Ethics and Safety in Open AI

The Ethics and Safety in Open AI course is designed for developers, engineers, and technical product builders who are new to Generative AI but already have intermediate machine learning knowledge, basic Python proficiency, and familiarity with development environments such as VS Code, and who want to engineer, customize, and deploy open generative AI solutions while avoiding vendor lock-in. The course equips learners with the frameworks and tools needed to ensure responsible use of generative AI models. The course begins with bias detection and mitigation, where learners identify harmful patterns in datasets and outputs, apply quantitative evaluation techniques, and implement mitigation strategies. Next, learners design and test safety guardrails, including input validation, output filtering, content moderation, and red-teaming practices to strengthen AI systems against misuse. The final module covers content provenance, licensing, and compliance, where learners apply watermarking techniques, implement provenance standards such as Coalition for Content Provenance and Authenticity (C2PA), and evaluate datasets and models for licensing adherence. Regulatory frameworks like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) are also introduced. Through hands-on exercises, learners will build safety layers, implement provenance metadata, and prepare compliance-ready audit documentation. By the end, learners will be able to design open AI applications that prioritize safety, fairness, and accountability

• Design safe and fair open AI applications
• Detect biases in datasets
• Apply quantitative evaluation techniques
• Implement mitigation strategies
• Develop advanced AI safety measures

• Basic familiarity with topic terminology
• Readiness for practical exercises or case studies