Master Study AI

Principles of Ethical AI: Building Responsible and Trustworthy Systems

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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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