作者归属中误归因不公平性的量化
Quantifying Misattribution Unfairness in Authorship Attribution
AI总结：
本文提出误归因不公平指数MAUIk，量化作者归属模型中不同作者被误判为文本作者的风险差异，发现模型普遍存在不公平性且与作者嵌入向量距质心远近相关。
摘要：
作者误归因在现实生活中可能产生深远后果。在司法鉴定场景中，仅仅被视作某份证据性文本或通信的潜在作者之一，就可能导致不必要的审查。这引出了一个公平性问题：候选池中的每一位作者是否面临同等的误归因风险？作者归属系统的标准评估指标并未明确考虑这一公平性概念。我们引入了一个简单度量——误归因不公平指数（MAUIk），其基于作者在未撰写的文本中被排入前k名的频率。利用该度量，我们量化了五个模型在两个不同数据集上的不公平性。所有模型都表现出高度不公平，部分作者面临更高风险。此外，我们发现这种不公平性与模型在潜在搜索空间中将作者嵌入为向量的方式有关。特别地，我们观察到，在干草堆（haystack）中越靠近嵌入作者质心（或中心）的作者，其误归因风险越高。这些结果表明存在潜在危害，并且在构建和提供此类模型供下游使用时，需要与最终用户沟通误归因风险并对其进行校准。
英文摘要：
Authorship misattribution can have profound consequences in real life. In forensic settings simply being considered as one of the potential authors of an evidential piece of text or communication can result in undesirable scrutiny. This raises a fairness question: Is every author in the candidate pool at equal risk of misattribution? Standard evaluation measures for authorship attribution systems do not explicitly account for this notion of fairness. We introduce a simple measure, Misattribution Unfairness Index (MAUIk), which is based on how often authors are ranked in the top k for texts they did not write. Using this measure we quantify the unfairness of five models on two different datasets. All models exhibit high levels of unfairness with increased risks for some authors. Furthermore, we find that this unfairness relates to how the models embed the authors as vectors in the latent search space. In particular, we observe that the risk of misattribution is higher for authors closer to the centroid (or center) of the embedded authors in the haystack. These results indicate the potential for harm and the need for communicating with and calibrating end users on misattribution risk when building and providing such models for downstream use.
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