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Related Questions
- What are the key differences between equalized odds metric and demographic parity metric in assessing fairness?
- Can you provide examples to illustrate the limitations of demographic parity metric?
- How does equalized odds metric account for both the accuracy and fairness of machine learning models?
- What are the practical implications of using equalized odds metric versus demographic parity metric in real-world applications?
- Are there any trade-offs or challenges associated with using equalized odds metric in assessment of fairness?
- Can you discuss the relationship between equalized odds metric and other fairness metrics, such as predictive rate parity?
- How does the equalized odds metric consider the specific characteristics of different protected groups in assessing fairness?
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