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Course Outline

Foundations of Responsible Artificial Intelligence

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

Addressing Bias and Fairness in AI-Generated Code

  • Mechanisms by which large language models may perpetuate bias through training data
  • Methods for detecting and remediating biased or unsafe code suggestions
  • Risks associated with AI hallucination and the potential for error propagation at scale

Licensing, Attribution, and Intellectual Property Considerations

  • Overview of open-source licenses, including MIT, GPL, and Copyleft
  • Determination of attribution requirements for outputs generated by large language models
  • Procedures for auditing AI-assisted code to identify third-party licensing conflicts

Security and Compliance in AI-Assisted Development

  • Strategies for ensuring code safety and mitigating insecure patterns derived from large language models
  • Adherence to internal security guidelines and applicable industry regulations
  • Requirements for auditable documentation of decision-making processes involving artificial intelligence

Policy Frameworks and Governance for Development Teams

  • Establishment of internal policies governing the use of artificial intelligence within software teams
  • Definition of acceptable usage standards and identification of prohibited practices
  • Criteria for tool selection and responsible integration of AI assistants into workflows

Evaluation and Auditing of AI Output

  • Utilization of checklists to assess the reliability and trustworthiness of generated content
  • Implementation of manual and automated review processes for code produced by artificial intelligence
  • Best practices for peer review procedures and formal sign-off protocols

Summary and Next Steps

Requirements

  • Foundational knowledge of software engineering processes
  • Working proficiency in Agile, DevOps, or standard software project management methodologies

Intended Audience

  • Compliance and governance personnel
  • Software engineers
  • Project managers overseeing software initiatives
 7 Hours

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