## 3 Results
\begin{table}
\begin{tabular}{l l c c c c} \hline \hline
**Models** & **Groups** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline ResNet18 & White & \(70.62\pm 2.83\) & \(0.76\pm 0.03\) & \(0.71\pm 0.03\) & \(0.73\pm 0.03\) \\  & non-White & \(55.27\pm 6.33\) & \(0.66\pm 0.10\) & \(0.55\pm 0.06\) & \(0.46\pm 0.08\) \\ ResNet50 & White & \(67.37\pm 1.75\) & \(0.77\pm 0.02\) & \(0.67\pm 0.02\) & \(0.71\pm 0.02\) \\  & non-White & \(61.95\pm 4.31\) & \(0.74\pm 0.04\) & \(0.62\pm 0.04\) & \(0.57\pm 0.05\) \\ Wide ResNet101 & White & \(58.31\pm 1.66\) & \(0.76\pm 0.02\) & \(0.58\pm 0.02\) & \(0.64\pm 0.02\) \\  & non-White & \(56.72\pm 4.24\) & \(0.57\pm 0.04\) & \(0.57\pm 0.04\) & \(0.57\pm 0.04\) \\ ResNet152 & White & \(62.21\pm 1.46\) & \(0.75\pm 0.01\) & \(0.62\pm 0.01\) & \(0.67\pm 0.01\) \\  & non-White & \(55.96\pm 3.59\) & \(0.57\pm 0.04\) & \(0.56\pm 0.04\) & \(0.55\pm 0.04\) \\ VGG & White & \(65.84\pm 1.34\) & \(0.76\pm 0.01\) & \(0.66\pm 0.01\) & \(0.70\pm 0.01\) \\  & non-White & \(57.16\pm 2.55\) & \(0.59\pm 0.03\) & \(0.57\pm 0.03\) & \(0.56\pm 0.03\) \\ EfficientNet & White & \(67.34\pm 1.13\) & \(0.76\pm 0.01\) & \(0.67\pm 0.01\) & \(0.71\pm 0.01\) \\  & non-White & \(56.38\pm 3.23\) & \(0.58\pm 0.03\) & \(0.56\pm 0.03\) & \(0.55\pm 0.03\) \\ ConvNeXt & White & \(66.91\pm 0.84\) & \(0.77\pm 0.01\) & \(0.67\pm 0.01\) & \(0.71\pm 0.01\) \\  & non-White & \(57.29\pm 1.94\) & \(0.60\pm 0.02\) & \(0.57\pm 0.02\) & \(0.55\pm 0.02\) \\ RegNet & White & \(67.58\pm 0.97\) & \(0.76\pm 0.01\) & \(0.68\pm 0.01\) & \(0.71\pm 0.01\) \\  & non-White & \(56.09\pm 1.96\) & \(0.59\pm 0.02\) & \(0.56\pm 0.02\) & \(0.53\pm 0.02\) \\ Ensemble & White & \(62.88\pm 8.76\) & \(0.67\pm 0.09\) & \(0.63\pm 0.09\) & \(0.62\pm 0.09\) \\  & non-White & \(29.56\pm 13.26\) & \(0.79\pm 0.34\) & \(0.30\pm 0.13\) & \(0.34\pm 0.18\) \\ Slide Level & White & \(65.44\pm 8.18\) & \(0.68\pm 0.08\) & \(0.65\pm 0.08\) & \(0.65\pm 0.08\) \\  & non-White & \(55.11\pm 16.47\) & \(0.77\pm 0.24\) & \(0.55\pm 0.16\) & \(0.63\pm 0.17\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of performance metrics across models for White and non-White groups prior to fairness adjustments. Figure 4: Comparative analyses across eight machine learning models, demonstrating the impact of fairness adjustments on the FPR, TPR, and loss. **(a)** FPR comparisons, **(b)** TPR comparisons, **(c)** loss comparisons. We do not observe consistent trends. Table 6 provides an updated comparison of performance metrics for several models for the multi-class setting. The analysis was conducted across White and non-White groups for three different labels. We did not observe consistent discrepancies in performance between the White and non-White groups in the multi-class formulation, but we are hesitant to draw any conclusions from this due to the low overall performance of the models in the multi-class setting. Figure 5 and Table 7 present a comparative analysis of the performance metrics for the deep learning models before and after fairness adjustments in the multi-class setting. We once again do not observe any improvements in performance after the fairness adjustments, but we are hesitant to draw any conclusions from the multi-class results due to the low overall baseline performance.