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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
