Analyzing and Mitigating Bias in a Generative AI Model focuses on identifying, understanding, and reducing unwanted biases in AI-generated outputs. Generative AI models can reflect biases present in training data, leading to unfair or inaccurate results. This training explains how bias can arise during data collection, model training, and evaluation stages. It also covers techniques for detecting bias using fairness metrics, evaluation datasets, and model auditing methods. You will learn strategies to mitigate bias through data balancing, reweighting, fine-tuning, and algorithmic adjustments. The course also highlights best practices for building fair, ethical, and responsible generative AI systems that ensure equitable outcomes across different user groups.
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