Responsible AI for Developers: Privacy and Safety

As artificial intelligence becomes central to software development, building systems that respect user privacy and ensure safety is no longer optional—it is a core engineering requirement. This text-based course guides you through the essential principles of responsible AI, helping you navigate the ethical and technical challenges of modern AI deployment. You will transition from writing basic AI implementations to architecting secure, compliant, and robust AI applications. Through clear written explanations and practical code scenarios, you will learn how to identify vulnerabilities, protect sensitive user data, and mitigate risks in machine learning pipelines. What you'll learn: - Understand foundational principles of responsible AI, algorithmic bias, and ethical decision-making. - Implement privacy-preserving techniques such as data anonymization and differential privacy basics. - Protect AI models from security threats, including prompt injection and data poisoning. - Design safety guardrails and moderation layers for large language model outputs. - Evaluate AI systems for fairness, transparency, and compliance with modern data regulations. - Configure secure data handling workflows to prevent sensitive data leakage. The course begins with core terminology and the ethical foundations of AI safety before moving into practical implementation strategies for securing data pipelines and model outputs. You will read through real-world development scenarios and practice applying safety frameworks to modern AI workflows. This course is designed for software developers, data practitioners, and technology enthusiasts who are new to AI safety and want to build trustworthy applications. No advanced background in machine learning or ethics is required. Start reading today to build AI applications that your users can trust.