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Related Questions
- What are some key steps to implement data curation processes that promote transparency and accountability in reducing bias in machine learning models?
- How can data curators ensure that their processes are free from human bias and result in more accurate and fair machine learning models?
- What are some common pitfalls to avoid when curating data to reduce bias in machine learning models, and how can they be mitigated?
- What role do data quality metrics play in ensuring transparency and accountability in data curation processes, and how can they be used to identify potential biases?
- How can data curators balance the need for data diversity with the need to avoid amplifying existing biases in machine learning models?
- What are some best practices for documenting and tracking data curation processes to ensure transparency and accountability in reducing bias in machine learning models?
- How can data curators collaborate with stakeholders, including domain experts and model developers, to ensure that data curation processes are effective in reducing bias in machine learning models?
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