AI Ethics and Bias

Build responsible AI.

Why Ethics Matter

AI affects real people's lives:

• Loan approvals
• Job applications
• Medical diagnoses
• Criminal justice

AI Bias

AI learns from data. If data has bias, AI will too!

Example of Bias

Hiring AI trained on past hires:

• If company mostly hired men
• AI learns: men = better candidates
• AI discriminates against women

This is wrong!

Sources of Bias

1. Historical data: Past discrimination
2. Incomplete data: Missing groups
3. Biased labels: Human prejudice

Real Case

Amazon's hiring AI showed bias against women. They stopped using it!

Fairness in AI

AI should treat everyone fairly:

• All races equally
• All genders equally
• All ages equally

Privacy Concerns

AI often uses personal data:

• Face recognition
• Location tracking
• Browsing history

Balance: Useful AI vs Privacy protection

Building Fair AI

• Use diverse data
• Test for bias
• Include diverse teams
• Be transparent

Remember

• AI can be biased
• Check for fairness
• Protect user privacy
• Build responsible AI