Transfer learning involves leveraging knowledge gained from solving one task and then using that knowledge to improve performance and reduce subsequent training time on a different but related task. Despite its advantages, recent attention has been directed towards a critical concern relating to the fairness of models trained with transfer learning. A previous study has demonstrated that transfer learning can preserve biases (that are intentionally planted) from the source task, transferring them to the target task. In this paper, we question a different but equally critical problem: whether transfer learning can introduce new biases or lead to greater biases in the target task compared to models trained from scratch. Our investigation reveals that transfer learning has the potential to introduce varying degrees of bias in the target task that were not originally present in the source task. Specifically, in an Alzheimer’s Disease classification task, we show that the use of transfer learning introduces greater bias with respect to sex and age, compared to an equivalent non-transfer learning approach and a simpler model, both trained from scratch and almost as accurate. These findings underscore the need for a comprehensive understanding of the inherent limitations and risks associated with the application of transfer learning, particularly in high-risk applications, e.g. healthcare. This result also suggests the need for further research into how and when transfer learning introduces and amplifies bias.