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Related Questions
- What are the common data curation and annotation methods used to identify biases in datasets?
- Can active learning techniques be employed to select the most informative samples for annotation and reduce bias?
- How do data preprocessing techniques, such as normalization and feature scaling, impact bias in machine learning models?
- What role do human annotators play in identifying and mitigating bias in datasets, and how can their performance be evaluated?
- Can transfer learning and domain adaptation be used to reduce bias in pre-trained models when adapting to new datasets?
- What are some common pitfalls to avoid when using data curation and annotation methods to address bias in datasets?
- Can ensemble methods, such as bagging and boosting, be used to combine models and reduce bias in predictions?
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