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Related Questions
- What are common pitfalls that annotators should be aware of when labeling data to avoid introducing biases?
- How can annotators maintain objectivity when labeling data that may be sensitive or emotionally charged?
- What techniques can annotators use to identify and challenge their own assumptions when labeling data?
- Can you provide examples of how annotators can use active learning to minimize the impact of their own biases on data labels?
- What role does cultural awareness play in reducing bias in data labeling, and how can annotators cultivate it?
- How can annotators ensure that their own linguistic and cultural backgrounds do not influence their labeling decisions?
- What tools or methodologies can annotators use to validate the accuracy and fairness of their labeled data?
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