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Related Questions
- Can annotators' biases affect the quality of labeled data for machine learning models?
- How do annotators' assumptions about language nuances impact the labeling of negative examples in machine learning datasets?
- Can annotators' prior knowledge and experience influence their labeling of negative examples?
- What strategies can be used to minimize the impact of annotators' assumptions on the labeling of negative examples?
- How can machine learning models be designed to account for annotators' biases and assumptions?
- Can the use of multiple annotators help to reduce the impact of individual annotators' assumptions on the labeling of negative examples?
- What are the potential consequences of ignoring annotators' assumptions when labeling negative examples in machine learning datasets?
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