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Related Questions
- What are the common types of bias that can occur in machine learning models and how can data curation help mitigate them?
- How does data annotation impact the performance of a model in terms of fairness and accuracy?
- Can you provide an example of a real-world scenario where data curation and annotation led to improved model accuracy and reduced bias?
- What techniques can be used to detect and address biases in training data before they affect model performance?
- How does the diversity of the data curation and annotation team impact the quality and fairness of the final model?
- What are some best practices for ensuring that data is representative of the population being modeled?
- Can you explain the relationship between data quality, model accuracy, and bias, and how data curation can improve this relationship?
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