The not-so-secret ingredient that underlies all successful Artificial Intelligence / Machine Learning (AI/ML) methods is training data. There would be no facial recognition, no targeted advertisements and no self-driving cars if it was not for large enough data sets with which those algorithms have been trained to perform their tasks. Given how central these data sets are, important ethics questions arise: How is data collection performed? And how do we govern its' use? This chapter – part of a forthcoming book – looks at why new data governance strategies are needed; investigates the relation of different data governance models to historic consent approaches; and compares different implementations of personal data exchange models.
