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Course Outline

Foundations of Responsible AI

  • Understanding responsible AI and its importance 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 through training data
  • Detecting and correcting biased or unsafe code suggestions
  • AI hallucinations and the potential for scalable errors

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 compliance

Security and Compliance in AI-Assisted Development

  • Ensuring code safety and avoiding insecure patterns from LLMs
  • Aligning with 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
  • Selecting tools and responsibly integrating AI assistants

Evaluating and Auditing AI Output

  • Using 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 procedures

Summary and Next Steps

Requirements

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

Target Audience

  • Compliance teams
  • Developers
  • Software project managers
 7 Hours

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