Duration
2 Days (8 hours per day)
Level
Basic Level
Design and Tailor this course
As per your team needs
Upon completing this course, participants will be able to lead AI initiatives with a solid ethical foundation. They will gain the skills to identify and address biases, uphold transparency and accountability, and adhere to legal and regulatory standards. This course empowers participants to embed ethical principles into AI development and governance, fostering trust and integrity across their organizations.
Outcomes:
After completing this course, participants will be equipped to strategically guide AI projects with a strong ethical foundation. They will understand how to identify and mitigate biases, ensure transparency and accountability, and comply with legal standards. This course empowers learners to integrate ethical practices into AI development and governance, fostering trust and integrity in their organizations.
Upon completion, participants will be able to:
- Lead AI projects with a focus on ethics and accountability
- Identify and reduce biases in AI systems
- Ensure transparency and align AI practices with legal standards
- Develop and implement AI governance frameworks
- Balance innovation with ethical responsibilities
- AI and machine learning practitioners
- Business leaders and executives
- Compliance and ethics officers
- Project managers in AI-driven initiatives
- Importance and implications of ethical AI
- Case studies and discussions on ethical dilemmas
- Grasp the core principles and challenges of ethical AI
- Combines “Ethical Decision-Making Frameworks” and “Addressing Bias, Fairness, and Transparency”
- Decision-making models, strategies for bias detection, and methods to promote fairness
- Scenarios, decision-making exercises, and hands-on audits
- Apply ethical frameworks and techniques to mitigate biases
- Integrates “Understanding Legal and Compliance Standards” with “Establishing AI Governance and Accountability”
- Global AI regulations, compliance requirements, governance structures, and accountability
- Case studies, governance framework design, and compliance exercises
- Navigate legal landscapes and establish robust AI governance
- Crafting and aligning ethical AI strategies with business goals
- Workshops to create an ethical AI strategy for a real-world project
- Develop a strategic roadmap for ethical AI integration
- Expands on “Privacy and Security in AI Systems” to include risk management
- Addressing privacy, security, and risks in AI applications
- Review of privacy and security protocols and risk management strategies
- Implement best practices for data protection, security, and risk mitigation
- Anticipating emerging trends and fostering ongoing ethical practices
- Group discussions on future trends and the creation of continuous improvement plans
- Stay ahead of ethical AI trends and cultivate a culture of continuous improvement
- Basic familiarity with ethical theories and principles
- Interest in the societal and ethical dimensions of AI
- Openness to learning about AI governance and ethical standards
