Principles of Ethical AI: Building Responsible and Trustworthy Systems
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Table of Contents
- 1. Course Modules:
- 2. Module 1: Introduction to AI Ethics
- 3. Module 2: Fairness and Non-Discrimination
- 4. Module 3: Transparency and Explainability
- 5. Module 4: Privacy, Consent, and Data Ethics
- 6. Module 5: Accountability and Human Oversight
- 7. Module 6: Safety, Misuse, and Long-Term Impact
- 8. Module 7: Capstone Project – AI Ethics Review
- 9. Tools & Technologies Used:
- 10. Target Audience:
- 11. Global Learning Benefits:
Course Modules:
Module 1: Introduction to AI Ethics
What is ethical AI?
Historical context and the rise of responsible AI
Global AI ethics guidelines and governance (EU, UNESCO, OECD, etc.)
Module 2: Fairness and Non-Discrimination
Understanding fairness in machine learning
Preventing bias and exclusion
Group vs. individual fairness and protected attributes
Module 3: Transparency and Explainability
The “black box” problem in AI
Explainable AI (XAI) methods: SHAP, LIME
Communicating AI decisions to non-technical users
Module 4: Privacy, Consent, and Data Ethics
Data protection principles (GDPR, HIPAA)
Informed consent in data collection
Ethical data sourcing and annotation practices
Module 5: Accountability and Human Oversight
Assigning responsibility for AI decisions
Designing systems with human-in-the-loop controls
Regulatory compliance and organizational ethics policies
Module 6: Safety, Misuse, and Long-Term Impact
Avoiding harmful or dual-use AI applications
Ethical risk assessments and mitigation strategies
Aligning AI with human values and public interest
Module 7: Capstone Project – AI Ethics Review
Select an AI system or use case (e.g., facial recognition, predictive policing, health assistant)
Analyze it using the five ethical AI principles
Submit a review report with risks, recommendations, and safeguards
Tools & Technologies Used:
SHAP / LIME (for model explainability)
Fairlearn, AIF360 (for bias assessment)
Ethics impact templates and review boards
Optional: Responsible AI frameworks (e.g., Microsoft, Google, AI4People)
Target Audience:
AI developers and machine learning engineers
Product managers and ethics officers
Policymakers and compliance professionals
Students and researchers studying ethical tech
Global Learning Benefits:
Build AI systems that respect human rights and dignity
Prevent unintended harm and bias
Align development practices with international ethics standards
Strengthen user trust, transparency, and social responsibility in AI
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