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Duration 7 hours
Course Outline
Foundations of Responsible AI
- Defining responsible AI and its significance in the context of software development.
- Core principles: fairness, accountability, transparency, and privacy.
- Case studies on ethical failures and instances of AI misuse within codebases.
Bias and Fairness in AI-Generated Code
- How LLMs may perpetuate bias derived from training data.
- Techniques for detecting and remediating biased or unsafe code suggestions.
- Understanding AI hallucinations and the associated risk of introducing errors at scale.
Licensing, Attribution, and IP Considerations
- An overview of open-source licenses (e.g., MIT, GPL, Copyleft).
- Examining whether LLM-generated outputs necessitate specific attribution.
- Strategies for auditing AI-assisted code to identify third-party licensing conflicts.
Security and Compliance in AI-Assisted Development
- Ensuring code safety by avoiding insecure patterns suggested by LLMs.
- Maintaining compliance with internal security standards and industry regulations.
- Creating auditable documentation for AI-assisted decision-making processes.
Policy and Governance for Development Teams
- Formulating internal AI usage policies tailored for software teams.
- Establishing clear guidelines for acceptable use and identifying potential red flags.
- Selecting appropriate tools and responsibly onboarding AI assistants.
Evaluating and Auditing AI Output
- Utilizing checklists to assess the trustworthiness of generated content.
- Performing both manual and automated reviews of AI-generated code.
- Adopting best practices for peer-review and final sign-off procedures.
Summary and Next Steps
Requirements
- A fundamental understanding of software development workflows.
- Familiarity with Agile, DevOps, or general software project management practices.
Target Audience
- Compliance teams.
- Software 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
