Lesson 4.1: Ethical AI

1. Lesson Snapshot

Theme: Understand the principles, risks, and frameworks of ethical AI
Duration: 60 minutes
Format: Scenario explorations, interactive debates, “Bias Busters” workshop, and policy sprint

2. Learning Objectives

By the end of this session, you will be able to:

Articulate the core ethical principles governing AI (e.g., fairness, transparency, accountability).
Identify common sources of bias and unintended harm in AI systems.
Apply evaluation techniques to detect and mitigate ethical risks.
Design a high-level AI ethics policy or guideline for your organization.

3. Key Concepts & Definitions

Term	Definition
Fairness	Ensuring AI decisions do not produce unjust or prejudiced outcomes for any group.
Transparency / Explainability	The ability to understand and explain how an AI model arrives at its decisions.
Accountability	Assigning responsibility for AI system outcomes, including mechanisms for redress.
Privacy & Data Protection	Safeguarding personal data and complying with regulations (e.g., GDPR, CCPA).
Robustness & Safety	Building systems that are reliable under varied conditions and resistant to adversarial attacks.

4. Lesson Flow

Time	Activity	Format
0–10′	Ethics Warm-Up	Quick poll + pair discussion
10–25′	Scenario Exploration	Small-group case studies
25–40′	Bias Busters Workshop	Hands-on bias detection & mitigation
40–50′	Ethics Policy Sprint	Drafting high-level guidelines in teams
50–60′	Debate & Debrief	Structured debate + summary takeaways

4.1 Ethics Warm-Up

Tool: Mentimeter or show of hands
Prompt: “Which ethical AI concern worries you most?” (options: bias, opacity, misuse, data privacy, other)
Pair-Share: Briefly discuss a personal example where technology raised an ethical question.

4.2 Scenario Exploration

Cases Provided:
1. Hiring Algorithm Bias: A recruitment tool systematically undervalues applicants from certain demographics.
2. Facial Recognition Misidentification: A retail store’s surveillance misidentifies customers, leading to false accusations.
Task: In groups, identify:
- The ethical principles at stake
- Potential harms and affected stakeholders
- High-level mitigation ideas

4.3 Bias Busters Workshop

Data Exercise: A synthetic dataset of loan applicants with demographic, income, and credit-score fields.
Challenge:
1. Compute acceptance rates by group (e.g., gender, ethnicity).
2. Spot disparities indicating bias.
3. Propose and implement at least one mitigation strategy (e.g., re-sampling, fairness constraints).
Deliverable: A short list of findings and the chosen mitigation approach.

4.4 Ethics Policy Sprint

Teams of 3–4:
- Draft a one-page “AI Ethics Charter” covering:
  1. Principles (fairness, transparency, privacy, accountability)
  2. Roles & governance (who reviews, who signs off)
  3. Process checkpoints (design review, bias audit, post-deployment monitoring)
Timebox: 10 minutes to draft, 5 minutes to refine.

4.5 Debate & Debrief

Format: Two teams debate the motion:

“All AI systems in our organization must be open-source to ensure accountability.”
Structure:
- 2 minutes opening by Pro and Con
- 3 minutes rebuttal round
- 2 minutes audience Q&A
Takeaways: Instructor summarizes key arguments and re-emphasizes best practices.

5. Reflection & Takeaways

Quickwrite (2 mins): “What’s the single most important ethical safeguard I will adopt in my next AI project?”
Pair-Share (3 mins): Swap insights and agree on one immediate action.

6. Homework

Ethics Audit Template:
1. Select an existing or planned AI project.
2. Complete an ethics audit template covering: stakeholders, potential harms, bias checks, data privacy considerations, and governance steps.
3. Submit the filled template before Lesson 4.2.