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Duration 7 hours
Course Outline
Foundations of Responsible AI
- Defining responsible AI and its significance in software development.
- Key principles: fairness, accountability, transparency, and privacy.
- Case studies of ethical failures and AI misuse within codebases.
Bias and Fairness in AI-Generated Code
- How LLMs may reinforce bias through training data.
- Techniques for detecting and remediating biased or unsafe code suggestions.
- Addressing AI hallucinations and the risk of scaling errors.
Licensing, Attribution, and IP Considerations
- Understanding open-source licenses (MIT, GPL, Copyleft).
- Determining if LLM-generated outputs require attribution.
- 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.
- Adhering to internal security guidelines and industry regulations.
- Creating auditable documentation for AI-assisted decision-making.
Policy and Governance for Development Teams
- Drafting internal AI usage policies for software teams.
- Defining acceptable use cases and identifying red flags.
- Selecting appropriate tools and onboarding AI assistants responsibly.
Evaluating and Auditing AI Output
- Utilising checklists to assess the trustworthiness of generated content.
- Performing manual and automated reviews of AI-generated code.
- Best practices for peer review and sign-off processes.
Summary and Next Steps
Requirements
- A fundamental understanding of software development workflows.
- Familiarity with Agile, DevOps, or general software project methodologies.
Target Audience
- Compliance teams.
- 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
