📘 Chapter 3: Ethics + Bias
⚖️ Why AI Isn’t Always Fair
Artificial Intelligence might feel neutral because it’s made of math and code — but it isn’t. AI systems are only as good as the data and decisions behind them.
If that data is biased or incomplete, the AI can make unfair, even harmful, choices. And since AI is used in things like hiring, policing, and loans, those choices matter.
🚨 Real-World Examples of AI Gone Wrong
| Case | What Happened | Why It Mattered |
|---|---|---|
| Facial recognition | Misidentified people of color more often | Led to wrongful arrests |
| Hiring algorithms | Gave lower scores to resumes with female names | Reinforced gender discrimination |
| Loan approvals | Denied more applications in certain zip codes | Echoed racial bias built into old systems |
AI doesn’t try to be unfair — but it inherits bias from the world it learns from.
🧠 Where Does Bias Come From?
- Biased training data
- Assumptions made by programmers
- Lack of diversity in development teams
- Ignoring edge cases or smaller populations
- Algorithms optimized only for speed or profit
Even well-meaning AI can create big problems if ethics are ignored.
🧪 Try It Yourself
Activity: AI in the Classroom — Pros & Cons
Make a two-column chart:
| Pros of AI in School | Cons of AI in School |
|---|---|
| Helps organize your schedule | Might make mistakes grading assignments |
| Instant answers for research | Could discourage original thinking |
| Personalized learning suggestions | Might track too much data about students |
Then ask yourself:
Would you want an AI teacher? Why or why not?
🛠️ Project Idea
Create a Visual Poster: “The Ethics of AI: What We Must Watch For”
Include:
- 3 real-world examples of bias or harm
- 3 questions AI developers should always ask
- 1 quote or message from your point of view
This could be a physical poster, a digital graphic, or a slideshow. You can also submit it to your school or class bulletin board.
🧾 Quick Recap
- AI can inherit bias — it doesn’t mean to, but it can do harm
- We must ask who builds AI, who it impacts, and how it’s used
- Ethical AI requires fairness, transparency, and accountability
- YOU can be part of that conversation
🔗 Next Up: Chapter 4 – Real-World Uses (The Good Stuff)