DATA-640 Principles and Practices of Responsible Artificial Intelligence (3)

This course provides a comprehensive exploration of the principles, tools, and practices underlying responsible and reliable artificial intelligence (AI). Students examine key dimensions of responsible AI, including fairness, transparency, privacy, sustainability, human-centered design, and reproducibility. Through a combination of conceptual discussions, applied labs, and case studies, students learn how to detect and mitigate bias, explain and interpret model decisions, design privacy-preserving systems, measure environmental impact, and ensure reproducibility in machine learning research. The course integrates modern tools such as AIF360, SHAP, LIME, CodeCarbon, and MLFlow to prepare students for responsible AI development in real-world settings. Crosslist: DATA-440. Prerequisite: DATA-612 and STAT-615. Recommendation: Experience in Python programming language recommended.