AI — Bias
Types of Bias
| Type | Description |
|---|---|
| Selection bias | Unrepresentative data |
| Confirmation bias | Favoring hypotheses |
| Automation bias | Over-relying on AI |
| Historical bias | Past patterns |
Detecting Bias
from fairlearn.metrics import demographic_parity_difference
difference = demographic_parity_difference(
y_true, y_pred, sensitive_features=protected_attribute
)
Mitigation Techniques
| Technique | Description |
|---|---|
| Data balancing | Equal representation |
| Fairness constraints | Add to training |
| Post-processing | Adjust outputs |
| Adversarial debiasing | Remove bias signals |
Mini Practice
- Identify bias types
- Detect bias in models
- Apply mitigation techniques
- Monitor fairness over time
Up Next
Continue with Safety — AI safety.
Related Topics
Frequently Asked Questions about Bias
What is Bias in AI?
Bias is a fundamental concept in AI. This lesson explains it step by step with clear examples, making it easy for beginners to understand.
How do I learn Bias?
Start by reading the explanation above, then try the code examples. Practice by modifying the examples and experimenting with different values. Hands-on practice is the best way to learn Bias.
Why is Bias important in AI?
Bias is essential for AI development. Understanding this concept will help you write better code and solve real-world problems more effectively.