Digital divides in scene recognition: uncovering socioeconomic biases in deep learning systems

Michelle R. Greene (), Mariam Josyula, Wentao Si and Jennifer A. Hart
Additional contact information
Michelle R. Greene: Columbia University
Mariam Josyula: University of Pennsylvania
Wentao Si: Dartmouth College
Jennifer A. Hart: Bates College

Palgrave Communications, 2025, vol. 12, issue 1, 1-14

Abstract: Abstract Automatic scene classification has applications ranging from urban planning to autonomous driving, yet little is known about how well these systems work across social differences. We investigate explicit and implicit biases in deep learning architectures, including deep convolutional neural networks (dCNNs) and multimodal large language models (MLLMs). We examined nearly one million images from user-submitted photographs and Airbnb listings from over 200 countries as well as all 3320 US counties. To isolate scene-specific biases, we ensured no people were in any of the photos. We found significant explicit socioeconomic biases across all models, including lower classification accuracy, higher classification uncertainty, and increased tendencies to assign labels that could be offensive when applied to homes (e.g., “slum”) in images from homes with lower socioeconomic status. We also found significant implicit biases, with pictures from lower socioeconomic conditions more aligned with word embeddings from negative concepts. All trends were consistent across countries and within the diverse economic and racial landscapes of the United States. This research thus demonstrates a novel bias in computer vision, emphasizing the need for more inclusive and representative training datasets.

Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1057/s41599-025-04719-w Abstract (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pal:palcom:v:12:y:2025:i:1:d:10.1057_s41599-025-04719-w

Ordering information: This journal article can be ordered from
https://www.nature.com/palcomms/about

DOI: 10.1057/s41599-025-04719-w

Access Statistics for this article

More articles in Palgrave Communications from Palgrave Macmillan
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().