The fairness concept that ensures equal true positive rates across groups is called...

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Equal Opportunity is the fairness concept that ensures equal true positive rates across groups. It looks at how well a classifier identifies actual positives, defined as the true positive rate, P(predicted positive | actual positive). By requiring this rate to be the same across groups, it ensures that people who are truly positive have an equal chance of being correctly identified, even when base rates differ between groups. This approach can allow different decision thresholds by group to maintain equal TPR, balancing fairness with predictive performance. Other options don’t describe this target: Mathematical Impossibility isn’t a fairness criterion, Problem Specification is about how the task is defined, and Transparency concerns how interpretable the model is rather than equality of true positive rates.