Alessandra Rister Portinari Maranca, Jihoon Chung, Musashi Hinck, Adam D. Wolsky, Naoki Egami, Brandon M. Stewart
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Correcting the Measurement Errors of AI-Assisted Labeling in Image Analysis Using Design-Based Supervised Learning
Generative artificial intelligence (AI) has shown incredible leaps in performance across data of a variety of modalities including texts, images, audio, and videos. This affords social scientists the ability to annotate variables of interest from unstructured media. While rapidly improving, these methods are far from perfect and, as we show, even ignoring the small amounts of error in high accuracy systems can lead to substantial bias and invalid confidence intervals in downstream analysis. We review how using design-based supervised learning (DSL) guarantees asymptotic unbiasedness and proper confidence interval coverage by making use of a small number of expert annotations. While originally developed for use with large language models in text, we present a series of applications in the context of image analysis, including an investigation of visual predictors of the perceived level of violence in protest images, an analysis of the images shared in the Black Lives Matter movement on Twitter, and a study of U.S. outlets reporting of immigrant caravans. These applications are representative of the type of analysis performed in the visual social science landscape today, and our analyses will exemplify how DSL helps us attain statistical guarantees while using automated methods to reduce human labor.
期刊介绍:
Sociological Methods & Research is a quarterly journal devoted to sociology as a cumulative empirical science. The objectives of SMR are multiple, but emphasis is placed on articles that advance the understanding of the field through systematic presentations that clarify methodological problems and assist in ordering the known facts in an area. Review articles will be published, particularly those that emphasize a critical analysis of the status of the arts, but original presentations that are broadly based and provide new research will also be published. Intrinsically, SMR is viewed as substantive journal but one that is highly focused on the assessment of the scientific status of sociology. The scope is broad and flexible, and authors are invited to correspond with the editors about the appropriateness of their articles.