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Related Questions
- What are the key steps involved in curating and annotating training data to ensure fairness in machine learning model training?
- How can data curation and annotation help mitigate bias in algorithms and improve model accuracy?
- What best practices can be followed while annotating data to address issues of fairness and objectivity in machine learning?
- Can data curation and annotation methods be used to identify and address potential biases in pre-existing datasets?
- How can fairness audits be integrated into the machine learning development process using curated and annotated data?
- What are some common pitfalls to avoid during data curation and annotation to ensure fairness in model training?
- Can data curation and annotation be used to explain and interpret the decisions of machine learning models, thus promoting transparency and fairness?
- How can data curation and annotation be used to evaluate and compare the fairness of machine learning models across different problem domains?
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