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Course Outline
Foundations of Responsible AI
- Defining responsible AI and its critical role in software development.
- Core principles: fairness, accountability, transparency, and privacy.
- Case studies of ethical failures and AI misuse within codebases.
Bias and Fairness in AI-Generated Code
- Mechanisms by which LLMs can perpetuate bias through training data.
- Strategies for detecting and remediating biased or unsafe code suggestions.
- Understanding AI hallucination and the risks of introducing errors at scale.
Licensing, Attribution, and Intellectual Property Considerations
- Overview of open-source licenses, including MIT, GPL, and Copyleft.
- Whether LLM-generated outputs require attribution.
- Auditing AI-assisted code for potential third-party licensing conflicts.
Security and Compliance in AI-Assisted Development
- Ensuring code safety by avoiding insecure patterns often suggested by LLMs.
- Aligning with internal security guidelines and industry regulations.
- Maintaining auditable documentation of AI-assisted decision-making processes.
Policy and Governance for Development Teams
- Developing internal AI usage policies for software teams.
- Defining acceptable use cases and identifying red flags.
- Selecting appropriate tools and responsibly onboarding AI assistants.
Evaluating and Auditing AI Output
- Utilizing 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 procedures.
Summary and Next Steps
Requirements
- A fundamental understanding of software development workflows.
- Familiarity with Agile, DevOps, or general software project management practices.
Target Audience
- Compliance teams.
- Software developers.
- Project managers in the software industry.
7 Hours
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
