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Duration 7 hours
Course Outline
Foundations of Responsible AI
- Defining responsible AI and understanding its critical importance in software engineering.
- Core principles: fairness, accountability, transparency, and privacy.
- Case studies on ethical failures and real-world instances of AI misuse in codebases.
Bias and Fairness in AI-Generated Code
- How Large Language Models (LLMs) may perpetuate biases found in their training data.
- Techniques for detecting and remediating biased or potentially unsafe code suggestions.
- The phenomenon of AI hallucination and the associated risks of introducing errors at scale.
Licensing, Attribution, and IP Considerations
- Navigating open-source licenses, including MIT, GPL, and Copyleft.
- Determining when and how LLM-generated outputs require attribution.
- Conducting audits on AI-assisted code to identify third-party licensing conflicts.
Security and Compliance in AI-Assisted Development
- Ensuring code safety and preventing the adoption of insecure patterns suggested by LLMs.
- Aligning development practices with internal security guidelines and industry regulations.
- Maintaining auditable documentation for all AI-assisted decision-making processes.
Policy and Governance for Development Teams
- Drafting internal AI usage policies tailored for software teams.
- Defining acceptable use cases and identifying critical red flags.
- Strategic tool selection and responsible onboarding of AI assistants.
Evaluating and Auditing AI Output
- Utilizing structured checklists to assess the trustworthiness of generated content.
- Implementing both manual and automated reviews for AI-generated code.
- Best practices for peer-review and final sign-off processes.
Summary and Next Steps
Requirements
- Fundamental understanding of standard software development workflows.
Target Audience
- Compliance and risk management teams.
- Software developers and engineers.
- 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
