Healthsheet: Development of a Transparency Artifact for Health Datasets

Diana Mincu, Negar Rostamzadeh, Subhrajit Roy, Andrew J Smart, Lauren Wilcox, Mahima Pushkarna, Jessica Schrouff, Razvan Adrian Amironesei, Nyalleng Moorosi, Katherine A Heller

Abstract: The use of machine learning (ML) is rapidly expanding in healthcare as the amount of new data generated improves our capacity to effectively manage complex clinical and diagnostic information [26, 40, 50, 64, 70]. During the last decade, ML has played a central role in high-stakes heathcare problems such as precision medicine [51, 65], survival analysis [36, 43], disease diagnosis [20], and continuous innovations in treatment plans. While ML approaches present opportunities to assist healthcare professionals, streamline the healthcare system, and potentially improve patient outcomes, they bring with them ethical concerns [8, 24, 41] that range from racial and gender disparities, accessibility of clinical studies, to subjectivity in healthcare practices and biases [14].