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Related Questions
- What are the primary challenges faced by human annotators in large-scale machine learning projects?
- How does annotator bias impact the accuracy and reliability of machine learning models?
- What are some strategies to mitigate annotator fatigue and maintain data quality?
- Can you explain the concept of 'gold standard' data and its importance in human-in-the-loop approaches?
- How do human annotators handle ambiguous or uncertain data, and what are the implications for model performance?
- What are some best practices for managing and monitoring annotator performance to ensure data quality?
- Can you discuss the trade-offs between human annotator effort and model accuracy in human-in-the-loop approaches?
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