Length: 2 Days
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Introduction to AI Governance and Ethics Training by Tonex

This course provides an overview of the governance and ethical considerations related to artificial intelligence (AI). Participants will learn about the regulatory frameworks, ethical challenges, and best practices for responsible AI development and deployment.

Learning Objectives

  • Understand the principles of AI governance.
  • Identify ethical issues in AI.
  • Explore regulatory frameworks for AI.
  • Develop strategies for ethical AI deployment.
  • Analyze case studies of AI ethics.
  • Promote responsible AI practices.

Audience

  • AI Developers
  • Data Scientists
  • Policy Makers
  • Ethics Officers
  • Legal Professionals
  • Business Leaders

Program Modules

  1. Introduction to AI Governance
    • Definition and importance of AI governance
    • Historical context of AI development
    • Key stakeholders in AI governance
    • AI governance frameworks
    • Challenges in AI governance
    • Case studies
  2. Ethical Issues in AI
    • Bias and fairness in AI
    • Privacy and data protection
    • Accountability and transparency
    • Human rights implications
    • Ethical dilemmas in AI
    • Practical exercises
  3. Regulatory Frameworks
    • Overview of AI regulations
    • National vs. international regulations
    • Role of regulatory bodies
    • Compliance requirements
    • Future regulatory trends
    • Case studies
  4. Strategies for Ethical AI Deployment
    • Developing ethical AI guidelines
    • Implementing fairness and transparency
    • Ensuring accountability
    • Mitigating bias in AI models
    • Ethical AI auditing
    • Practical exercises
  5. Case Studies of AI Ethics
    • Analysis of real-world AI ethical issues
    • Lessons learned from past incidents
    • Best practices for ethical AI
    • Industry-specific ethical challenges
    • Ethical success stories
    • Practical exercises
  6. Promoting Responsible AI Practices
    • Building an ethical AI culture
    • Training and awareness programs
    • Collaboration with stakeholders
    • Public engagement and communication
    • Continuous improvement in AI ethics
    • Case studies

 

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