# A Comprehensive Review and Benchmarking of Fairness-Aware Variants of Machine Learning Models
## 8 Results
### EAP2024 Dataset Results  
Table 9 and Figure 5 showcase the comparative performance of fair machine learning algorithms on the EAP2024 dataset. The results highlight the varying trade-offs between predictive accuracy and fairness across different models.  
Fairboost achieved the highest overall accuracy (\(0.8015\pm 0.0255\)), closely followed by Prejudice Remover (\(0.7978\pm 0.0210\)) and FairGBM (\(0.7896\pm 0.0213\)). Notably, these  
\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 & \(0.7463\pm 0.0248\) & \(0.0383\pm 0.0356\) & \(0.0631\pm 0.0438\) & \(0.0307\pm 0.0244\) & \(0.0644\pm 0.0434\) \\ FairGBM & \(0.7896\pm 0.0213\) & \(0.0484\pm 0.0201\) & \(0.0618\pm 0.0480\) & \(0.0840\pm 0.0591\) & \(0.0523\pm 0.0367\) \\ Fairlearn-GB & \(0.7731\pm 0.0161\) & \(0.0445\pm 0.0411\) & \(0.0933\pm 0.0501\) & \(0.0804\pm 0.0648\) & \(0.0954\pm 0.0582\) \\ Fairlearn-NB & \(0.7873\pm 0.0347\) & \(0.0736\pm 0.0484\) & \(0.0852\pm 0.0608\) & \(0.0890\pm 0.0517\) & \(0.1077\pm 0.0829\) \\ Fairlearn-DT & \(0.7522\pm 0.0277\) & \(0.0550\pm 0.0418\) & \(0.0617\pm 0.0436\) & \(0.1189\pm 0.0554\) & \(0.0735\pm 0.0520\) \\ Fairlearn-LR & \(0.7604\pm 0.0582\) & \(0.0600\pm 0.0526\) & \(0.0787\pm 0.0307\) & \(0.1735\pm 0.0987\) & \(0.0914\pm 0.0495\) \\ Fair Random Forest & \(0.5769\pm 0.0200\) & \(0.1073\pm 0.0708\) & \(0.0400\pm 0.0303\) & \(0.0000\pm 0.0000\) & \(0.0836\pm 0.0528\) \\ Fair Decision Trees & \(0.3791\pm 0.0601\) & \(0.0798\pm 0.0338\) & \(0.0521\pm 0.0313\) & \(0.0623\pm 0.0502\) & \(0.0477\pm 0.0346\) \\ NNB-Parity & \(0.7299\pm 0.0294\) & \(0.0984\pm 0.0574\) & \(0.0788\pm 0.0456\) & \(0.2319\pm 0.0918\) & \(0.0782\pm 0.0486\) \\ NNB-DF & \(0.6522\pm 0.0535\) & \(0.0457\pm 0.0536\) & \(0.0655\pm 0.0585\) & \(0.1505\pm 0.0717\) & \(0.0895\pm 0.0747\) \\ Fairboost & \(0.8015\pm 0.0255\) & \(0.0620\pm 0.0315\) & \(0.0587\pm 0.0578\) & \(0.1068\pm 0.0547\) & \(0.0836\pm 0.0708\) \\ Prejudice Remover & \(0.7978\pm 0.0210\) & \(0.0610\pm 0.0290\) & \(0.1000\pm 0.0642\) & \(0.1083\pm 0.0708\) & \(0.1045\pm 0.0615\) \\ \hline \hline \end{tabular}
\end{table}
Table 9: Fair ML algorithm comparison on EAP2024 dataset.  
top-performing models also maintained relatively low fairness disparities. For instance, Fairboost reported a statistical parity difference of 0.0587 and a TPR difference of 0.1068, suggesting it provides a strong balance between predictive performance and group fairness--making it a compelling candidate for practical deployment.  
AdaFair demonstrated particularly low disparities across all fairness metrics (e.g., TPR difference of 0.0307), while still maintaining competitive accuracy (\(0.7463\pm 0.0248\)). This indicates that substantial fairness improvements can be achieved with only moderate compromise on predictive power, positioning AdaFair as a suitable choice for fairness-critical applications.  
Models such as Fairlearn-NB, Fairlearn-DT, and Fairlearn-LR showed moderate trade-offs between accuracy and fairness. However, Fairlearn-LR exhibited a relatively high TPR disparity (0.1735), suggesting potential group-specific bias or instability in classification performance.  
In contrast, NNB-Parity offered acceptable predictive performance (accuracy of 0.7299), but had the highest TPR disparity (0.2319), indicating significant inequity in positive classification rates across demographic groups. Similarly, while Fair Random Forest reported a perfect TPR difference of 0.0000, its poor accuracy (0.5769) limits its practical applicability, as fairness is achieved at the expense of reliable predictions.  
Fair Decision Trees performed worst across the board, with the lowest accuracy (0.3791) and only marginal fairness benefits. This outcome suggests limited utility for this model in the context of the EAP2024 dataset.  
In summary, Fairboost and FairGBM emerge as the most effective models for this dataset, offering a favorable balance between accuracy and fairness.