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Assessing the Geographic Diversity of AI's Platial Representations in Image Generation
arXiv:2606.05188v1 Announce Type: new Abstract: (Gen)AI diversity is not merely an ethical issue. From the perspective of geographic information science (GIScience), it could be interpreted as a function of uncertainty and as a form of cognitive bias, embedded in AI outputs. Recent work has sought to develop information-theoretic diversity measures and apply them to evaluate AI-chatbot outputs in a geographic context.
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Announce Type: new Abstract: Among the many challenges hindering the responsible development and deployment of AI, arguably none has faced more intense scrutiny than bias in its various forms. This underscores the widespread concerns across AI researchers that model outputs, e.g., from generative AI, may encode structural distributional imbalances (stemming from training data or model design) that may amplify social inequality or introduce systemic distortions across application domains...
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Brussels' datacenter efficiency scorecard may come with a credit warning
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Tessera AI model offers accessible way to view Earth
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