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Duration 7 hours
Course Outline
Principles of Ethical Artificial Intelligence
- Defining ethical AI frameworks and their significance in software engineering
- Core principles: equity, oversight, openness, and data protection
- Case studies of ethical lapses and improper AI application in code repositories
Bias and Equity in AI-Generated Code
- Mechanisms by which Large Language Models (LLMs) propagate bias via training datasets
- Strategies for identifying and correcting biased or hazardous code recommendations
- Hallucinations in AI models and the potential for systemic error introduction
Licensing, Attribution, and Intellectual Property Obligations
- Analysis of open-source licenses (MIT, GPL, Copyleft)
- Determining attribution requirements for LLM-generated outputs
- Conducting audits of AI-assisted code for third-party licensing compliance
Security and Regulatory Compliance in AI-Augmented Development
- Ensuring code integrity and mitigating insecure patterns from LLMs
- Adherence to internal security protocols and industry standards
- Maintaining auditable records of AI-assisted decision-making processes
Policy and Governance Frameworks for Development Teams
- Formulating internal AI utilization policies for software organizations
- Establishing acceptable use guidelines and identifying risk indicators
- Selecting tools and implementing responsible onboarding of AI assistants for government operations
Evaluation and Audit of AI Output
- Applying checklists to verify the reliability of generated content
- Performing manual and automated inspections of AI-generated code
- Best practices for peer review and approval workflows
Conclusions and Recommendations
Requirements
- Foundational knowledge of software development workflows
- Familiarity with Agile, DevOps, or standard software project methodologies
Target Audience
- Compliance and oversight units
- 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
