Responsible AI for Developers: Privacy and Safety

Building artificial intelligence requires more than just training models; it demands a commitment to ethics, user privacy, and system safety. This course provides developers with a clear, structured pathway to understanding and implementing responsible AI practices in modern software applications. You will transition from simply writing model code to architecting secure, transparent, and fair AI systems, gaining the skills to identify vulnerabilities, protect sensitive user data, and mitigate risks. What you'll learn: Learn the core principles of responsible AI, including fairness, accountability, and transparency; Implement privacy-preserving techniques such as differential privacy and secure data handling; Configure safety guardrails to prevent harmful model outputs and mitigate prompt injection attacks; Analyze AI models for bias and apply mitigation strategies during training and deployment; Apply modern security standards, including OWASP safety guidelines for large language models; Design transparent system workflows that respect user consent and data sovereignty. The course begins with foundational definitions of AI ethics and privacy regulations, then guides you through practical techniques for securing data, testing models for safety, and deploying resilient AI applications. This course is designed for software developers, data scientists, and technical product managers who are new to AI safety and want to build trustworthy applications, with no advanced machine learning background required. Start reading today to build AI systems that your users can trust.