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Duration 7 hours
Course Outline
Core Principles of Responsible AI
- Defining Responsible AI and its significance within software engineering contexts
- Key principles: fairness, accountability, transparency, and privacy protection
- Case studies on ethical lapses and misuse of AI in code repositories
Addressing Bias and Ensuring Fairness in AI-Generated Code
- How Large Language Models (LLMs) may propagate biases from their training datasets
- Strategies for identifying and correcting biased or potentially unsafe code recommendations
- Understanding AI hallucinations and the associated risks of widespread errors
Licensing, Attribution, and Intellectual Property Nuances
- Interpreting open-source licenses (MIT, GPL, Copyleft)
- Determining if LLM-generated outputs necessitate specific attribution
- Conducting audits of AI-assisted code for third-party licensing conflicts
Security and Regulatory Compliance in AI-Assisted Workflows
- Safeguarding code integrity and avoiding insecure patterns suggested by LLMs
- Aligning with internal security protocols and industry regulatory standards
- Maintaining auditable records of decision-making processes involving AI
Establishing Policy and Governance for Engineering Teams
- Drafting internal AI usage guidelines for software teams
- Defining acceptable use cases and identifying potential red flags
- Selecting appropriate tools and responsibly onboarding AI assistants
Assessment and Auditing of AI Outputs
- Utilizing checklists to verify the reliability of generated content
- Performing both manual and automated reviews of AI-generated code
- Adopting best practices for peer review and final sign-off procedures
Key Takeaways and Future Recommendations
Requirements
- A fundamental grasp of software development lifecycles
- Familiarity with Agile methodologies, DevOps practices, or standard software project management
Target Audience
- Compliance specialists and teams
- Software developers and engineers
- Project managers overseeing software delivery
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
