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Duration 7 hours
Course Outline
Foundations of Responsible 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 in codebases
Bias and Fairness in AI-Generated Code
- How LLMs may perpetuate bias derived from training data
- Identifying and addressing biased or unsafe code suggestions
- AI hallucination and the potential for large-scale error introduction
Licensing, Attribution, and IP Considerations
- Navigating open-source licenses (MIT, GPL, Copyleft)
- Determining if LLM-generated outputs necessitate attribution
- Auditing AI-assisted code for third-party licensing compliance
Security and Compliance in AI-Assisted Development
- Ensuring code security and preventing insecure patterns from LLMs
- Adhering to internal security protocols and industry regulations
- Maintaining auditable documentation of AI-assisted decision-making
Policy and Governance for Development Teams
- Drafting 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
- Applying checklists to assess the trustworthiness of generated content
- Performing manual and automated reviews of AI-generated code
- Implementing best practices for peer-review and approval processes
Summary and Next Steps
Requirements
- A basic grasp of software development workflows
- Familiarity with Agile, DevOps, or general software project methodologies
Target 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
