Bias amplification measures how much more often a target attribute is predicted with a protected attribute than the ground truth value.
| Property | Notes |
|---|---|
| input | Specify the dataset CSV file containing the data to analyze. Output result are shown in the 'Evaluation' tab. Default output_result.csv file acts as input for Bias Amplification computation. |
| target_variable | Specify the name of the column in the input CSV file to use as the target variable. |
| output_variable | Specify the name of the column in the input CSV file to use as the output variable (classification output, by default y' is the output 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). |
| clf_threshold | Specify the optimal classification threshold. Default threshold for interpreting probabilities to class labels is 0.5. |
| fair_threshold | Specify fairness threshold, between -1.0 & 1.0. Based on this value, model outputs whether outcome is "fair" or "unfair". Default value is 0.10. So, all outcomes between -0.1 and 0.1 are "fair". |
| num_samples | Specify the number of samples to compute the Bias Amplification. By default, num_samples is "all", which leads to computation of Bias Amplification for all samples in the input file. |
| output | Specify the name of the CSV file to output the Bias Amplification (BA) result. |
Result of this plugin is saved in the designated 'output' path as CSV file. Information on the columns of CSV file is as follows:
| Fairness Plot | Fairness plot helps in visualization of adherence/deviation of privileged and unprivileged groups with respect to the fairness definition. If the bar plot stretches beyond green zone, that is indication of non-satisfaction of fairness goal for the corresponding sub-group. |
| Bias Amplification | Low Bias Amplification values mean fair model - desirable; high values imply unfair model. |