Shapes and frictions of synthetic data

Abstract

Synthetic data are computer-generated data that mimic and substitute empirical observations without directly corresponding to real-world phenomena. Widely used in privacy protection, machine learning, and simulation, synthetic data is an emerging field only just beginning to be explored in the social sciences and critical data studies. However, recent developments, such as the use of synthetic data in the US Census and American Community Survey, make a reflection on the nature and implications of synthetic data urgent. While earlier work focused mostly on training data for machine-learning models, this paper presents a broad typology of synthetic data and discusses its frictions. The main argument presented is that the traditional representational model of data as symbolic references to corresponding physical or conceptual objects is insufficient for understanding and critically engaging with issues and implications of synthetic data. The paper discusses an alternative relational model, which defines data not through an object of reference but based on “who uses them, how and for which purposes”. The relational model is more productive for capturing the fact that synthetic data are defined through their purpose; their performance in a particular situation (such as training a machine learning model); and a context-dependent operationalization of evidence. The post-representational anything-goes epistemology of synthetic data can be productively challenged through a forensic approach that foregrounds the outliers, artifacts, and gaps in datasets as meaningful information.