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Course Outline
Foundations of Responsible AI
- Definition of responsible AI and its importance in software development.
- Core principles: fairness, accountability, transparency, and privacy.
- Case studies of ethical failures and AI misuse in codebases.
Bias and Fairness in AI-Generated Code
- How large language models (LLMs) may reinforce bias via training data.
- Detecting and correcting biased or unsafe code suggestions.
- AI hallucinations and the risk of introducing errors at scale.
Licensing, Attribution, and IP Considerations
- Understanding open-source licenses (e.g., MIT, GPL, Copyleft).
- Determining whether LLM-generated outputs require attribution.
- Auditing AI-assisted code for third-party licensing issues.
Security and Compliance in AI-Assisted Development
- Ensuring code safety and avoiding insecure patterns from LLMs.
- Aligning with internal security guidelines and industry regulations.
- Maintaining auditable documentation of AI-assisted decision-making.
Policy and Governance for Development Teams
- Developing internal AI usage policies for software teams.
- Defining acceptable use cases and identifying red flags.
- Selecting tools and responsibly onboarding AI assistants.
Evaluating and Auditing AI Output
- Using checklists to assess the trustworthiness of generated content.
- Conducting manual and automated reviews of AI-generated code.
- Implementing best practices for peer review and sign-off processes.
Summary and Next Steps
Requirements
- Basic knowledge of software development workflows.
- Familiarity with Agile, DevOps, or general software project methodologies.
Audience
- Compliance teams.
- Developers.
- Software project managers.
7 Hours
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
