Hypothesis after observing the dataset:
The Chicago Face Database, while comprehensive, appears to have an underrepresentation of individuals aged 60 and above. This observed age bias could potentially impact the performance of machine learning models trained on this dataset, particularly in tasks involving the generation or recognition of faces belonging to this age group. Consequently, it is hypothesized that a model trained on this dataset may exhibit decreased accuracy and reliability when applied to faces of individuals older than 60 years old. This project aims to statistically validate this hypothesis and explore potential mitigation strategies to address this bias, thereby enhancing the model’s performance across all age groups.