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Related Questions
- What are some common biases that human annotators may inadvertently introduce into data labels?
- How can human annotators ensure that their labeling decisions are consistent and unbiased?
- What are some strategies for mitigating the impact of annotator biases on machine learning model performance?
- Can you provide examples of how annotator biases have led to biased machine learning models in the past?
- How can data annotators balance the need for efficient labeling with the need to ensure high-quality, unbiased data?
- What role do data augmentation techniques play in reducing the impact of annotator biases on machine learning models?
- How can organizations develop and implement effective training programs for human annotators to prevent perpetuating biases in machine learning models?
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