# Mitigating Digital Ageism in Skin Lesion Detection with Adversarial Learning
## 4 Results
### Evaluation of the Deep Fusion VAE Model  
To evaluate the effectiveness of capturing more fine-grained features with the deep fusion VAE model, as shown in Figure 3, we conducted an ablation study using the ISIC2019 dataset. Firstly, for the ISIC2019 dataset, we compared different backbones for the VAE's encoder and decoder, as utilized in [PERSON] et al. (2018). To compare all models fairly, we used the same size of input. If we only use higher-resolution inputs, i.e., \(1024\times 1024\), this would be equal to only using the left branch in Figure 3. On the other hand, if we only use lower-resolution inputs, i.e., \(28\times 28\), this would be equal to only using the right branch in Figure 3. For the backbones, we consider SE-ResNeXt-101 ([PERSON] et al., 2017) and ResNeSt-101 ([PERSON] et al., 2017), which are some representative backbones from (K  
interplay between bias mitigation and performance retention. Nonetheless, our work is not without its limitations, which we acknowledge and discuss below.  
One of the primary limitations of our study is the reliance on existing datasets, which, despite their utility, are not representative of the full demographic spectrum. The under-representation of certain age groups, particularly the elderly, in the ISIC2019 dataset poses a challenge in training and validating models that are equitable across all ages. This limitation is not unique to our study but is a common constraint in medical imaging research, which we hope that future data collection efforts will address. Future research could focus on training fair (and unfair) models on data from various younger age groups and testing them on various older age groups to develop an understanding of their performance. These experiments could establish the generalization capacities and limitations of models trained on younger patients' skin lesions to detect older patients' skin lesions.  
Additionally, while our method has been effective in reducing age-related bias, it is possible that other forms of bias, such as those related to ethnicity, sex, or gender, may not be as effectively mitigated. Other types of bias, beyond age and demographics, may also be present in datasets due to selection bias, negative set bias, and category or labeling bias [1,2]. Therefore, it is very difficult to obtain a completely unbiased dataset in practical settings, especially those using human participants and different data collection protocols, instruments, and labeling strategies. Hypothetically, if a completely unbiased dataset existed, MA-ADRel would still perform comparably, with an additional functionality to manage future (age-related) bias. Our focus on age as a continuous attribute was intentional given the spotlight on digital ageism; however, a comprehensive assessment of all potential biases was beyond the scope of this study. We recommend that future research in this area should attempt to develop skin lesion models that can address different types of demographics and other data biases in one inclusive model. Other potential directions for research, besides adversarial learning to mitigate continuous variable-related bias (e.g., age), are to investigate deep imbalanced regression [40] and supervised contrastive regression approaches [41].  
Lastly, our study focused on the technical aspects of bias mitigation and did not delve into the ethical and societal implications of deploying AI in healthcare. Future work should consider the broader ethical frameworks within which these technologies operate, including issues of consent, transparency, and accountability.