Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Foundations of Ethical AI
- Defining responsible AI and its significance 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
- Mechanisms by which LLMs reinforce bias through training data
- Detecting and remediating biased or unsafe code suggestions
- Understanding AI hallucination and the risk of introducing errors at scale
Licensing, Attribution, and IP Considerations
- Understanding open-source licenses (MIT, GPL, Copyleft)
- Determining if 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
- Compliance with internal security guidelines and industry regulations
- Maintaining auditable documentation of AI-assisted decision-making
Policy and Governance for Development Teams
- Creating internal AI usage policies for software teams
- Defining acceptable use and identifying red flags
- Tool selection and responsible onboarding of AI assistants
Evaluating and Auditing AI Output
- Using checklists to assess trustworthiness of generated content
- Conducting manual and automated reviews of AI-generated code
- Best practices for peer-review and sign-off processes
Summary and Next Steps
Requirements
- Basic understanding of software development workflows
- Familiarity with Agile, DevOps, or general software project practices
Target 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
