Responsible AI: Designing Fair and Unbiased Systems

As artificial intelligence increasingly influences critical decisions in hiring, lending, and healthcare, ensuring fairness and preventing bias has become a fundamental engineering requirement. This course provides a clear, conceptual foundation for understanding and implementing responsible AI practices in modern software. You will transition from a general understanding of ethics to being able to practically identify bias, apply quantitative fairness metrics, and implement mitigation strategies in your data pipelines. What you'll learn: Understand the core definitions of fairness, equity, and algorithmic bias; Identify common sources of bias in data collection and feature engineering; Apply mathematical fairness metrics such as demographic parity and equalized odds to datasets; Mitigate bias using pre-processing, in-processing, and post-processing techniques; Evaluate modern large language models for toxic outputs and representation harms; Implement responsible AI governance frameworks within your development lifecycle. The course begins with essential terminology and the philosophical foundations of AI ethics before guiding you through text-based scenarios and written code walkthroughs to measure and correct bias in real-world datasets. This course is designed for aspiring data scientists, product managers, and software developers who are new to AI ethics and want to build socially responsible technology, with no prior advanced mathematics required. Start reading today to build AI systems that everyone can trust.