Learning Xai: Explainable Artificial Intelligence (2025)
 

This course explores how to identify, evaluate, and mitigate bias in large language models through data curation, mathematical analysis, and model constraints.
Course details
This course focuses on the data-based and mathematical factors contributing to bias in generative AI. Join instructor Jazmia Henry as she explores how data curation, analytical techniques, and post-training constraints can mitigate harmful biases embedded in these models. Through real-world examples and case studies, the course aims to help you understand and apply strategies for creating fairer, more transparent AI systems.
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