AI Ethics Basics
Why we need to be careful with AI. Understanding bias, fairness, and safety in simple terms.
Learning Goals
What you'll understand and learn
- Understand what 'AI Bias' is and why it happens
- Learn why 'Fairness' is hard to define for computers
- Discover the importance of Transparency (knowing *why* AI made a decision)
Beginner-Friendly Content
This lesson is designed for newcomers to AI. No prior experience required - we'll guide you through the fundamentals step by step.
AI Ethics Basics
The Mirror Effect
AI learns by looking at data from the real world. The problem? The real world isn't perfect.
If you teach an AI using history books written 100 years ago, it might learn outdated or unfair ideas about people.
AI Bias is when an AI makes unfair decisions because of the data it was trained on.
- Example: An AI hiring manager might reject female candidates if it was trained on resumes from a company that mostly hired men in the past.
The "Black Box" Problem
Imagine a judge sends someone to jail but refuses to say why. That wouldn't be fair, right?
Sometimes, modern AI is like that judge. It gives an answer, but even the creators don't know exactly how it reached that conclusion.
Transparency (or Explainability) is the goal of making AI explain its thinking. We want to know why it denied a loan or rejected a resume.
Safety First
AI is powerful. Like a fast car, it needs brakes and seatbelts.
AI Safety involves making sure the AI:
- Does what we want it to do (Alignment).
- Doesn't get tricked by bad actors (Robustness).
- Doesn't accidentally hurt anyone.
Conclusion
Building AI isn't just about code. It's about responsibility. We need to teach AI to be fair, just like we teach children to be kind.
Build Your AI Foundation
You're building essential AI knowledge. Continue with more beginner concepts to strengthen your foundation before advancing.