🎓 Lesson 17: Ethics and Bias in AI
Lesson Objective:
To help learners understand the ethical risks, responsibilities, and challenges of AI systems, especially related to bias, fairness, transparency, and accountability.
Why Ethics in AI Matters
AI has the power to make decisions that affect:
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Who gets a loan
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Who gets a job interview
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What news you see
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How police are deployed
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Which patients get priority care
If AI is biased, unfair, or opaque, it can amplify inequality and cause real harm — even unintentionally.
That’s why ethics is not optional. It’s essential.
What Is Bias in AI?
AI bias happens when an AI system treats some people or groups unfairly, often because it was trained on biased data or designed without enough diversity in mind.
Example: If a hiring AI is trained mostly on resumes from men, it may learn to favor male candidates — even if unintentionally.
Common Sources of AI Bias
| Source | Description |
|---|---|
| Training Data Bias | Biased or unbalanced data used for learning |
| Labeling Bias | Mistakes or assumptions in how data is labeled |
| Design Bias | The team designing the system lacks diversity |
| Feedback Loops | AI decisions reinforce past patterns, even if harmful |
| Cultural Bias | Ignoring cultural contexts, languages, or values |
Bias is often invisible until it’s too late — unless you actively test for it.
Real-World Examples of Ethical Issues
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Facial Recognition
Studies show some systems have error rates over 30% for darker-skinned women, but under 1% for white men. -
Healthcare AI
An algorithm trained mostly on data from urban hospitals under-served rural populations. -
Predictive Policing
Systems trained on historical crime data often target communities that were over-policed in the past. -
Credit Scoring
AI that excludes zip codes or education history may reflect and amplify systemic inequality.
Core Ethical Principles in AI
| Principle | What It Means |
|---|---|
| Fairness | Treat all users equally, without bias or discrimination |
| Transparency | Make decisions understandable to humans |
| Accountability | Ensure someone is responsible for AI outcomes |
| Privacy | Protect user data and avoid surveillance abuse |
| Safety | Ensure AI does not cause harm — intentionally or unintentionally |
| Human Control | AI should assist, not replace, ethical human judgment |
What Ethical AI Looks Like
A responsible AI system will:
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Be tested for bias across different user groups
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Be explainable: “Why did it make this decision?”
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Have human oversight
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Protect personal data
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Be aligned with laws and regulations
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Be continuously monitored and improved
Ethical AI is trustworthy AI.
What Managers and Leaders Should Ask
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Was this AI system tested for bias?
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What data was used to train it?
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Can users understand or appeal its decisions?
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Who is accountable if it fails?
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How do we handle misuse or unintended consequences?
Ethical AI = Competitive Advantage
Companies that prioritize ethics in AI will build:
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Greater trust with customers
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Stronger brand loyalty
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Better compliance with global regulations
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Less risk of lawsuits, bad PR, and societal harm
Reflection Prompt (for Learners)
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Has there been a time when you felt a system treated you unfairly? Could AI bias have played a role?
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What ethical guardrails would you want in place for AI systems at your workplace?
✅ Quick Quiz (not scored)
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What is AI bias?
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Name one cause of AI bias.
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What does it mean for AI to be transparent?
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True or False: AI systems can be biased even if programmers didn’t intend them to be.
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Name two ethical principles in AI development.
Key Takeaway
AI is powerful — but power without ethics is dangerous.
Building AI systems that are fair, transparent, and human-centered is not just the right thing to do — it’s the necessary thing to do.