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Duration 7 hours
Course Outline
Fundamentals of Responsible AI
- Defining responsible AI and its critical importance in software development.
- Core principles: fairness, accountability, transparency, and privacy.
- Case studies of ethical lapses and AI misuse in codebases.
Bias and Fairness in AI-Generated Code
- How LLMs may amplify biases stemming from training data.
- Techniques for identifying and correcting biased or unsafe code recommendations.
- Understanding AI hallucinations and the potential for large-scale error introduction.
Licensing, Attribution, and Intellectual Property Implications
- Navigating open-source licenses (MIT, GPL, Copyleft).
- Assessing whether LLM-generated outputs necessitate attribution.
- Conducting audits on AI-assisted code for third-party licensing conflicts.
Security and Compliance in AI-Assisted Development
- Guaranteeing code safety and preventing insecure patterns from LLMs.
- Aligning with internal security standards and industry regulatory requirements.
- Maintaining auditable records of AI-assisted decision-making processes.
Policy and Governance for Development Teams
- Formulating internal AI usage policies for software teams.
- Establishing acceptable use guidelines and identifying warning signs.
- Selecting tools and responsibly onboarding AI assistants.
Assessment and Audit of AI Output
- Employing checklists to evaluate the reliability of generated content.
- Performing both manual and automated reviews of AI-generated code.
- Implementing best practices for peer review and approval workflows.
Recap and Future Directions
Requirements
- A fundamental grasp of software development processes.
- Familiarity with Agile, DevOps, or standard software project methodologies.
Target Audience
- Compliance professionals.
- Software 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
