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Course Outline

Foundations of Ethical 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 within codebases.

Bias and Fairness in AI-Generated Code

  • How large language models (LLMs) can perpetuate bias through training data.
  • Strategies for detecting and remedying biased or unsafe code suggestions.
  • Addressing AI hallucinations and the risk of introducing errors at scale.

Licensing, Attribution, and Intellectual Property Considerations

  • Understanding open-source licenses such as MIT, GPL, and Copyleft.
  • Determining whether 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.
  • Adhering to 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 cases and identifying red flags.
  • Tool selection and responsible onboarding of 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 processes.

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.
  • Software developers.
  • Software project managers.
 7 Hours

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