1.2.1. Fairness

Definition: AI systems should treat all people fairly and avoid affecting similarly situated groups in different ways.

Key Concerns:

• Bias in training data leading to biased predictions
• Discrimination based on protected characteristics (gender, ethnicity, age, disability)
• Disparate impact on different demographic groups

Example Scenario: A hiring AI trained on historical data learns to favor male candidates because the historical workforce was predominantly male.

Test Pattern: Questions about loan approvals, hiring decisions, or any scenario mentioning gender, ethnicity, or demographic bias → Fairness