Day 29 of 30Ethics & Capstone
⚖️AI Ethics, Bias & Careers
🎯 Today's objective
Build AI responsibly and see where this can take you.
📖 Key concepts
- Bias in = bias out (data fairness)
- Privacy & consent for data
- Transparency & accountability
- Human-in-the-loop for high-stakes decisions
💻 Try this code
# Reflection, not code.
# Ask of any AI: Who is in the data? Who is left out?
# What happens when it's wrong? Who is accountable?👥 Learn it your way
🧒 Young Explorers
With great AI comes great responsibility — fairness and kindness matter as much as accuracy.
Your task
Describe an AI that could be unfair, and one rule to make it fairer.
💼 Professionals
Know frameworks: fairness metrics, model cards, data governance, EU-AI-Act-style risk tiers.
Your task
Write a short "responsible use" checklist for an AI feature you might ship.
🤖 AI connection
The best engineers ask not just "can we build it?" but "should we, and for whom?" — this is what makes AI trustworthy.
✅ Quick check
Where does AI bias usually come from?
Biased or unrepresentative training data.
What is "human-in-the-loop"?
Keeping a person reviewing/approving important AI decisions.