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Duration 7 hours
Course Outline
Foundations of Responsible AI
- Defining responsible AI and its significance in the software development context.
- Core principles: fairness, accountability, transparency, and privacy.
- Case studies of ethical failures and instances of AI misuse in codebases.
Bias and Fairness in AI-Generated Code
- How LLMs may perpetuate biases derived from training data.
- Strategies for detecting and correcting biased or unsafe code suggestions.
- Understanding AI hallucination and the potential for introducing errors at scale.
Licensing, Attribution, and Intellectual Property Considerations
- Analyzing open-source licenses, including MIT, GPL, and Copyleft.
- Assessing the need for attribution in LLM-generated outputs.
- Auditing AI-assisted code for potential third-party licensing conflicts.
Security and Compliance in AI-Assisted Development
- Ensuring code safety and preventing insecure patterns generated by LLMs.
- Aligning with internal security guidelines and broader industry regulations.
- Maintaining auditable documentation of AI-assisted decision-making processes.
Policy and Governance for Development Teams
- Drafting internal AI usage policies tailored for software teams.
- Establishing acceptable use criteria and identifying red flags.
- Selecting appropriate tools and responsibly onboarding AI assistants.
Evaluating and Auditing AI Output
- Applying checklists to gauge the reliability of generated content.
- Performing both manual and automated reviews of AI-generated code.
- Implementing best practices for peer review and sign-off procedures.
Summary and Next Steps
Requirements
- A foundational grasp of software development workflows.
- Familiarity with Agile, DevOps, or standard software project methodologies.
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
