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Duration 7 hours
Course Outline
Foundations of Responsible AI
- Defining responsible AI and its critical role in software development
- Core principles: fairness, accountability, transparency, and privacy
- Case studies illustrating ethical lapses and AI misuse in codebases
Bias and Fairness in AI-Generated Code
- How Large Language Models (LLMs) may perpetuate bias via training data
- Identifying and correcting biased or unsafe code suggestions
- Understanding AI hallucinations and the potential for widespread errors
Licensing, Attribution, and Intellectual Property
- Navigating open-source licenses (MIT, GPL, Copyleft)
- Determining if LLM-generated outputs necessitate attribution
- Reviewing AI-assisted code for third-party licensing conflicts
Security and Compliance in AI-Assisted Development
- Ensuring code integrity by avoiding insecure LLM patterns
- Aligning with internal security protocols and industry regulations
- Maintaining auditable records of AI-informed decisions
Policy and Governance for Development Teams
- Formulating internal AI usage policies for software teams
- Establishing acceptable use guidelines and identifying red flags
- Selecting appropriate tools and responsibly onboarding AI assistants
Evaluating and Auditing AI Output
- Employing checklists to gauge the reliability of generated content
- Performing manual and automated reviews of AI-produced code
- Implementing best practices for peer-review and approval workflows
Summary and Next Steps
Requirements
- A foundational grasp of standard software development workflows
- Familiarity with Agile, DevOps, or broader software project methodologies
Target Audience
- Compliance professionals
- Software developers
- Project managers in software environments
Testimonials (1)
That i gained a knowledge regarding streamlit library from python and for sure i'll try to use it to improve applications in my team which are made in R shiny
