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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 within codebases
Bias and Fairness in AI-Generated Code
- How LLMs may perpetuate bias through training data
- Techniques for detecting and correcting biased or unsafe code suggestions
- Understanding AI hallucination and the potential for scaled error introduction
Licensing, Attribution, and IP Considerations
- Navigating open-source licenses (MIT, GPL, Copyleft)
- Assessing the necessity for attribution on 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 from LLM outputs
- Aligning with internal security guidelines and industry regulatory standards
- Maintaining auditable documentation of AI-assisted decision-making processes
Policy and Governance for Development Teams
- Formulating internal AI usage policies for software teams
- Defining acceptable use cases and identifying red flags
- Strategic tool selection and the responsible onboarding of AI assistants
Evaluating and Auditing AI Output
- Utilizing checklists to assess the trustworthiness of generated content
- Executing 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 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
