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Duration 7 hours
Course Outline
Core Principles of Responsible AI
- Defining responsible AI and its importance in software engineering
- Key principles: equity, responsibility, openness, and data privacy
- Case studies illustrating ethical breaches and misuse of AI in codebases
Addressing Bias and Fairness in AI-Generated Code
- How Large Language Models (LLMs) may propagate bias from training data
- Identifying and correcting biased or unsafe code recommendations
- Understanding AI hallucinations and the potential for scaled error introduction
Licensing, Attribution, and Intellectual Property
- Navigating open-source licenses (such as MIT, GPL, Copyleft)
- Determining if LLM outputs necessitate specific attributions
- Reviewing AI-assisted code for potential third-party licensing conflicts
Security and Compliance in AI-Assisted Workflows
- Safeguarding code integrity and preventing insecure patterns from LLMs
- Adhering to internal security standards and industry regulations
- Maintaining auditable records of AI-informed decision making
Governance and Policy for Development Teams
- Drafting internal AI usage guidelines for software teams
- Establishing clear boundaries for acceptable use and identifying warning signs
- Selecting appropriate tools and onboarding AI assistants responsibly
Assessment and Audit of AI Output
- Utilizing checklists to verify the reliability of generated content
- Performing manual and automated inspections of AI-generated code
- Adopting best practices for peer review and approval processes
Recap and Future Directions
Requirements
- Foundational knowledge of software development lifecycles
- Awareness of Agile, DevOps, or general software project methodologies
Target Audience
- Compliance specialists
- Software engineers
- Software project leads
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
