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Related Questions
- What are some common methods used to augment data for machine learning models, and how do they impact fairness for underrepresented groups?
- Can data augmentation techniques exacerbate existing biases in machine learning models, and if so, how can this be mitigated?
- How does the quality of augmented data affect the fairness of machine learning models, particularly for underrepresented groups?
- What are some strategies for ensuring that data augmentation techniques do not perpetuate existing biases in machine learning models?
- Can data augmentation techniques be used to improve the representation of underrepresented groups in machine learning models, and if so, how?
- How does the concept of 'proxy datasets' relate to data augmentation and fairness in machine learning models?
- What are some potential risks associated with using data augmentation to improve fairness in machine learning models, particularly for underrepresented groups?
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