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Duration 7 hours
Course Outline
Foundations of Responsible AI
- Defining responsible AI and its significance in software engineering
- 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 propagate bias originating from training data
- Strategies for identifying and correcting biased or unsafe code suggestions
- Addressing AI hallucinations and the potential for large-scale errors
Licensing, Attribution, and IP Implications
- Navigating open-source licenses (MIT, GPL, Copyleft)
- Determining if LLM-generated outputs necessitate attribution
- Reviewing AI-assisted code for potential third-party licensing conflicts
Security and Compliance in AI-Assisted Development
- Ensuring code integrity and preventing insecure patterns from LLMs
- Adhering to internal security standards and industry regulations
- Maintaining auditable records of AI-assisted decision-making processes
Policy and Governance for Development Teams
- Drafting internal AI usage policies for software teams
- Establishing 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 reliability of generated content
- Performing manual and automated reviews of AI-generated code
- Applying best practices for peer review and approval workflows
Summary and Future Actions
Requirements
- A foundational grasp of software development workflows.
- Familiarity with Agile, DevOps, or broader 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
