# A Comprehensive Review and Benchmarking of Fairness-Aware Variants of Machine Learning Models
## 9 Discussion  
This study presents a comprehensive evaluation of various fair machine learning models across multiple diverse datasets--including ADULT, BANK, LAW, MBA, EAP2024, GERMAN, and TITANIC--highlighting persistent trade-offs between predictive accuracy and fairness. The findings reveal no single model universally dominates across all datasets and fairness criteria; instead, model performance seems to be highly context-dependent, influenced by dataset characteristics and fairness goals.  
A central theme is the classical fairness-accuracy trade-off: models enforcing strict fairness guarantees, such as AdaFair, often achieve near-perfect or zero disparity in fairness metrics (statistical parity, true positive rate (TPR), false positive rate (FPR) differences), but sometimes at a modest cost to accuracy, especially on more complex or imbalanced datasets like GERMAN, TITANIC, and EAP2024. However, AdaFair's performance on datasets such as MBA and BANK demonstrates that strict fairness constraints can occasionally be met without significant accuracy loss.  
Gradient boosting-based methods--including FairGBM, Fairboost, and Fairlearn-GB--consistently deliver strong overall performance, balancing high accuracy with moderate to low fairness disparities across most datasets. Their robustness and reliability make them attractive for real-world deployment, especially where both accurate predictions and equitable outcomes are desired. Similarly, Prejudice Remover often ranks among the most accurate models, although it sometimes exhibits higher disparities in FPR, raising cution in sensitive decision-making contexts. NNB-Parity and NNB-DF show inconsistent fairness performance and substantial variability in accuracy, frequently underperforming on fairness metrics like TPR and FPR differences.  
Within the Fairlearn framework, linear and tree-based models--particularly Fairlearn-LR--offer interpretable and stable trade-offs, achieving near-perfect fairness with minimal accuracy loss on some datasets. Nonetheless, their effectiveness can vary with dataset complexity, as observed in datasets like TITANIC and EAP2024, where fairness disparities remain pronounced despite reasonable accuracy. Fair Decision Trees consistently show favorable fairness metrics but suffer from low predictive accuracy, limiting their practical use as standalone models. They may be better suited as components within ensemble approaches rather than primary classifiers.  
Dataset characteristics substantially influence fairness-accuracy dynamics. Balanced datasets such as MBA and LAW allow multiple models to achieve both high accuracy and fairness, while more complex or imbalanced datasets, such as EAP2024 and TITANIC, exacerbate trade-offs, making it difficult to optimize both objectives simultaneously.  
In summary, model selection must be guided by specific application requirements and fairness priorities. For scenarios where fairness is non-negotiable--such as legal, hiring, or lending contexts--models like AdaFair and Fairlearn-LR are promising due to their strict fairness guarantees. When higher accuracy is critical and some fairness disparities are tolerable, models like Fairboost, FairGBM, and Prejudice Remover provide compelling options, provided fairness impacts are carefully monitored.