Public Training with Exam: November 14-15
This certification course, developed by NLL.ai in collaboration with Tonex, aims to equip participants with the knowledge and skills necessary to design, implement, and manage AI systems that prioritize human safety and ethical considerations. The course includes theoretical foundations, practical applications, and an examination to certify participants as AI Safety and Ethics Specialists.
Format: Live virtual training with interactive sessions and practical exercises
Learning Objectives:
- Understand the fundamental principles of AI safety and ethics.
- Learn to design AI systems with human values and safety in mind.
- Develop skills to implement robust safety mechanisms and ethical guidelines.
- Gain knowledge of regulatory and legal frameworks governing AI.
- Learn practical approaches to AI transparency, accountability, and governance.
Target Audience:
- AI developers and engineers
- Project managers and team leaders in AI projects
- Regulatory and compliance officers
- Policy makers and legal professionals
- Anyone interested in AI safety and ethics
Program Modules:
Part 1: Introduction to AI Safety and Ethics
Module 1: Overview of AI and Its Impacts
- Historical context and evolution of AI
- Current and future applications of AI
Module 2: Ethical AI Design
- Value alignment and human-centric AI
- Transparency and explainability in AI systems
- Case studies of ethical dilemmas in AI
Part 2: Technical and Human Oversight
Module 3: Robust and Safe AI Systems
- Safety mechanisms and fail-safes in AI
- Testing and validation of AI systems
- Case studies on AI safety incidents
Module 4: Human Oversight in AI
- Human-in-the-loop systems
- Monitoring and control of AI behaviors
- Real-world applications and best practices
Part 3: Regulatory, Governance, and Practical Measures
Module 5: Regulatory and Legal Frameworks
- Overview of global AI regulations and standards
- Accountability and legal implications for AI developers
Module 6: Collaboration and Governance
- International cooperation on AI safety
- AI governance models and structures
- Public engagement and societal impacts
Module 7: Research and Development in AI Safety
- Current research trends and future directions
- Interdisciplinary approaches to AI safety
- Practical measures for safe AI implementation
Exam
Certification Exam: Certified AI Safety and Ethics Specialist (CASES™)
- Format: Multiple-choice and scenario-based questions
- Duration: 2 hours
- Passing Score: 70%