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Duration 7 hours
Course Outline
Core Principles of Responsible AI
- Defining responsible AI and its significance in the software development context
- Key principles: fairness, accountability, transparency, and privacy
- Case studies of ethical lapses and AI misuse in codebases
Bias and Fairness in AI-Generated Code
- How Large Language Models (LLMs) may perpetuate bias derived from training data
- Identifying and correcting biased or unsafe code suggestions
- AI hallucinations and the potential for widespread error introduction
Licensing, Attribution, and Intellectual Property
- Navigating open-source licenses (MIT, GPL, Copyleft)
- Determining if LLM outputs necessitate attribution
- Reviewing AI-assisted code for third-party license conflicts
Security and Compliance in AI-Assisted Development
- Ensuring code safety and preventing insecure patterns from LLMs
- Adhering to internal security protocols and industry regulations
- Creating auditable records of AI-driven decisions
Policy and Governance for Development Teams
- Developing internal AI usage guidelines for software teams
- Establishing acceptable use cases and identifying warning signs
- Selecting tools and responsibly onboarding AI assistants
Evaluating and Auditing AI Output
- Utilizing checklists to verify the reliability of generated content
- Performing manual and automated checks on AI-generated code
- Best practices for peer review and approval workflows
Recap and Future Steps
Requirements
- Fundamental knowledge of software development processes
- Awareness of 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
