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Related Questions
- What are the key differences between statistical parity and equalized odds in terms of their definitions and applications?
- Can you explain how statistical parity focuses on the ratio of true positives to false positives across different groups?
- How does equalized odds, on the other hand, consider both true positives and false positives, as well as false negatives and true negatives?
- What are some real-world examples of scenarios where statistical parity might be more suitable than equalized odds, and vice versa?
- In what ways do statistical parity and equalized odds metrics complement each other in evaluating group fairness in machine learning models?
- How do statistical parity and equalized odds metrics handle cases where the base rates of the sensitive attribute vary across different groups?
- Can you provide examples of machine learning algorithms and datasets where statistical parity and equalized odds metrics have been successfully applied?
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