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Duration 7 hours
Course Outline
Fundamentals of Responsible AI
- Defining responsible AI and its significance in software engineering
- Core principles: fairness, accountability, transparency, and data privacy
- Case studies on ethical lapses and improper AI usage in code repositories
Bias and Equity in AI-Generated Code
- How Large Language Models (LLMs) may propagate bias via training datasets
- Identifying and correcting biased or unsafe code recommendations
- AI hallucinations and the potential for large-scale error introduction
Licensing, Attribution, and Intellectual Property
- Interpreting open-source licenses (MIT, GPL, Copyleft)
- Determining if LLM-generated output necessitates attribution
- Reviewing AI-assisted code for third-party licensing conflicts
Security and Compliance in AI-Enhanced Development
- Ensuring code integrity and avoiding insecure patterns suggested by LLMs
- Adhering to internal security protocols and industry regulatory standards
- Maintaining auditable records of AI-informed decision-making
Policy and Governance for Engineering Teams
- Drafting internal AI usage policies for software groups
- Establishing acceptable use guidelines and identifying warning signs
- Selecting appropriate tools and responsibly onboarding AI assistants
Assessment and Audit of AI Output
- Applying checklists to verify the reliability of generated content
- Performing manual and automated inspections of AI-created code
- Best practices for peer reviews and approval workflows
Recap and Future Actions
Requirements
- Fundamental comprehension of software development workflows
- Knowledge of Agile, DevOps, or general software project methodologies
Target Audience
- Compliance departments
- 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
