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When should I apply equal opportunity instead of demographic parity?

Asked on Oct 24, 2025

Answer

Equal opportunity and demographic parity are fairness metrics used to evaluate and mitigate bias in AI systems. Equal opportunity focuses on ensuring that individuals from different groups have equal true positive rates, while demographic parity aims for equal positive prediction rates across groups. The choice between these metrics depends on the context and the specific fairness goals of your application.

Example Concept: Equal opportunity is typically applied when the goal is to ensure that a model's accuracy is consistent across different demographic groups, particularly in scenarios where false negatives have significant consequences (e.g., loan approvals, medical diagnoses). Demographic parity is used when the focus is on ensuring equal access or representation, regardless of group-specific outcomes, which can be important in hiring or admissions processes.

Additional Comment:

Equal opportunity is often preferred in high-stakes applications where fairness in true positive rates is critical.
Demographic parity may be more suitable for applications where equal representation is prioritized over individual outcome accuracy.
Consider the ethical implications and potential trade-offs of each metric in your specific use case.
Use fairness dashboards to visualize and compare the impact of these metrics on your model's performance.

✅ Answered with AI Ethics best practices.

Didn’t find the answer you were looking for?

When should I apply equal opportunity instead of demographic parity?

Asked on Oct 24, 2025

Answer

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