# A Comprehensive Review and Benchmarking of Fairness-Aware Variants of Machine Learning Models
## 8 Results  
### Adult Dataset Results  
Table 5 and Figure 1 present a comparative evaluation of several fair machine learning algorithms on the ADULT dataset. The analysis yields several noteworthy observations:  
Among all models, FairGBM achieved the highest accuracy (\(0.8740\pm 0.0019\)), followed closely by Prejudice Remover (\(0.8504\pm 0.0019\)) and Fairboost (\(0.8475\pm 0.0159\)). However, these models exhibited notable variations in fairness performance. In particular, both  
Figure 1: Fairness-accuracy trade-offs of various models on the Adult dataset. Each subplot visualizes the relationships between accuracy, statistical parity difference, equal opportunity difference (TPR difference), and false positive rate (FPR) difference across multiple fairness-aware algorithms. The ideal models are those in the lower-right areas (high accuracy, low fairness disparity). \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 & \(0.7607\pm 0.0000\) & \(0.1913\pm 0.0046\) & \(0.0000\pm 0.0000\) & \(0.0000\pm 0.0000\) & \(0.0000\pm 0.0000\) \\ FairGBM & \(0.8740\pm 0.0019\) & \(0.0950\pm 0.0050\) & \(0.1730\pm 0.0056\) & \(0.0661\pm 0.0221\) & \(0.0605\pm 0.0039\) \\ Fairlearn-GB & \(0.8662\pm 0.0021\) & \(0.0898\pm 0.0060\) & \(0.1174\pm 0.0047\) & \(0.0232\pm 0.0165\) & \(0.0207\pm 0.0037\) \\ Fairlearn-NB & \(0.6185\pm 0.0140\) & \(0.0179\pm 0.0149\) & \(0.0389\pm 0.0141\) & \(0.0150\pm 0.0107\) & \(0.0128\pm 0.0111\) \\ Fairlearn-DT & \(0.8186\pm 0.0020\) & \(0.1223\pm 0.0045\) & \(0.1848\pm 0.0087\) & \(0.0641\pm 0.0193\) & \(0.0988\pm 0.0059\) \\ Fairlearn-LR & \(0.8431\pm 0.0018\) & \(0.0904\pm 0.0073\) & \(0.1042\pm 0.0065\) & \(0.0286\pm 0.0141\) & \(0.0168\pm 0.0043\) \\ Fair Random Forest & \(0.8351\pm 0.0081\) & \(0.0827\pm 0.0099\) & \(0.1924\pm 0.0179\) & \(0.0762\pm 0.0233\) & \(0.0825\pm 0.0148\) \\ Fair Decision Trees & \(0.2403\pm 0.0018\) & \(0.1896\pm 0.0057\) & \(0.0019\pm 0.0033\) & \(0.0015\pm 0.0029\) & \(0.0019\pm 0.0031\) \\ NNB-Parity & \(0.5863\pm 0.0213\) & \(0.0378\pm 0.0165\) & \(0.2153\pm 0.0160\) & \(0.0514\pm 0.0110\) & \(0.1601\pm 0.0143\) \\ NNB-DF & \(0.5837\pm 0.0214\) & \(0.0362\pm 0.0162\) & \(0.2135\pm 0.0159\) & \(0.0499\pm 0.0119\) & \(0.1589\pm 0.0139\) \\ Fairboost & \(0.8475\pm 0.0159\) & \(0.1037\pm 0.0209\) & \(0.1713\pm 0.0203\) & \(0.1017\pm 0.0187\) & \(0.0697\pm 0.0190\) \\ Prejudice Remover & \(0.8504\pm 0.0019\) & \(0.1144\pm 0.0038\) & \(0.1403\pm 0.0048\) & \(0.0551\pm 0.0132\) & \(0.0502\pm 0.0034\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Fair ML algorithm comparison on ADULT dataset. FairGBM and Fairboost reported relatively high statistical parity differences (0.1730 and 0.1713, respectively), suggesting a tendency toward disparate outcomes across demographic groups despite their high predictive performance. AdaFair achieved perfect fairness across all evaluated metrics, with all fairness values equal to zero. However, this came at the expense of lower predictive accuracy (\(0.7607\pm 0.0000\)), indicating a clear trade-off between fairness and accuracy in this case. Models such as Fairlearn-NB and NNB-DF offered more favorable fairness metrics compared to the top-performing models in terms of accuracy. For instance, Fairlearn-NB reported low TPR and FPR differences (0.0150 \(\pm\) 0.0107 and 0.0128 \(\pm\) 0.0111, respectively). However, these improvements in fairness were accompanied by substantially lower accuracies, generally ranging from 0.58 to 0.62, limiting their practical applicability. Fair Decision Trees achieved very low disparities in fairness metrics but suffered from the lowest accuracy (0.2403 \(\pm\) 0.0018), rendering them ineffective for real-world deployment despite their strong fairness characteristics. Overall, Fairlearn-GB, Fairlearn-LR, and Prejudice Remover demonstrated a more favorable trade-off between fairness and accuracy.