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Course Outline
Foundations of Responsible AI
- Defining responsible AI and its importance in software development
- Core principles: fairness, accountability, transparency, and privacy
- Case studies of ethical failures and AI misuse in codebases
Bias and Fairness in AI-Generated Code
- How LLMs can perpetuate bias via training data
- Detecting and remedying biased or unsafe code suggestions
- AI hallucination and the risk of introducing errors at scale
Licensing, Attribution, and IP Considerations
- Understanding open-source licenses (MIT, GPL, Copyleft)
- Requirements for attributing LLM-generated outputs
- Auditing AI-assisted code for third-party licensing issues
Security and Compliance in AI-Assisted Development
- Ensuring code safety and avoiding insecure patterns from LLMs
- Compliance with internal security guidelines and industry regulations
- Maintaining auditable documentation of AI-assisted decision-making
Policy and Governance for Development Teams
- Developing internal AI usage policies for software teams
- Defining acceptable use and identifying red flags
- Selecting tools and responsibly onboarding AI assistants
Evaluating and Auditing AI Output
- Using checklists to assess the trustworthiness of generated content
- Conducting manual and automated reviews of AI-generated code
- Best practices for peer-review and sign-off processes
Summary and Next Steps
Requirements
- Basic understanding of software development workflows
- Familiarity with Agile, DevOps, or general software project practices
Audience
- Compliance teams
- Developers
- Software project managers
7 Hours
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
