What does fairness mean in AI?
Fairness in means fairness out.
How It Works:
Fairness techniques measure disparate impacts across groups and adjust data sampling or model training to equalize outcomes.
Key Benefits:
- Equal treatment: Ensures unbiased predictions.
- Public trust: Demonstrates responsible AI.
- Legal safety: Complies with anti-discrimination laws.
Real-World Use Cases:
- Hiring algorithms: Balanced selection across demographics.
- Credit scoring: Equal default risk across genders.
FAQs
Can fairness reduce accuracy?
How measure fairness?
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