DecoupleGen personalizes diffusion models to create images with uncommon contexts for debiasing object recognition, yielding consistent gains on scene classification tasks.
No Classification without Representation: Assessing Geodiversity Issues in Open Data Sets for the Developing World
3 Pith papers cite this work. Polarity classification is still indexing.
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abstract
Modern machine learning systems such as image classifiers rely heavily on large scale data sets for training. Such data sets are costly to create, thus in practice a small number of freely available, open source data sets are widely used. We suggest that examining the geo-diversity of open data sets is critical before adopting a data set for use cases in the developing world. We analyze two large, publicly available image data sets to assess geo-diversity and find that these data sets appear to exhibit an observable amerocentric and eurocentric representation bias. Further, we analyze classifiers trained on these data sets to assess the impact of these training distributions and find strong differences in the relative performance on images from different locales. These results emphasize the need to ensure geo-representation when constructing data sets for use in the developing world.
years
2026 3verdicts
UNVERDICTED 3representative citing papers
The study adapts ecological diversity measures to evaluate platial representations in GPT and DALL-E images, finding low diversity, greater gains from prompt revision than generation, and stereotypical feature use.
Literature review catalogs geographic biases in AI from training data imbalances to generative outputs over-favoring prototypical places and discusses diversity evaluations.
citing papers explorer
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Personalized Generative Models for Contextual Debiasing
DecoupleGen personalizes diffusion models to create images with uncommon contexts for debiasing object recognition, yielding consistent gains on scene classification tasks.
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Assessing the Geographic Diversity of AI's Platial Representations in Image Generation
The study adapts ecological diversity measures to evaluate platial representations in GPT and DALL-E images, finding low diversity, greater gains from prompt revision than generation, and stereotypical feature use.
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Geographic Bias and Diversity in AI Evaluation
Literature review catalogs geographic biases in AI from training data imbalances to generative outputs over-favoring prototypical places and discusses diversity evaluations.