Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Duration 7 hours
Course Outline
Foundations of Responsible AI
- Defining responsible AI and its significance in the context of software development
- 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 bias originating from training data
- Techniques for detecting and remedying biased or unsafe code suggestions
- Addressing AI hallucinations and the associated risk of introducing errors at scale
Licensing, Attribution, and IP Considerations
- Navigating open-source licenses (including MIT, GPL, and Copyleft)
- Assessing whether LLM-generated outputs necessitate attribution
- Conducting audits of AI-assisted code to identify third-party licensing issues
Security and Compliance in AI-Assisted Development
- Ensuring code integrity and avoiding insecure patterns generated by LLMs
- Aligning with internal security protocols and industry regulatory standards
- Maintaining auditable documentation of decisions made with AI assistance
Policy and Governance for Development Teams
- Drafting internal AI usage policies specifically for software teams
- Defining acceptable use cases and identifying potential red flags
- Selecting appropriate tools and responsible onboarding strategies for AI assistants
Evaluating and Auditing AI Output
- Utilising checklists to verify the trustworthiness of generated content
- Performing manual and automated reviews of AI-generated code
- Best practices for peer review and approval processes
Summary and Next Steps
Requirements
- A foundational grasp of software development workflows
- Familiarity with Agile, DevOps, or standard software project methodologies
Audience
- Compliance specialists and teams
- Software 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
