The ontological politics of synthetic data: Normalities, outliers, and intersectional hallucinations

Synthetic data is increasingly used as a substitute for real data due to ethical, legal, and logistical reasons. However, the rise of synthetic data also raises critical questions about its entanglement with the politics of classification and the reproduction of social norms and categories. This paper aims to problematize the use of synthetic data by examining how its production is intertwined with the maintenance of certain worldviews and classifications. We argue that synthetic data, like real data, is embedded with societal biases and power structures, leading to the reproduction of existing social inequalities. Synthetic data can have crucial ontological consequences and contribute to the reproduction of social facts and categories, such as class, race, gender, or age. Through empirical examples, we demonstrate how synthetic data tends to highlight majority elements as the 'normal' and minimize minority elements, and that changes to data structures that create synthetic data will inevitably result in what we term 'intersectional hallucinations.'

read the pdf doi: 10.1177/20539517251318289

Lee, Francis, Saghi Hajisharif, and Ericka Johnson. 2025. "The ontological politics of synthetic data: Normalities, outliers, and intersectional hallucinations." Big Data & Society 12(2). https://doi.org/10.1177/20539517251318289