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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Learning Xai: Explainable Artificial Intelligence (2025)
