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Abstract
While there has been much research into developing artificial intelligence (AI) techniques for fake news detection aided by various global benchmark datasets, it has often been pointed out that fake news in different geo-political regions traces different contours. In this work we uncover, through analytical arguments and empirical evidence, the existence of an important characteristic in news originating from the Global South viz., the geo-political veracity gradient. In particular, we conjecture that Global South news about topics from Global North—such as news from an Indian news agency on US elections—tend to be less likely to be fake, and provide three forms of support for the conjecture. First, observing through the prism of political economy, we posit a relative lack of monetarily aligned incentives in producing fake news about a different region than the regional remit of the audience. Second, we provide empirical evidence for this from benchmark datasets used in AI research on fake news detection. Third, we empirically illustrate this conjecture through observing its empirical effect in applying AI-based fake news detection models tested in a regional remit distinct from their training. Consequently, we point out how AI models trained in the Global North may encounter this gradient as a facet that enhances friction within Global South application contexts, creating predictions with less utility for the Global South. We locate our work within emerging critical scholarship on geo-political biases within media in the context of widespread application of AI in fake news identification. We hope our insight into the geo-political veracity gradient will help illuminate the latent geo-political anchoring within AI for fake news detection.
Published Version
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