# A Comprehensive Review and Benchmarking of Fairness-Aware Variants of Machine Learning Models
## 8 Results
### MBA Dataset Results  
Table 10, along with Figure 6, provides a comparative evaluation of fair machine learning algorithms on the MBA dataset. The analysis highlights meaningful differences in the trade-offs between predictive accuracy and fairness across the models. Figure 5: Fairness–accuracy trade-offs of various models on the EAP2024 dataset. The highest-performing model in terms of accuracy was Fairlearn-LR (0.8529 \(\pm\) 0.0012), closely followed by AdaFair (0.8524 \(\pm\) 0.0000) and Fairlearn-NB (0.8487 \(\pm\) 0.0071). Remarkably, AdaFair achieved perfect fairness across all evaluated metrics, with zero values for statistical parity difference, true positive rate (TPR) difference, and false positive rate (FPR) difference. This result suggests that strict fairness constraints or post-processing techniques may have been applied, allowing the model to enforce equitable treatment without sacrificing accuracy. Fairlearn-LR also demonstrated exceptional fairness performance, reporting minimal disparity scores--statistical parity difference (0.0020), TPR difference (0.0124), and FPR difference (0.0014)--thereby confirming that, in this context, high predictive performance can coexist with strong fairness guarantees. Other models such as Fairboost and Prejudice Remover maintained solid predictive performance (approximately 0.84-0.85 in accuracy), but with slightly higher fairness disparities. For example, Fairboost exhibited a statistical parity difference of 0.0544, indicating some trade-off between fairness and accuracy. \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 & 0.8524 \(\pm\) 0.0000 & 0.0967 \(\pm\) 0.0103 & 0.0000 \(\pm\) 0.0000 & 0.0000 \(\pm\) 0.0000 & 0.0000 \(\pm\) 0.0000 \\ FairGBM & 0.8345 \(\pm\) 0.0061 & 0.0812 \(\pm\) 0.0073 & 0.0115 \(\pm\) 0.0106 & 0.0547 \(\pm\) 0.0526 & 0.0091 \(\pm\) 0.0062 \\ Fairlearn-GB & 0.8440 \(\pm\) 0.0058 & 0.0840 \(\pm\) 0.0124 & 0.0115 \(\pm\) 0.0098 & 0.0383 \(\pm\) 0.0184 & 0.0119 \(\pm\) 0.0107 \\ Fairlearn-NB & 0.8487 \(\pm\) 0.0071 & 0.0961 \(\pm\) 0.0093 & 0.0026 \(\pm\) 0.0031 & 0.0156 \(\pm\) 0.0265 & 0.0023 \(\pm\) 0.0025 \\ Fairlearn-DT & 0.7966 \(\pm\) 0.0074 & 0.0493 \(\pm\) 0.0155 & 0.0135 \(\pm\) 0.0105 & 0.1011 \(\pm\) 0.0466 & 0.0202 \(\pm\) 0.0167 \\ Fairlearn-LR & 0.8529 \(\pm\) 0.0012 & 0.0952 \(\pm\) 0.0116 & 0.0020 \(\pm\) 0.0037 & 0.0124 \(\pm\) 0.0162 & 0.0014 \(\pm\) 0.0021 \\ Fair Random Forest & 0.7808 \(\pm\) 0.0118 & 0.0280 \(\pm\) 0.0150 & 0.0212 \(\pm\) 0.0128 & 0.2370 \(\pm\) 0.0435 & 0.0317 \(\pm\) 0.0127 \\ Fair Decision Trees & 0.4852 \(\pm\) 0.0320 & 0.0424 \(\pm\) 0.0250 & 0.0209 \(\pm\) 0.0132 & 0.1129 \(\pm\) 0.0501 & 0.0311 \(\pm\) 0.0152 \\ NNB-Parity & 0.8090 \(\pm\) 0.0061 & 0.0614 \(\pm\) 0.0085 & 0.0146 \(\pm\) 0.0119 & 0.1646 \(\pm\) 0.0307 & 0.0166 \(\pm\) 0.0098 \\ NNB-DF & 0.8056 \(\pm\) 0.0073 & 0.0519 \(\pm\) 0.0119 & 0.0156 \(\pm\) 0.0125 & 0.1697 \(\pm\) 0.0322 & 0.0214 \(\pm\) 0.0122 \\ Fairboost & 0.8469 \(\pm\) 0.0041 & 0.1024 \(\pm\) 0.0121 & 0.0544 \(\pm\) 0.0125 & 0.0811 \(\pm\) 0.0458 & 0.0397 \(\pm\) 0.0072 \\ Prejudice Remover & 0.8441 \(\pm\) 0.0055 & 0.0957 \(\pm\) 0.0114 & 0.0231 \(\pm\) 0.0100 & 0.0321 \(\pm\) 0.0280 & 0.0172 \(\pm\) 0.0055 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Fair ML algorithm comparison on MBA dataset. Figure 6: Fairness-accuracy trade-offs of various models on the MBA dataset. Models including Fair Random Forest and the NNB variants (NNB-Parity and NNB-DF) achieved moderate accuracy levels (around 0.81) but reported considerably higher fairness disparities. Notably, both NNB-based models had TPR differences in the range of 0.165-0.170, suggesting inconsistent classification behavior across demographic subgroups and raising concerns about equitable model behavior. The lowest-performing models in terms of both accuracy and fairness were Fair Decision Trees (accuracy: 0.4852) and Fair Random Forest (accuracy: 0.7808).