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Duration 7 hours
Course Outline
Core Principles of Responsible AI
- Defining responsible AI and its critical role in software engineering
- Key principles: fairness, accountability, transparency, and privacy
- Case studies illustrating ethical shortcomings and misuse of AI in codebases
Bias and Fairness in AI-Produced Code
- How Large Language Models (LLMs) may perpetuate bias via training data
- Strategies for identifying and correcting biased or unsafe code recommendations
- Addressing AI hallucinations and the risk of scaling errors
Licensing, Attribution, and Intellectual Property
- Navigating open-source licenses (MIT, GPL, Copyleft)
- Determining attribution requirements for LLM-generated outputs
- Auditing 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 protocols and industry standards
- Maintaining auditable records of AI-assisted decision-making processes
Governance and Policy Frameworks for Teams
- Drafting internal AI usage policies for software teams
- Establishing clear guidelines for acceptable use and identifying red flags
- Selecting appropriate tools and responsibly onboarding AI assistants
Assessment and Audit of AI Outputs
- Applying checklists to evaluate the reliability of generated content
- Performing manual and automated reviews of AI-generated code
- Implementing best practices for peer review and approval processes
Recap and Future Directions
Requirements
- A solid grasp of standard software development workflows
- Familiarity with Agile, DevOps, or broader software project methodologies
Target Audience
- Compliance specialists
- Software developers
- Software project managers
Testimonials (2)
The session was highly interactive and applicable to the business.
Jorge Boscan - Chevron Global Technology Services Company
Course - Advanced GitHub Copilot & AI for Projects and Infrastructure
Machine Translated
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
