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Duration 7 hours
Course Outline
Foundations of Responsible AI
- Defining responsible AI and its significance in the software development context.
- Core principles: fairness, accountability, transparency, and privacy.
- Case studies highlighting ethical lapses and AI misuse within codebases.
Bias and Fairness in AI-Generated Code
- Examining how LLMs may propagate bias derived from training data.
- Strategies for detecting and correcting biased or unsafe code suggestions.
- Addressing AI hallucinations and the potential for introducing errors at scale.
Licensing, Attribution, and IP Considerations
- Clarifying open-source license types (MIT, GPL, Copyleft).
- Determining whether LLM-generated outputs require specific attribution.
- Conducting audits of 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.
- Maintaining compliance with internal security standards and broader industry regulations.
Policy and Governance for Development Teams
- Formulating internal AI usage policies for software teams.
- Establishing clear acceptable use guidelines and identifying red flags.
- Selecting appropriate tools and responsibly onboarding AI assistants.
Evaluating and Auditing AI Output
- Utilizing checklists to verify the trustworthiness of generated content.
- Performing manual and automated reviews of AI-generated code.
Summary and Next Steps
Requirements
- A fundamental grasp of standard software development workflows.
Target Audience
- Compliance and legal teams.
- Software developers.
- Project managers overseeing software initiatives.
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
