Overview

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.

Syllabus

Bias Detection and Mitigation

Learn how to identify bias in both training data and model outputs, measure it with quantitative techniques, and apply strategies to mitigate it. You’ll use evaluation tools on fine-tuned models to see the impact of bias firsthand and practice approaches for reducing it. By the end, you’ll have practical methods to ensure your models are fair, credible, and reliable in real-world applications.

Implementing Safety Guardrails

This module gives you the tools to make AI systems safer and more trustworthy. You’ll design content filtering and moderation layers, apply input validation and output sanitation, and simulate real-world red-teaming scenarios. These skills help you prevent harmful or unsafe model behavior, building the kind of guardrails that organizations expect in production-ready AI systems.

Content Provenance, Licensing, and Compliance

Learn how to prove where AI content comes from and keep your deployments compliant. You’ll apply watermarking and provenance standards like Coalition for Content Provenance and Authenticity (C2PA), practice detecting AI-generated content, and review licensing requirements and attribution rules. You’ll also examine regulatory frameworks like General Data Protection Regulation (GDPR) and Central Consumer Protection Authority (CCPA), giving you the skills to reduce risk and protect credibility in professional AI projects.