AI Ethics & Bias
Building responsible AI systems: understanding bias, fairness, transparency, and societal impact.
Why Ethics Matters
AI systems increasingly make decisions that affect people's lives: loan approvals, hiring, criminal justice, healthcare. As engineers, we have a responsibility to build systems that are fair, transparent, and beneficial to society.
Types of Bias
Data Bias
Training data doesn't represent the population. Example: facial recognition trained mostly on white faces performs poorly on darker skin tones.
Algorithmic Bias
Model design or optimization inadvertently discriminates. Example: optimizing for accuracy can ignore minority groups.
Historical Bias
Data reflects past discrimination. Example: hiring models trained on historical data may perpetuate gender biases.
Fairness Definitions
Multiple mathematical definitions of fairness exist, often in tension:
Demographic Parity
Equal acceptance rates across groups
Equalized Odds
Equal true/false positive rates across groups
Individual Fairness
Similar individuals receive similar outcomes
Calibration
Predicted probabilities match actual outcomes
⚠️ The Impossibility Theorem
It's mathematically proven that you cannot satisfy all fairness criteria simultaneously (except in trivial cases). For example, you cannot have both demographic parity and equalized odds when base rates differ between groups. This means fairness requires making trade-offs based on values and context, not just optimizing metrics.
Responsible AI Practices
- Diverse teams: Include diverse perspectives in development
- Bias testing: Test models across demographic groups
- Transparent documentation: Model cards, datasheets
- Human oversight: Keep humans in the loop for high-stakes decisions
- Adversarial testing: Red-team models to find failure modes
- Continuous monitoring: Track model performance in production
Key Ethical Considerations
Privacy
Training data may contain sensitive information. Use differential privacy, federated learning.
Transparency & Explainability
Users should understand why AI made a decision. LIME, SHAP for interpretability.
Accountability
Clear responsibility when AI systems cause harm. Audit trails, version control.
Key Takeaways
- →Bias enters AI systems through data, algorithms, and deployment
- →Multiple fairness definitions exist and may conflict
- →Responsible AI requires testing, documentation, and monitoring
- →Ethics is not just a compliance issue—it's engineering excellence