How does the concept of equal opportunity differ from demographic parity in the context of algorithmic decision-making?

Equal opportunity ensures that equally qualified individuals have the same chance of a positive outcome, regardless of their group, focusing on equal true positive rates. Demographic parity requires that decision outcomes are independent of protected attributes, aiming for equal acceptance rates across groups. Thus, equal opportunity considers qualifications, while demographic parity focuses on equal representation.

In algorithmic decision-making, equal opportunity and demographic parity are two distinct fairness metrics. Equal opportunity ensures that individuals who are equally qualified have the same probability of receiving a positive outcome, regardless of their group membership. This metric focuses on equal true positive rates across different groups, meaning that if two individuals are equally qualified, they should have the same chance of being selected or approved, irrespective of their race, gender, or other protected attributes. This approach aligns with meritocratic principles and allows for outcome differences if they are justified by legitimate factors. On the other hand, demographic parity, also known as statistical parity, requires that decision outcomes are independent of protected attributes. It aims for equal acceptance rates across different demographic groups, meaning that the proportion of positive outcomes (such as hiring or loan approvals) should be the same for all groups. This metric focuses on achieving equal representation and does not consider individual qualifications, which can lead to criticisms such as ignoring legitimate between-group differences in qualifications or requiring "quotas" that may violate meritocratic principles.