# Mitigating Digital Ageism in Skin Lesion Detection with Adversarial Learning
## 1 Introduction  
In recent years, the success of deep learning models across a wide range of application domains has spurred the creation of numerous large datasets. However, these datasets often exhibit various forms of bias [1], which can broadly be categorized as operational or systemic. Operational biases stem from factors like the instruments used for data collection, the labeling strategies, and the definition of different data classes [2]. On the other hand, systemic biases in AI can be attributed to data collection that inadvertently reinforces societal prejudices, including those related to sexism, racism, and ageism. To address these issues, the field of fairness [3] and ethical artificial intelligence (AI) [4] approaches are becoming mainstream, aiming to mitigate some of the biases persisting in datasets. The general idea in many of these approaches is to learn a'sensitive attribute' agnostic representation that can be generalized to the rest of the data [5]. While many of these approaches are designed as classification problems, dealing with binary or multi-class scenarios like sex or ethnicity, they often overlook the consideration of numerical attributes, particularly in the context of age, when developing fair AI models. In the context of skin lesion detection, addressing age-related bias and digital ageism is critical [3; 6; 7] in developing fair AI models. Unfair models with respect to age could result in multiple issues. One major issue is the difficulty in collecting sufficient data from older adults, which limits the model's ability to generalize across different age groups. [PERSON] et al. [8] describe the presence of sampling bias in several databases containing very few data from older people. The other crucial aspect is the misrepresentation of age as a category, the reinforcement of stereotypes, a lack of awareness, and the neglect of the older population. These factors, in most cases, lead to the predominance of training data from younger people [9]. In a health-sensitive application, such as skin lesion detection, this systemic data imbalance could lead to algorithms that are less sensitive to the nuances of skin lesions in older demographics, potentially resulting in higher misdiagnosis rates. Another significant issue is the deployment of medical devices using unfair AI algorithms. As an example, if a medical treatment requires the use of a digital device (i.e., a mobile phone, web services) and the digital literacy and education of older adults is not considered, such devices risk producing discriminatory results regarding older people and may have negative impacts on their health [10]. For skin lesion detection problems, it appears that older people who are more exposed to sunlight may have a higher likelihood of developing skin cancer [11]. However, an important question is whether the publicly available datasets on skin lesions contain equal or larger numbers of older people in their cohorts. To test this, we plotted the age distributions of the PAD20 [12] and ISIC2019 [13] datasets (more details are given in Section 3.5). Figure 1 shows the combined age distribution in the ISIC2019 and PAD20 datasets. It can be seen that the representation of older adult people (more than 65 years) is small. In the PAD20 dataset, the age distribution of people above 80 is very small. Therefore, it is not clear if the skewed age distribution in these datasets reflects the prevalence of skin lesions in the older population. This could potentially lead to age-biased AI models. Furthermore, it is not clear whether the models trained on these datasets, predominately comprising younger people, generalize well to older people. Adversarial learning is one of the most commonly used techniques to mitigate general biases, and it can achieve fair and transferable performance [14]. In this setting, an adversarial network can be employed as an encoder before or after the classifier to learn a fair latent  
Figure 1: Histogram of age distribution across PAD20 (blue) and ISIC2019 (yellow) skin lesion datasets.