Special Collection

From machine learning to artificial intelligence, data science is expected to transform healthcare, through rediscovered and newly created algorithms, improved computing power and, most importantly, the availability of bigger and increasingly reliable data with which to train these algorithms. However, numerous critical ethical issues have been identified, spanning privacy, data protection, transparency and explainability, responsibility, and bias. It is widely recognised that many of the machine learning models and tools may have discriminatory impact thereby inadvertently encoding and perpetuating societal biases, thereby contributing to health inequities. Machine learning algorithms should not be focused solely on accuracy, but should also be evaluated on how they might impact disparities in patient outcomes. This special issue brings together the growing community of healthcare practitioners, social scientists, policymakers, engineers and computer scientists to design and discuss practical solutions addressing algorithmic fairness and accountability.