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Duration 7 hours
Course Outline
Foundations of Responsible AI
- Defining responsible AI and its significance in software development
- Core principles: fairness, accountability, transparency, and privacy
- Case studies of ethical lapses and AI misuse within codebases
Bias and Fairness in AI-Generated Code
- How Large Language Models (LLMs) may perpetuate bias via training data
- Strategies for identifying and correcting biased or unsafe code recommendations
- The impact of AI hallucinations and the risk of widespread error introduction
Licensing, Attribution, and Intellectual Property
- Navigating open-source licenses (MIT, GPL, Copyleft)
- Determining if LLM-generated outputs necessitate attribution
- Auditing AI-assisted code for potential third-party licensing conflicts
Security and Compliance in AI-Assisted Development
- Ensuring code safety and preventing insecure patterns from LLMs
- Aligning with internal security protocols and industry regulations
- Maintaining auditable documentation of AI-assisted decision processes
Policy and Governance for Development Teams
- Drafting internal AI usage policies for software teams
- Establishing acceptable use guidelines and identifying warning signs
- Selecting 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
- Adopting best practices for peer-review and approval workflows
Summary and Future Directions
Requirements
- Foundational knowledge of software development workflows
- Familiarity with Agile, DevOps, or standard software project methodologies
Intended Audience
- Compliance specialists and teams
- 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
