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Related Questions
- What are some common biases that human annotators may unintentionally introduce into labeled data?
- How can human annotators ensure that their labeled data accurately represents the demographics of the population being modeled?
- What strategies can human annotators use to minimize annotation noise and improve data quality?
- How can human annotators use active learning to select the most informative samples for labeling and reduce annotation costs?
- What role do human annotators play in ensuring that labeled data is free from cultural and linguistic biases?
- How can human annotators use data augmentation techniques to increase the diversity of their labeled data?
- What are some best practices for human annotators to follow when labeling data for machine learning models that require high accuracy and generalizability?
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