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Related Questions
- What are the key differences between equalized odds and demographic parity fairness metrics in machine learning?
- How do fairness metrics like equalized odds affect the overall accuracy of a model in deployment?
- Can you provide examples of how equalized odds is used to balance the trade-off between accuracy and fairness in real-world applications?
- What are the potential drawbacks of relying solely on equalized odds as a fairness metric in model deployment?
- How do fairness metrics like equalized odds impact the decision-making process in high-stakes applications like healthcare or finance?
- What are some alternative fairness metrics that can be used in conjunction with equalized odds to achieve a better balance between accuracy and fairness?
- Can you discuss the relationship between equalized odds and other fairness metrics, such as disparate impact and predictive rate parity?
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