场景识别中的数字鸿沟：揭示深度学习系统中的社会经济偏见
Digital Divides in Scene Recognition: Uncovering Socioeconomic Biases in Deep Learning Systems
AI总结：
该研究利用近百万张图像调查深度卷积神经网络在场景分类中的社会经济偏见，发现模型对低社会经济地位家庭的图像分类准确率和置信度更低且更易分配冒犯性标签，强调需通过构建更具包容性的训练数据集来减轻偏见以实现公平。
摘要：
基于计算机的场景理解影响了从城市规划到自动驾驶车辆性能等多个领域，但人们对这些技术在不同社会差异下的表现知之甚少。我们调查了深度卷积神经网络（dCNNs）在场景分类中的偏见，使用了来自全球和美国来源的近一百万张图像，包括用户提交的家庭照片和Airbnb房源。我们应用统计模型量化了家庭收入、人类发展指数（HDI）以及来自公共数据源（CIA和美国人口普查）的人口统计因素等社会经济指标对dCNN性能的影响。我们的分析揭示了显著的社会经济偏见，预训练的dCNNs表现出较低的分类准确率、较低的分类置信度，以及在应用于家庭时分配可能具有冒犯性标签（例如“废墟”、“贫民窟”）的更高倾向，特别是在来自社会经济地位（SES）较低家庭的图像中。这一趋势在两个国际图像数据集以及美国多样化的经济和种族环境中保持一致。本研究有助于理解计算机视觉中的偏见，强调需要更具包容性和代表性的训练数据集。通过减轻计算机视觉流程中的偏见，我们可以确保应用计算机视觉获得更公平、更公正的结果，包括房屋估价和智能家居安全系统。解决这些偏见具有紧迫性，因为它们会显著影响城市发展和资源分配中的关键决策。我们的发现还推动了AI系统的开发，使其能更好地理解和服务多元化社区，迈向公平惠及社会所有部门的技术。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
