Module 1: Overview of AI Ethics & Societal Impact
1.1 Introduction to Ethical Considerations in AI
1.2 Understanding The Societal Impact of AI Technologies
1.3 Strategies for Conducting Social and Ethical Impact Assessments
Module 2: Bias and Fairness in AI
2.1 Exploration of Biases in Data and Algorithms
2.2 Strategies for Mitigating Bias and Ensuring Fairness in AI Systems
Module 3: Transparency and Explainable AI
3.1 Importance of Transparent AI Systems
3.2 Techniques for Explaining AI Models to Diverse Stakeholders
3.3 Guided Projects on Designing and Analysis of AI Systems with Ethical Considerations
Module 4: Privacy and Security Issues in AI
Study frameworks for holding organizations accountable for the ethical use of AI.
Why it matters: Ensures ethical AI deployment and helps mitigate the consequences of potential misuse or harm.
Module 5: Accountability and Responsibility
5.1 Concepts of Accountability in AI Development and Deployment
5.2 Responsibilities of AI Practitioners and Organizations
Module 6: Legal and Regulatory Issues
6.1 Overview of Relevant Laws and Regulations Pertaining to AI
6.2 Understanding the Global Regulatory Issues for AI Technologies
6.3 Case Studies: GDPR Compliance
6.4 Legal Compliance of AI Tools
Module 7: Ethical Decision-Making Frameworks
7.1 Introduction to Frameworks for Making Ethical Decisions in AI
7.2 Case Studies and Applications of Ethical Decision-Making
7.3 Use of Simulation Platforms in Ethical Decision-Making
Module 8: AI Governance & Best Practices
8.1 Principles and Functions of International AI Governance
8.2 Best Practices for Integrating AI Ethics into Organizational Policies
8.3 Case Studies on AI Governance
Module 9: Global AI Ethics Standards
9.1 Explore Standards: IEEE’s Ethically Aligned Design
9.2 Comparative Case Studies on Standard Implementations
9.3 Tools for Evaluating AI Systems Against Global Standards
Optional Module: AI Agents for Ethics and Its Implications
- Understanding AI Agents
- Case Studies
- Hands-On Practice with AI Agents
