Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence Training by Tonex
This course is designed to provide participants with comprehensive knowledge and practical skills for developing and using Artificial Intelligence (AI) applications in a safe, secure, and trustworthy manner. Participants will learn about the ethical considerations, potential risks, security threats, and best practices associated with AI development and deployment. Through hands-on exercises, case studies, and interactive discussions, participants will gain insights into ensuring the responsible and ethical use of AI technologies.
Learning Objectives:
- Understand the ethical principles and considerations in AI development.
- Identify potential risks and security threats associated with AI applications.
- Learn best practices for ensuring the safety, security, and trustworthiness of AI systems.
- Develop skills in implementing security controls, data protection measures, and privacy-enhancing techniques for AI.
- Gain knowledge of regulatory frameworks, standards, and compliance requirements related to AI.
Audience:
- Software developers and engineers
- AI researchers and practitioners
- Data scientists and analysts
- IT professionals and security experts
- Compliance officers and legal advisors
- Business executives and decision-makers
Course Modules:
Day 1: Understanding Ethical Considerations and Risks in AI
- Introduction to Ethical AI Development
- Ethical principles in AI
- Bias and fairness in AI algorithms
- Transparency and explainability in AI systems
- Privacy and Data Protection
- Data privacy regulations (e.g., GDPR, CCPA)
- Anonymization and pseudonymization techniques
- Data governance and compliance
- Security Threats in AI
- Cybersecurity risks in AI applications
- Adversarial attacks and defenses
- Secure development lifecycle for AI
Day 2: Best Practices for Safe and Trustworthy AI
- AI Model Validation and Testing
- Model validation techniques
- Testing for reliability and robustness
- Error handling and fail-safe mechanisms
- Responsible AI Deployment
- Governance frameworks for AI
- Responsible AI guidelines and toolkits
- Ethics committees and oversight mechanisms
- Regulatory Compliance and Legal Aspects
- Regulatory landscape for AI (e.g., AI Act, AI Ethics Guidelines)
- Intellectual property rights and licensing
- Risk management and legal considerations
- Case Studies and Practical Applications
- Real-world examples of ethical dilemmas in AI
- Case studies on AI security incidents and lessons learned
- Hands-on exercises and group discussions
Delivery Format:
- Instructor-led sessions
- Hands-on exercises and workshops
- Case studies and group discussions
- Q&A sessions and interactive learning
- Course materials, resources, and references
Assessment and Certification:
Participants will be assessed based on their participation in discussions, completion of hands-on exercises, and a final assessment covering key concepts and practical applications. A certificate of completion will be awarded to participants who successfully pass the assessment.