# A Comprehensive Review and Benchmarking of Fairness-Aware Variants of Machine Learning Models
## 8 Results
### BANK Dataset Results  
Table 6, along with Figure 2, presents a comparative analysis of fair machine learning algorithms on the BANK dataset. The results reveal several key insights:  
FairGBM and Fairlearn-GB achieved the highest overall accuracies (0.9177 \(\pm\) 0.0018 and 0.9178 \(\pm\) 0.0018, respectively), while also maintaining low disparities across multiple fairness metrics. For example, Fairlearn-GB reported a statistical parity difference of just 0.0120 \(\pm\) 0.0065, along modest true positive rate (TPR) and false positive rate (FPR) differences. These results suggest that both gradient boosting variants deliver strong predictive performance while ensuring relatively equitable treatment across demographic groups. AdaFair, consistent with its performance on the ADULT dataset, achieved perfect fairness with all fairness metrics equal to zero. Unlike in the previous dataset, however, its accuracy on the BANK dataset was also strong (0.8874 \(\pm\) 0.0000), making it a highly competitive candidate for fairness-sensitive applications with minimal compromise in performance. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Accuracy** & **Acc. Diff** & **Stat. Parity Diff** & **TPR Diff** & **FPR Diff** \\ \hline AdaFair & \(0.8874\pm 0.0000\) & \(0.0160\pm 0.0042\) & \(0.0000\pm 0.0000\) & \(0.0000\pm 0.0000\) & \(0.0000\pm 0.0000\) \\ FairGBM & \(0.9177\pm 0.0018\) & \(0.0123\pm 0.0024\) & \(0.0108\pm 0.0045\) & \(0.0233\pm 0.0162\) & \(0.0053\pm 0.0028\) \\ Fairlearn-GB & \(0.9178\pm 0.0018\) & \(0.0110\pm 0.0014\) & \(0.0120\pm 0.0065\) & \(0.0220\pm 0.0216\) & \(0.0045\pm 0.0036\) \\ Fairlearn-NB & \(0.8214\pm 0.0098\) & \(0.0163\pm 0.0074\) & \(0.0127\pm 0.0075\) & \(0.0347\pm 0.0146\) & \(0.0148\pm 0.0093\) \\ Fairlearn-DT & \(0.8894\pm 0.0022\) & \(0.0121\pm 0.0037\) & \(0.0095\pm 0.0036\) & \(0.0240\pm 0.0147\) & \(0.0047\pm 0.0026\) \\ Fairlearn-LR & \(0.9122\pm 0.0014\) & \(0.0140\pm 0.0028\) & \(0.0104\pm 0.0058\) & \(0.0267\pm 0.0141\) & \(0.0052\pm 0.0033\) \\ Fair Random Forest & \(0.8592\pm 0.0114\) & \(0.0158\pm 0.0051\) & \(0.0197\pm 0.0078\) & \(0.0250\pm 0.0162\) & \(0.0131\pm 0.0071\) \\ Fair Decision Trees & \(0.5605\pm 0.0344\) & \(0.0153\pm 0.0091\) & \(0.0078\pm 0.0040\) & \(0.0144\pm 0.0062\) & \(0.0110\pm 0.0083\) \\ NNB-Parity & \(0.7780\pm 0.0053\) & \(0.0352\pm 0.0061\) & \(0.0413\pm 0.0068\) & \(0.0174\pm 0.0123\) & \(0.0377\pm 0.0078\) \\ NNB-DF & \(0.5624\pm 0.1693\) & \(0.0689\pm 0.0244\) & \(0.0893\pm 0.0317\) & \(0.0475\pm 0.0131\) & \(0.0891\pm 0.0351\) \\ Fairboost & \(0.8813\pm 0.0068\) & \(0.0020\pm 0.0012\) & \(0.0152\pm 0.0074\) & \(0.0825\pm 0.0332\) & \(0.0160\pm 0.0054\) \\ Prejudice Remover & \(0.9116\pm 0.0010\) & \(0.0142\pm 0.0028\) & \(0.0082\pm 0.0054\) & \(0.0269\pm 0.0173\) & \(0.0044\pm 0.0027\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Fair ML algorithm comparison on BANK dataset. Fairboost demonstrated competitive accuracy (\(0.8813\pm 0.0068\)) and an exceptionally low accuracy difference (\(0.0020\pm 0.0012\)). However, its relatively high TPR difference (\(0.0825\pm 0.0332\)) indicates potential imbalances in positive classification rates across groups. Both Prejudice Remover and Fairlearn-LR also performed well, achieving accuracies above 0.91 and fairness metrics comparable to the top-performing models. These results underscore that linear and regularized models can attain both fairness and accuracy when properly calibrated. In contrast, NNB-DF and NNB-Parity exhibited significantly lower accuracies (\(0.5624\pm 0.1693\) and \(0.7780\pm 0.0053\), respectively) and higher disparities in key fairness metrics, particularly in statistical parity and FPR. These findings indicate that, although neural network-based approaches offer flexibility, their fairness performance in this context was less consistent and more variable. Fair Decision Trees recorded the lowest accuracy (\(0.5605\pm 0.0344\)) but maintained fairness metrics within acceptable bounds.