Course Outline

Foundations of Responsible AI

  • What is responsible AI and why it matters in software development
  • Principles: fairness, accountability, transparency, and privacy
  • Examples of ethical failures and AI misuse in codebases

Bias and Fairness in AI-Generated Code

  • How LLMs can reinforce bias through training data
  • Detecting and remediating biased or unsafe code suggestions
  • AI hallucination and the risk of introducing errors at scale

Licensing, Attribution, and IP Considerations

  • Understanding open-source licenses (MIT, GPL, Copyleft)
  • Do 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 from LLMs
  • Compliance with internal security guidelines and industry regulations
  • Auditable documentation of AI-assisted decision-making

Policy and Governance for Development Teams

  • Creating internal AI usage policies for software teams
  • Defining acceptable use and red flags
  • Tool selection and responsible onboarding of AI assistants

Evaluating and Auditing AI Output

  • Using checklists to assess 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 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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