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AI Safety and Security Training

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Instructor: AI SafeGuard

Course Description: This course provides a comprehensive introduction to the field of AI safety and security, exploring the technical, ethical, and societal challenges posed by increasingly intelligent systems. Students will gain a thorough understanding of potential risks associated with artificial intelligence, analyze existing safety approaches, and engage in critical discussions about how to ensure the responsible development and deployment of AI technologies.

Course Objectives:

  • Define and explain key concepts in AI safety and security.
  • Identify potential risks associated with artificial intelligence, including bias, explainability, robustness, and existential risks.
  • Analyze existing approaches to mitigating AI safety risks, such as formal verification, adversarial training, and interpretable models.
  • Explore the ethical and societal implications of AI, including fairness, transparency, and accountability.
  • Engage in critical discussions about responsible AI development and deployment strategies.
  • Develop essential skills for researching and staying informed about emerging issues in AI safety and security.

Course Outline:

Module 1: Introduction to AI Safety and Security

  • What is AI safety and security?
  • The history and landscape of AI safety research
  • Potential risks associated with artificial intelligence
  • Case studies of AI safety failures

Module 2: Technical Approaches to AI Safety

  • Formal verification and program analysis
  • Adversarial training and robustness
  • Explainable AI and interpretability
  • Safety-critical control systems and autonomous systems

Module 3: Ethical and Societal Implications of AI

  • Algorithmic bias and discrimination
  • Transparency and accountability in AI decision-making
  • Privacy and data security concerns
  • The future of work and the impact of AI on society

Module 4: Responsible AI Development and Deployment

  • AI safety best practices and frameworks
  • Regulatory considerations and policy debates
  • Multistakeholder collaboration and governance
  • Individual and professional responsibility in AI development

Course Requirements:

  • Active participation in class discussions and activities
  • Completion of weekly readings and assignments
  • Midterm exam and final research paper


  • Class participation: 20%
  • Readings and assignments: 30%
  • Midterm exam: 25%
  • Final research paper: 25%


  • Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach (Chapters 30-33)
  • Toby Ord, The Precipice: Existential Risk and the Future of Humanity
  • Joanna Bryson, The Right to Control Our Own Minds: Replacing Alienation with Agency
  • Eliezer Yudkowsky, The Alignment Problem
  • Course website with additional readings and resources

Recommended Background:

  • Basic understanding of artificial intelligence concepts
  • Familiarity with ethical reasoning and philosophy of technology

Note: This syllabus is subject to change at the instructor’s discretion.