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Duration 7 hours
Course Outline
Foundations of Responsible AI
- Defining responsible AI and its importance in software development
- Core principles: fairness, accountability, transparency, and privacy
- Case studies on ethical failures and AI misuse in codebases
Bias and Fairness in AI-Generated Code
- How LLMs may amplify bias via training data
- Techniques for detecting and rectifying biased or unsafe code suggestions
- AI hallucination and the potential for widespread error introduction
Licensing, Attribution, and IP Considerations
- Insights into open-source licenses (MIT, GPL, Copyleft)
- Determining whether LLM-generated outputs necessitate attribution
- Reviewing AI-assisted code for third-party licensing conflicts
Security and Compliance in AI-Assisted Development
- Guaranteeing code safety and preventing insecure patterns from LLMs
- Adhering to internal security guidelines and industry regulations
- Maintaining auditable documentation of AI-assisted decision-making
Policy and Governance for Development Teams
- Formulating internal AI usage policies for software teams
- Establishing acceptable use guidelines and identifying red flags
- Selecting tools and responsibly onboarding AI assistants
Evaluating and Auditing AI Output
- Utilizing checklists to gauge the trustworthiness of generated content
- Performing manual and automated reviews of AI-generated code
- Best practices for peer review and sign-off workflows
Summary and Next Steps
Requirements
- A foundational grasp of software development workflows
- Familiarity with Agile, DevOps, or standard software project practices
Audience
- Compliance teams
- Developers
- Software project managers
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
