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.
Homepage:

https://www.linkedin.com/learning/learning-xai-explainable-artificial-intelligence-25369961