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Related Questions
- How can data annotators' personal experiences influence the quality of labeled data?
- In what ways can human annotators' biases impact the accuracy of machine learning models?
- Can annotators' prior knowledge and experiences affect the consistency of labeled data?
- How do individual annotators' perspectives and backgrounds shape the annotation process?
- What are some strategies for reducing the impact of annotators' biases on labeled data?
- Can the use of diverse annotators help mitigate the effects of personal experiences and biases?
- How can data quality be ensured when relying on human annotators to label large datasets?
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