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Related Questions
- What are the key differences between demographic parity and equalized odds metrics in evaluating disparate impact in machine learning models?
- Can you provide an example of a situation where demographic parity would be more suitable than equalized odds, and vice versa?
- How do demographic parity and equalized odds metrics address the issue of confounding variables in machine learning models?
- What are some common challenges in implementing demographic parity and equalized odds metrics in real-world machine learning applications?
- Can you explain the concept of 'disparate impact' in machine learning and how it relates to demographic parity and equalized odds metrics?
- How do demographic parity and equalized odds metrics compare to other fairness metrics, such as equal opportunity and predictive rate parity?
- What are some potential consequences of ignoring disparate impact in machine learning models, and how can demographic parity and equalized odds metrics help mitigate these consequences?
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