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Course Outline

Foundations of Responsible AI

  • Defining responsible AI and its significance in software development
  • Core principles: fairness, accountability, transparency, and privacy
  • Case studies of ethical failures and AI misuse in codebases

Bias and Fairness in AI-Generated Code

  • How Large Language Models (LLMs) may perpetuate bias via training data
  • Strategies for detecting and remedying biased or unsafe code suggestions
  • AI hallucinations and the risk of introducing errors at scale

Licensing, Attribution, and IP Considerations

  • Comprehending open-source licenses (MIT, GPL, Copyleft)
  • Requirements for attributing LLM-generated outputs
  • Auditing AI-assisted code for third-party licensing conflicts

Security and Compliance in AI-Assisted Development

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

Policy and Governance for Development Teams

  • Developing internal AI usage policies for software teams
  • Defining acceptable use cases and identifying red flags
  • Tool selection and the responsible onboarding of 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
  • Best practices for peer-review and sign-off processes

Summary and Next Steps

Requirements

  • Basic comprehension 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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