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Course Outline

Foundations of Responsible AI

  • Understanding what responsible AI entails and its importance in software development
  • Core principles: fairness, accountability, transparency, and privacy
  • Case studies of ethical failures and AI misuse within codebases

Bias and Fairness in AI-Generated Code

  • How Large Language Models (LLMs) may reinforce bias through training data
  • Strategies for detecting and remediating biased or unsafe code suggestions
  • Addressing AI hallucination and the risk of introducing errors at scale

Licensing, Attribution, and IP Considerations

  • Understanding open-source licenses (MIT, GPL, Copyleft)
  • Determining if LLM-generated outputs require attribution
  • Auditing AI-assisted code for third-party licensing issues

Security and Compliance in AI-Assisted Development

  • Ensuring code safety and avoiding insecure patterns generated by LLMs
  • Ensuring compliance with internal security guidelines and industry regulations
  • Maintaining auditable documentation of AI-assisted decision-making processes

Policy and Governance for Development Teams

  • Developing internal AI usage policies for software teams
  • Defining acceptable use standards and identifying red flags
  • Selecting appropriate tools and responsibly onboarding AI assistants

Evaluating and Auditing AI Output

  • Utilizing checklists to assess the trustworthiness of generated content
  • Conducting manual and automated reviews of AI-generated code
  • Implementing best practices for peer-review and sign-off processes

Summary and Next Steps

Requirements

  • Basic understanding of software development workflows
  • Familiarity with Agile, DevOps, or general software project practices

Audience

  • Compliance teams
  • Developers
  • Software project managers
 7 Hours

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