Responsible AI Development: Privacy and Safety for Engineers

As artificial intelligence systems become integrated into everyday software, developers must ensure these technologies are secure, unbiased, and respectful of user privacy. Building AI responsibly is no longer optional; it is a core engineering requirement. This written course equips you with the foundational knowledge and practical strategies needed to integrate safety and privacy directly into your development lifecycle. What you'll learn: - Understand the core principles of responsible AI, including fairness, accountability, and transparency. - Implement privacy-preserving techniques to protect sensitive user data during model training and inference. - Mitigate security risks unique to AI, such as adversarial attacks and prompt injection vulnerabilities. - Apply data governance best practices to comply with modern safety standards and regulatory guidelines. - Evaluate AI models for bias and toxic outputs using structured testing methodologies. The course begins with essential terminology and ethical frameworks before guiding you through secure data handling, threat modeling, and safety alignment workflows. Through clear written explanations and practical code scenarios, you will learn how to make safety a first-class citizen in your codebase. This course is designed for software developers, data engineers, and technical product managers who are new to AI ethics and safety. No prior experience in cryptography or advanced machine learning is required. Start building safer, more trustworthy AI applications today.