Abstract

Data synthesis as a strategy for broadening access to private and confidential data has seen an exponential growth in recent years. The hype around synthetic data is mostly triggered by the rise of modern AI and its hunger for data. In this context, the synthetic data approach also became increasingly popular in Computer Science with various generative AI models being turned into data synthesizers. However, the strong focus on algorithms in the Computer Science literature led to what I call the three black boxes problem. Current discussions at least in the scientific community often do not think about the data, ignore the purposes for which the data are generated, and typically do not try to understand how the synthesis model affects downstream analysis tasks. In this talk, I will discuss some of the challenges arising from this narrow focus on the algorithm.