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Related Questions
- How do annotation guidelines account for the potential for annotators to introduce their own biases when labeling negative examples?
- What strategies are used to mitigate the impact of annotator bias on negative example labeling in machine learning data?
- Can you describe a scenario where annotation guidelines failed to address potential biases in negative example labeling, and what were the consequences?
- How do annotation guidelines ensure that negative examples are not biased towards specific demographics or groups?
- What role do human evaluators play in identifying and addressing potential biases in negative example labeling?
- How do annotation guidelines balance the need for diverse and representative negative examples with the risk of introducing bias?
- Are there any specific techniques or tools used to detect and correct biases in negative example labeling during the annotation process?
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