Equalized Odds is a fairness criterion used in classification problems to ensure that a model's predictions are independent of a sensitive attribute, like race or gender, while maintaining performance metrics across all groups. It requires that the true positive rate and the false positive rate remain equal for each subgroup, reflecting a balanced misclassification cost across groups.

Equalized Odds

fairness criterion

classification problems

model's predictions

sensitive attribute

Race is a socially constructed category used to group people based on physical characteristics such as skin color, but it has no biological basis. It plays a significant role in shaping social dynamics, influencing identity, and perpetuating systemic inequalities.

race

Gender is a complex social construct that encompasses a range of identities and expressions beyond the binary framework of male and female. It is distinct from biological sex and is influenced by cultural, social, and individual factors, shaping personal identity and societal roles.

gender

Performance metrics are quantifiable measures used to evaluate the success of an organization, employee, or process in meeting predefined objectives. They provide a framework for assessing efficiency, effectiveness, and progress, enabling data-driven decision-making and continuous improvement.

performance metrics

The True Positive Rate (TPR), also known as sensitivity or recall, measures the proportion of actual positives that are correctly identified by a binary classification model. It is crucial for evaluating the performance of models in contexts where missing positive instances can have severe consequences, such as medical diagnoses or fraud detection.

true positive rate

The false positive rate is the probability of incorrectly rejecting the null hypothesis when it is true, indicating the proportion of negative instances that are mistakenly classified as positive. It is a critical metric for evaluating the performance of a binary classification model, especially in scenarios where the cost of false positives is high, such as in medical testing or fraud detection.

false positive rate

A subgroup is a subset of a group that itself forms a group under the same operation as the original group. It must satisfy the group axioms: closure, associativity, identity, and invertibility within the subset.

subgroup

balanced misclassification cost

Misclassification cost refers to the penalty incurred when a predictive model incorrectly labels an instance, which can vary depending on the type and impact of the error. Understanding and minimizing these costs is crucial in developing models that are not only accurate but also aligned with the real-world consequences of their predictions.

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