- This course focuses on integrating ethics and governance into AI projects. Students will explore frameworks for ethical decision-making, understand regulatory compliance, and learn how to design AI systems responsibly. Topics include mitigating risks, ensuring fairness, transparency, compliance, and accountability, and aligning AI development with societal values and standards.
- Level: Intermediate
- Prerequisites: Basic understanding of AI concepts.
- Assessments: Weekly Case Studies, Final Presentation.
- Job Roles Applicability: AI Ethics Specialist, AI Governance Analyst, AI Policy Consultant.
Week 1 - Foundations of Ethical AI
Learning OutcomeUnderstand the importance of ethics in AI and the key principles guiding ethical AI design.
1.1 The significance of ethics in AI development.1.2 Key principles: fairness, accountability, transparency, and privacy (FATP).
1.3 Regulatory and legal landscape for AI ethics.
Practical Component
Review real-world case studies where ethics in AI were compromised and discuss mitigation strategies, with examples of algorithms that don't meet ethical standards.
Week 2 - Detecting Bias through Datasets used
Learning OutcomeLearn to identify, measure, and mitigate bias in AI systems.
2.1 Sources of bias in AI models and datasets.2.2 Tools and techniques for bias detection.
2.3 Best practices for designing fair AI systems.
Practical Component
Analyze a dataset for bias and propose modifications to improve fairness.
Week 3 - Evaluating an Artificial Intelligence for Bias
Learning OutcomeEDevelop frameworks for AI governance and understand accountability structures.
3.1 Detecting if an AI is bias, what to check for.3.2 Understanding the cause of bias, and suggesting fixes.
3.3 Monitoring and auditing AI systems for ethical compliance.
Practical Componenet
Process and strategy of evaluating an AI for Bias with practical examples, and evaluations.
Week 4 - Governance Frameworks and Accountability in AI
Learning OutcomeDevelop frameworks for AI governance and understand accountability structures.
4.1 Elements of effective AI governance.4.2 Creating accountability in AI systems.
4.3 Monitoring and auditing AI systems for ethical compliance.
Practical Componenet
Draft a governance framework for a live AI application considering key elements of process.
Week 5 - Transparency and Explainability in AI
Learning OutcomeLearn techniques to make AI systems more interpretable and transparent.
5.1 Why explainability matters in AI.5.2 Methods for improving transparency
5.3 Communicating AI decisions to stakeholders.
Practical Component
Develop an explainability report for various models shown, and compare and contrast.
Week 6 - Final Project and Assessment
Learning OutcomeApply ethical and governance principles to an AI project and present findings.
6.1 Identify potential ethical risks in an AI application of your choice.6.2 Develop a governance strategy to address risks.
6.3 Present and defend your strategy to peers and instructors.
Practical Component
Complete an ethical audit of an AI system and propose an actionable governance plan.