# A Comprehensive Review and Benchmarking of Fairness-Aware Variants of Machine Learning Models
## 8 Results
### Titanic Dataset Results  
Table 7 and Figure 3 report the performance of various fair machine learning models on the TITANIC dataset. The results underscore a more pronounced trade-off between fairness and accuracy compared to the ADULT and BANK datasets. Fairlearn-GB achieved the highest overall accuracy (\(0.8299\pm 0.0220\)), closely followed by Fairlearn-DT (\(0.8250\pm 0.0183\)) and Prejudice Remover (\(0.8216\pm 0.0125\)). However, despite their strong predictive performance, these models exhibited significant fairness disparities. For example, Fairlearn-GB recorded high statistical parity difference (\(0.7261\pm 0.0834\)) and true positive rate (TPR) difference (\(0.6740\pm 0.1089\)), indicating substantial group-based disparities in classification outcomes. Figure 2: Fairness–accuracy trade-offs of various models on the BANK dataset. Prejudice Remover, while not the most fair, offered a more balanced performance across metrics. It maintained high accuracy while reducing disparities across multiple fairness indicators, such as accuracy difference (\(0.0161\pm 0.0134\)), statistical parity difference (\(0.5725\pm 0.0417\)), and false positive rate (FPR) difference (\(0.1847\pm 0.0993\)). This suggests that it achieves a relatively favorable trade-off between accuracy and fairness. In contrast, AdaFair, which typically excels in fairness across datasets, exhibited anomalously poor fairness performance on the TITANIC dataset. Although its accuracy remained reasonable (\(0.7813\pm 0.0221\)), all fairness metrics were maximized at 1.0, indicating extreme and uniform disparity--a concerning result for a model designed to prioritize fairness. Fairboost delivered solid accuracy (\(0.8127\pm 0.0164\)) and a modest accuracy difference (\(0.0279\pm 0.0166\)), but exhibited some of the worst fairness disparities among the evaluated models. Its statistical parity difference (\(0.8255\pm 0.0333\)) and FPR difference (\(0.6269\pm 0.0811\)) point to highly unequal treatment between demographic groups, undermining its utility in fairness-sensitive applications. \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 & \(0.7813\pm 0.0221\) & \(0.0552\pm 0.0324\) & \(1.0000\pm 0.0000\) & \(1.0000\pm 0.0000\) & \(1.0000\pm 0.0000\) \\ FairGBM & \(0.8090\pm 0.0204\) & \(0.0549\pm 0.0335\) & \(0.5738\pm 0.0801\) & \(0.4563\pm 0.0932\) & \(0.2093\pm 0.1270\) \\ FairLearn-GB & \(0.8299\pm 0.0220\) & \(0.0316\pm 0.0273\) & \(0.7261\pm 0.0834\) & \(0.6740\pm 0.1089\) & \(0.3932\pm 0.1916\) \\ FairLearn-NB & \(0.4608\pm 0.0124\) & \(0.4807\pm 0.0693\) & \(0.0898\pm 0.0400\) & \(0.0736\pm 0.0584\) & \(0.3346\pm 0.0671\) \\ FairLearn-DT & \(0.8250\pm 0.0183\) & \(0.0414\pm 0.0356\) & \(0.6780\pm 0.0383\) & \(0.5792\pm 0.0901\) & \(0.3454\pm 0.1005\) \\ FairLearn-LR & \(0.7810\pm 0.0122\) & \(0.1141\pm 0.0409\) & \(0.3689\pm 0.0672\) & \(0.2232\pm 0.1007\) & \(0.0861\pm 0.0919\) \\ Fair Random Forest & \(0.6970\pm 0.0432\) & \(0.1215\pm 0.0630\) & \(0.1815\pm 0.0693\) & \(0.0626\pm 0.0511\) & \(0.1851\pm 0.0725\) \\ Fair Decision Trees & \(0.3851\pm 0.0052\) & \(0.5448\pm 0.0528\) & \(0.0030\pm 0.0069\) & \(0.0033\pm 0.0098\) & \(0.0047\pm 0.0142\) \\ NNB-Parity & \(0.4810\pm 0.0186\) & \(0.3133\pm 0.1185\) & \(0.1113\pm 0.0391\) & \(0.1056\pm 0.0555\) & \(0.3211\pm 0.0453\) \\ NNB-DF & \(0.4810\pm 0.0186\) & \(0.3133\pm 0.1185\) & \(0.1113\pm 0.0391\) & \(0.1056\pm 0.0555\) & \(0.3211\pm 0.0453\) \\ Fairboost & \(0.8127\pm 0.0164\) & \(0.0279\pm 0.0166\) & \(0.8255\pm 0.0333\) & \(0.7659\pm 0.0559\) & \(0.6269\pm 0.0811\) \\ Prejudice Remover & \(0.8216\pm 0.0125\) & \(0.0161\pm 0.0134\) & \(0.5725\pm 0.0417\) & \(0.4862\pm 0.0406\) & \(0.1847\pm 0.0993\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Fair ML algorithm comparison on Titanic dataset. Figure 3: Fairness–accuracy trade-offs of various models on the TITANIC dataset.