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Mitigating bias in training data for generative AI involves several strategies that can be employed at different stages of the AI development lifecycle: Ensure the data used to train the AI model is representative of diverse groups. This involves collecting data from a wide range of sources and demographics to avoid over-representation or under-representation of Read More
Mitigating bias in training data for generative AI is a multi-faceted challenge that requires a comprehensive approach throughout the data collection, model training, and evaluation phases. Here are some effective strategies: 1. Diverse and Representative Data Collection 2. Data Annotation and Labeling 3. Preprocessing and Data Augmentation 4. Algorithmic Fairness Techniques 5. Model Training and Read More