Advances in Artificial Intelligence are challenged by the biases rooted in
the datasets used to train the models. In image geolocation estimation, models
are mostly trained using data from specific geographic regions, notably the
Western world, and as a result, they may struggle to comprehend the
complexities of underrepresented regions. To assess this issue, we apply a
state-of-the-art image geolocation estimation model (ISNs) to a crowd-sourced
dataset of geolocated images from the African continent (SCA100), and then
explore the regional and socioeconomic biases underlying the model's
predictions. Our findings show that the ISNs model tends to over-predict image
locations in high-income countries of the Western world, which is consistent
with the geographic distribution of its training data, i.e., the IM2GPS3k
dataset. Accordingly, when compared to the IM2GPS3k benchmark, the accuracy of
the ISNs model notably decreases at all scales. Additionally, we cluster images
of the SCA100 dataset based on how accurately they are predicted by the ISNs
model and show the model's difficulties in correctly predicting the locations
of images in low income regions, especially in Sub-Saharan Africa. Therefore,
our results suggest that using IM2GPS3k as a training set and benchmark for
image geolocation estimation and other computer vision models overlooks its
potential application in the African context.

人工智能的进展受到训练模型使用的数据集中的偏见的挑战。在图像地理定位估计中，模型通常使用特定地理区域（尤其是西方世界）的数据进行训练，因此他们可能难以理解未被充分代表的地区的复杂性。为了评估这个问题，我们使用最先进的图像地理定位估计模型（ISNs）对来自非洲大陆的众包地理定位图像数据集（SCA100）进行应用，并探讨模型预测中潜在的地区和社会经济偏见。我们的研究结果显示，ISNs 模型倾向于对西方富裕国家的图像位置进行过度预测，这与其训练数据集 IM2GPS3k 的地理分布一致。因此，与 IM2GPS3k 基准相比，ISNs 模型的准确性在各个尺度上显著降低。此外，我们基于 ISNs 模型对 SCA100 数据集中的图像进行了聚类，展示了模型在准确预测低收入地区图像位置方面的困难，尤其在撒哈拉以南非洲地区。因此，我们的研究结果表明，在图像地理定位估计和其他计算机视觉模型中，使用 IM2GPS3k 作为训练集和基准忽视了其在非洲背景下的潜在应用。