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Related Questions
- What are the potential consequences of data bias on machine learning models, and how can it lead to unfair outcomes?
- How can data bias be introduced during the annotation process, and what are some common sources of bias?
- What are some strategies for mitigating data bias during the annotation process, such as active learning and human evaluation?
- Can you explain the concept of ' annotation drift' and how it can affect the accuracy of machine learning models?
- How can data bias be detected and measured, and what are some common metrics used to evaluate bias?
- What role do annotators play in introducing data bias, and how can their biases be accounted for in the annotation process?
- What are some best practices for ensuring diversity and representation in the annotation process to mitigate data bias?
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