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Duration 7 hours
Course Outline
Foundations of Responsible AI
- Defining responsible AI and understanding its significance in software development
- Core principles: fairness, accountability, transparency, and privacy
- Case studies of ethical lapses and improper AI usage in codebases
Bias and Fairness in AI-Generated Code
- How LLMs may perpetuate bias originating from training data
- Identifying and correcting biased or unsafe code recommendations
- The concept of AI hallucinations and the potential for large-scale error introduction
Licensing, Attribution, and IP Considerations
- Gaining insight into open-source licenses (MIT, GPL, Copyleft)
- Determining whether LLM-generated outputs necessitate attribution
- Reviewing AI-assisted code for potential third-party licensing conflicts
Security and Compliance in AI-Assisted Development
- Ensuring code security and preventing insecure patterns from LLMs
- Adhering to internal security protocols and industry regulatory standards
- Maintaining auditable records of AI-assisted decision-making processes
Policy and Governance for Development Teams
- Developing internal AI usage policies for software teams
- Outlining acceptable use cases and identifying warning signs
- Selecting appropriate tools and onboarding AI assistants responsibly
Evaluating and Auditing AI Output
- Applying checklists to gauge the reliability of generated content
- Performing manual and automated inspections of AI-generated code
- Best practices for peer-review and approval workflows
Summary and Next Steps
Requirements
- Fundamental knowledge of software development workflows
- Familiarity with Agile, DevOps, or standard software project methodologies
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
