The application of machine learning (ML) technologies in medicine generally but also in radiology more specifically is hoped to improve clinical processes and the provision of healthcare . A central motivation in this regard is to advance patient treatment by reducing human error and increasing the accuracy of prognosis, diagnosis and therapy decisions . There is, however, also increasing awareness about bias in ML technologies and its potentially harmful consequences . Biases refer to systematic distortions of datasets, algorithms, or human decision making . These systematic distortions are understood to have negative effects on the quality of an outcome in terms of accuracy, fairness, or transparency . But biases are not only a technical problem that requires a technical solution . Because they often also have a social dimension, the 'distorted' outcomes they yield often have implications for equity . This paper assesses different types of biases that can emerge within applications of ML in radiology, and discusses in what cases such biases are problematic . Drawing upon theories of equity in healthcare, we argue that while some biases are harmful and should be acted upon, others might be unproblematic and even desirable-exactly because they can contribute to overcome inequities.