Real Risks of Fake Data: Synthetic Data, Diversity-Washing and Consent Circumvention

arXiv:2405.01820v1 Announce Type: cross
Abstract: Machine learning systems require representations of the real world for training and testing - they require data, and lots of it. Collecting data at scale has logistical and ethical challenges, and synthetic data promises a solution to these challenges. Instead of needing to collect photos of real people's faces to train a facial recognition system, a model creator could create and use photo-realistic, synthetic faces. The comparative ease of generating this synthetic data rather than …

abstract arxiv challenges consent cs.ai cs.cv cs.cy data diversity ethical fake fake data learning systems machine machine learning risks scale solution synthetic synthetic data systems testing training type world