Digital Divides in Scene Recognition: Uncovering Socioeconomic Biases in Deep Learning Systems

• URL: http://arxiv.org/abs/2401.13097v1
• Date: Tue, 23 Jan 2024 21:22:06 GMT
• Title: Digital Divides in Scene Recognition: Uncovering Socioeconomic Biases in Deep Learning Systems
• Authors: Michelle R. Greene, Mariam Josyula, Wentao Si and Jennifer A. Hart
• Abstract summary: We investigate the biases of deep convolutional neural networks (dCNNs) in scene classification. We use nearly one million images from global and US sources, including user-submitted home photographs and Airbnb listings. Our analyses revealed significant socioeconomic bias, where pretrained dCNNs demonstrated lower classification accuracy, lower classification confidence, and a higher tendency to assign labels that could be offensive.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Computer-based scene understanding has influenced fields ranging from urban planning to autonomous vehicle performance, yet little is known about how well these technologies work across social differences. We investigate the biases of deep convolutional neural networks (dCNNs) in scene classification, using nearly one million images from global and US sources, including user-submitted home photographs and Airbnb listings. We applied statistical models to quantify the impact of socioeconomic indicators such as family income, Human Development Index (HDI), and demographic factors from public data sources (CIA and US Census) on dCNN performance. Our analyses revealed significant socioeconomic bias, where pretrained dCNNs demonstrated lower classification accuracy, lower classification confidence, and a higher tendency to assign labels that could be offensive when applied to homes (e.g., "ruin", "slum"), especially in images from homes with lower socioeconomic status (SES). This trend is consistent across two datasets of international images and within the diverse economic and racial landscapes of the United States. This research contributes to understanding biases in computer vision, emphasizing the need for more inclusive and representative training datasets. By mitigating the bias in the computer vision pipelines, we can ensure fairer and more equitable outcomes for applied computer vision, including home valuation and smart home security systems. There is urgency in addressing these biases, which can significantly impact critical decisions in urban development and resource allocation. Our findings also motivate the development of AI systems that better understand and serve diverse communities, moving towards technology that equitably benefits all sectors of society.

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