Disparate Impact is a metric to evaluate fairness. It compares the proportion of individuals that receive positive output for two groups: an unprivileged group and a privileged group.
This metric computed as the ratio of rate of favorable outcome for the unprivileged group to that of the privileged group.
| Property | Notes |
|---|---|
| input | Specify the dataset CSV file containing the data to analyze. File name of the current dataset will be set as default. |
| target_variable | Specify the name of the column in the input CSV file to use as the target(label) variable. |
| privileged_variable | Specify the name of the column in the input CSV file to use as the privileged variable (Class in the protected attribute with the majority is called privileged class). |
| unprivileged_variable | Specify the name of the column in the input CSV file to use as the unprivileged variable (Class in the protected attribute with minority is called unprivileged class). |
| num_samples | Specify the number of samples to compute the Disparate Impact, by default num_samples is "all", which means compute the Disparate Impact of all the samples in the input file. |
| output | Specify the name of the CSV file to output the Disparate Impact(DI) result. |
The result of this plugin is saved in the designated 'output' path as CSV file. The information on the columns of CSV file is as follows.
| Fairness Plot | Plot fairness check enables us to look how the fairness between privileged and unprivileged groups with respect to the fairness definition. If the bar plot reaches out of the green zone it means that for this subgroup fairness goal is not satisfied. |
| Disparate Impact | The ideal value of this Disparate Impact metric is 1.0 , value of Disparate Impact < 1.0 implies a higher benefit for the privileged group, and value of Disparate Impact > 1.0 implies a higher benefit for the unprivileged group. |