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Duration 7 hours
Course Outline
Foundations of Responsible AI
- Defining responsible AI and its importance in the software development context
- Core principles: fairness, accountability, transparency, and privacy
- Case studies illustrating ethical failures and misuse of AI in codebases
Bias and Fairness in AI-Generated Code
- How Large Language Models (LLMs) may propagate biases from training data
- Techniques for detecting and correcting biased or unsafe code recommendations
- The phenomenon of AI hallucination and the potential for large-scale error introduction
Licensing, Attribution, and Intellectual Property
- Navigating open-source licenses (MIT, GPL, Copyleft)
- Determining when 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 the adoption of insecure patterns from LLMs
- Adhering to internal security protocols and industry regulatory standards
- Maintaining auditable records of AI-informed decision-making processes
Policy and Governance for Development Teams
- Formulating internal AI usage guidelines for software teams
- Establishing boundaries for acceptable use and identifying warning signs
- Selecting appropriate tools and onboarding AI assistants responsibly
Evaluating and Auditing AI Output
- Utilizing checklists to verify the reliability of generated content
- Performing both manual and automated assessments of AI-generated code
- Implementing best practices for peer review and approval workflows
Conclusion and Future Directions
Requirements
- Foundational knowledge 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
