Ethical AI Leadership for Executives

Audience

This course is primarily intended for CEOs and Senior Executives working in the areas of Risk and Compliance. 

• Understand the importance of ethics in AI development and deployment. 

• Learn to identify and mitigate ethical risks associated with AI. 

• Gain insights into regulatory and compliance requirements. 

• Develop strategies for fostering an ethical AI culture within their organisations. 

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customisation Options

• To request a customised training for this course, please contact us to arrange.

Module 1: Introduction to AI Ethics 

• 1.1 Understanding AI Ethics: Definition, scope, and significance of AI ethics. 

• 1.2 Historical Context: Evolution of ethical considerations in technology. 

• 1.3 Ethical Frameworks: Key ethical theories and frameworks applicable to AI. 

Module 2: Identifying Ethical Risks in AI 

• 2.1 Bias and Fairness: Understanding and mitigating bias in AI algorithms. 

• 2.2 Transparency and Explainability: Ensuring AI systems are understandable and transparent.

• 2.3 Privacy and Data Protection: Safeguarding data privacy in AI applications. 

• 2.4 Accountability and Responsibility: Defining accountability in AI operations.

Module 3: Regulatory and Compliance Landscape 

• 3.1 Global Regulations: Overview of AI regulations around the world. 

• 3.2 Compliance Strategies: Implementing compliance frameworks for AI. 

• 3.3 Ethical Auditing: Conducting ethical audits of AI systems. 

Module 4: Developing an Ethical AI Strategy 

• 4.1 Ethical AI Roadmap: Creating a roadmap for ethical AI implementation. 

• 4.2 Policy Development: Developing policies and guidelines for ethical AI use. 

• 4.3 Stakeholder Engagement: Engaging stakeholders in ethical AI discussions. 

Module 5: Fostering an Ethical AI Culture 

• 5.1 Leadership and Governance: Establishing leadership roles for AI ethics. 

• 5.2 Training and Awareness: Building awareness and training programs for ethical AI. 

• 5.3 Ethical Decision-Making: Incorporating ethics into AI decision-making processes. 

Module 6: Case Studies and Real-World Applications 

• 6.1 Industry Case Studies: Analysing real-world examples of ethical and unethical AI use. 

• 6.2 Lessons Learned: Key takeaways from successful and failed AI ethics initiatives. 

• 6.3 Scenario Analysis: Interactive scenarios to apply ethical principles in AI. 

Module 7: Emerging Trends and Future Directions 

• 7.1 Technological Advances: Exploring the impact of emerging AI technologies on ethics. 

• 7.2 Future Challenges: Anticipating future ethical challenges in AI. 

• 7.3 Preparing for the Future: Strategies to stay ahead in ethical AI leadership.