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Related Questions
- What are some strategies for collecting data from diverse sources to increase representation in the training dataset?
- How can data curation techniques such as data cleaning, data transformation, and data normalization be used to ensure data quality and diversity?
- What are some methods for identifying and mitigating bias in the training data to ensure that the model is fair and representative?
- How can active learning and human-in-the-loop approaches be used to select the most informative and diverse data points for the model to learn from?
- What are some techniques for evaluating the diversity and representativeness of the training data, such as metrics for measuring demographic parity and equalized odds?
- How can data augmentation techniques be used to increase the diversity of the training data without collecting additional data?
- What are some strategies for using transfer learning to leverage pre-trained models and adapt them to new, diverse datasets to ensure representation of the target population?
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