Black box medicine and transparency - Interpretability by design framework - Hall, Alison; Ordish, Johan; Mitchell, Colin; Richardson (nee Murfet), Hannah (PHG Foundation, 2020)
Building on the ethical and legal analysis, a new Interpretability by Design framework (ID framework) assists developers to think through interpretability of machine learning models intended for medical applications. As an aid to good practice, this free framework enables the systematic review of various dimensions of the proposed tool and its application via a set of seven principles and seven steps to assist developers to consider the interpretability of their machine learning model.