# Mitigating Digital Ageism in Skin Lesion Detection with Adversarial Learning
## 4 Results
### PAD20 Dataset  
As shown in Table 2, we directly applied the MA-ADReL framework to the PAD20 dataset with the best \(MI=7.51\). We can see that MA-ADReL without an adversary can achieve the best \(AUCROC=0.986\). Secondly, focusing on the transfer learning performance, we compared different regularization factors \(\gamma\) on the PAD20 dataset. Following the transfer learning algorithm as proposed by [PERSON] et al. (2014), we trained an encoder (with \(\gamma=0.3\)) using data \(X\) on the ISIC2019 dataset, and then we froze the encoder and trained a classifier on top of the pretrained encoder using data \(X^{\prime}\) on the PAD20 dataset. Then, we evaluated the fairness and performance of the classifier.  
Table 3 shows a comparison of the accuracy and fairness results on the PAD20-transfer dataset. We observed that MA-ADReL(\(\gamma=0.3\)) achieved significantly better fairness while maintaining relatively good performance. Our method can achieve MI = 5.51, which is better than the MA-ADReL without any constraints (\(\gamma=0\)). Additionally, our findings demonstrate that learning latent representations can lead to better transfer performance. We also find that, when using the transfer encoder, the MI score is usually lower. This  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **AUROC** & **AUPRC** & **MI** \\ \hline EfficientNet Ensemble & 0.958 (\(\pm\)0.03) & 0.513 (\(\pm\)0.03) & 7.56 (\(\pm\)0.31) \\ MA-ADReL without Adversary & **0.959** (\(\pm\)0.02) & 0.487 (\(\pm\)0.09) & **7.51** (\(\pm\)0.40) \\ LAGFR & 0.859 (\(\pm\)0.03) & 0.387 (\(\pm\)0.07) & 3.36 (\(\pm\)0.21) \\ MA-ADReL (\(\gamma=0.3\)) & **0.942** (\(\pm\)0.03) & **0.476** (\(\pm\)0.08) & **1.89** (\(\pm\)0.28) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance comparison on ISIC2019 dataset. The methods above the dash line are without fairness constraints. The higher the AUROC, the better the prediction performance as represented by the AUPRC, while a lower MI score indicates lower age-related bias.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **AUROC** & **AUPRC** & **MI** \\ \hline EfficientNet Ensemble & 0.961 (\(\pm\)0.02) & 0.676 (\(\pm\)0.04) & 16.88 (\(\pm\)0.42) \\ MA-ADReL without Adversary (\(\gamma=0\)) & **0.966** (\(\pm\)0.02) & **0.684** (\(\pm\)0.09) & **15.84** (\(\pm\)0.39) \\ LAGFR & 0.891 (\(\pm\)0.03) & 0.539 (\(\pm\)0.08) & 8.98(\(\pm\)0.33) \\ MA-ADReL (\(\gamma=0.3\)) & **0.948** (\(\pm\)0.02) & **0.621** (\(\pm\)0.07) & **7.51** (\(\pm\)0.23) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Direct application performance comparison on PAD20 dataset. The methods above the dash line are without fairness constraints. The higher the AUROC, the better the prediction performance as represented by the AUPRC, while a lower MI score indicates lower age-related bias.  
Figure 10: Trade-off between fairness representation (MI) and prediction performance (AUC score).  
suggests that the transferred knowledge from the source domain helps to reduce age-related bias in the target domain. This reduction in bias is desirable as it indicates that our model's performance is less influenced by age-related factors, making it more equitable and applicable to a broader range of patients, regardless of age.