. Detecting and mitigating bias requires proactive testing, diverse datasets, and ongoing monitoring to ensure equitable treatment across all user groups.
Interactive: Types of AI Bias
Explore common bias types and their mitigation strategies:
Interactive: Bias Detection Simulator
Test different AI systems to identify disparate outcomes across demographic groups:
Male Applicants
Female Applicants
Significant disparity detected (26% difference). This system requires immediate bias mitigation.
Use multiple fairness metrics to evaluate your AI: demographic parity (equal outcomes across groups), equalized odds (equal true positive and false positive rates), and individual fairness (similar individuals get similar outcomes). No single metric captures all fairness dimensions.