南方国家新闻中关于北方国家的内容更可信,因缺乏利益驱动造假。
News about Global North considered Truthful! The Geo-political Veracity Gradient in Global South News
- 发现南方新闻对北方话题更真实,源于跨区域造谣缺乏经济动机。
- 实证显示用北方数据训练的假新闻模型在南方语境下误判率上升。
- 提醒警惕AI假新闻检测中的地缘偏见,适合关注AI公平性的研究者。
尽管已有大量研究利用基准数据集开发用于假新闻检测的AI技术,但普遍指出不同地缘政治区域的假新闻呈现不同特征。本文通过分析论证与实证证据,揭示了全球南方新闻中存在一个重要现象:地缘真实性梯度。具体而言,我们发现全球南方媒体关于全球北方的话题(如印度媒体报道美国大选)更不容易为假。基于假新闻生产的政治经济学视角,我们认为这一现象可能源于跨区域制造虚假信息缺乏与受众地域范围相匹配的经济激励。我们从基准数据集中获得了实证支持。此外,我们还实证分析了该效应在将基于某一地区训练的AI假新闻检测模型应用于另一地区时带来的后果。本研究置于新兴的关于人工智能中地缘政治偏见的批判性学术脉络中,特别是在假新闻识别领域的应用;我们希望对地缘真实性梯度的洞察能推动假新闻AI研究朝着积极影响全球南方社会的方向发展。
原文摘要 · Abstract (English)
While there has been much research into developing AI techniques for fake news detection aided by various 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 show 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. Observing through the prism of the political economy of fake news creation, we posit that this pattern could be due to the relative lack of monetarily aligned incentives in producing fake news about a different region than the regional remit of the audience. We provide empirical evidence for this from benchmark datasets. We also empirically analyze the consequences of this effect in applying AI-based fake news detection models for fake news AI trained on one region within another regional context. We locate our work within emerging critical scholarship on geo-political biases within AI in general, particularly with AI usage in fake news identification; we hope our insight into the geo-political veracity gradient could help steer fake news AI scholarship towards positively impacting Global South societies.
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