Responsible AI in Software Development

• Basic understanding of software development workflows
• Familiarity with Agile, DevOps, or general software project practices

Audience

• Compliance teams
• Developers
• Software project managers

Responsible AI in Software Development is a foundational course that addresses the ethical, legal, and governance considerations when using AI tools in the software development process. The course emphasizes transparency, fairness, intellectual property concerns, and safe deployment of AI-generated code.

This instructor-led, live training (online or onsite) is aimed at beginner-level technical and non-technical professionals who wish to ensure ethical, compliant, and risk-aware use of AI in their software projects.

By the end of this training, participants will be able to:

• Identify and mitigate ethical and legal risks associated with AI-assisted development.
• Understand bias in AI-generated code and how to evaluate fairness.
• Manage software licenses and attribution related to AI-generated content.
• Establish governance, policy, and compliance frameworks for AI use in development teams.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Foundations of Responsible AI

• What is responsible AI and why it matters in software development
• Principles: fairness, accountability, transparency, and privacy
• Examples of ethical failures and AI misuse in codebases

Bias and Fairness in AI-Generated Code

• How LLMs can reinforce bias through training data
• Detecting and remediating biased or unsafe code suggestions
• AI hallucination and the risk of introducing errors at scale

Licensing, Attribution, and IP Considerations

• Understanding open-source licenses (MIT, GPL, Copyleft)
• Do 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
• 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 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