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Course Outline

Introduction to Ethics in AI

  • Recognizing the critical significance of ethical standards in AI applications
  • Examining historical developments and contemporary ethical discussions
  • Establishing core ethical principles for the implementation of AI systems

Ethical Challenges with LLMs

  • Navigating privacy issues and ensuring robust data protection measures
  • Evaluating transparency, accountability, and algorithmic bias within LLMs
  • Analyzing the influence of LLMs on the workforce and broader societal structures

Applying Ethical Frameworks to LLMs

  • Utilizing structured frameworks for ethical decision-making in AI contexts
  • Reviewing case studies involving ethical complexities in LLM implementation
  • Formulating directives for the ethical utilization of LLMs for government operations

Strategies for Ethical LLM Deployment

  • Adhering to best practices for responsible AI development cycles
  • Facilitating engagement with stakeholders and incorporating diverse perspectives
  • Cultivating an organizational environment that prioritizes ethical AI standards

Hands-on Lab: Ethical Analysis of LLM Use Cases

  • Evaluating practical scenarios involving the application of LLMs
  • Measuring ethical consequences and developing appropriate policy responses
  • Delivering findings and strategic recommendations for consideration

Summary and Next Steps

Requirements

  • A foundational understanding of artificial intelligence and machine learning concepts
  • Practical experience with ethical decision-making frameworks
  • Knowledge of LLMs and their broader societal implications

Audience

  • AI professionals and ethicists
  • Data scientists and engineers
  • Policy makers and stakeholders involved in AI governance
 7 Hours

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