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Register a new userDetecting Cross-Geographic Biases in Toxicity Modeling on Social Media
الكشف عن التحيزات عبر الجغرافية في نمذجة سمية على وسائل التواصل الاجتماعي
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تعتمد منصات وسائل التواصل الاجتماعي عبر الإنترنت على نحو متزايد على تقنيات معالجة اللغة الطبيعية (NLP) للكشف عن محتوى مسيء على نطاق واسع من أجل تخفيف الأضرار التي يسببها لمستخدميها. ومع ذلك، فإن هذه التقنيات تعاني من مختلف تحيزات أخذ العينات والجمعية الموجودة في البيانات التدريبية، والتي غالبا ما تؤدي إلى أداء الفرعية على المحتوى ذي الصلة بالمجموعات المهمشة، مما يحتمل أن يؤدي إلى أضرار غير متناسبة تجاههم. ركزت دراسات حول مثل هذه التحيزات حتى الآن على حفنة فقط من محاور التباينات والمجموعات الفرعية التي تحتوي على التعليقات التوضيحية / المعجم المتاحة. وبالتالي، يتم تجاهل التحيزات المتعلقة بالسياقات غير الغربية إلى حد كبير في الأدبيات. في هذه الورقة، نقدم طريقة خاضعة للإشراف ضعيفا للكشف عن التحيزات المعجمية بقوة في السياقات الجغرافية الثقافية الأوسع نطاقا. من خلال دراسة الحالة حول نموذج كشف للسمية المتوفرة للجمهور، نوضح أن طريقتنا تحدد المجموعات البارزة من الأخطاء المتبادلة الجغرافية، وفي متابعة، توضح أن هذه التجمعات تعكس الأحكام الإنسانية من اللغة الهجومية واللغة الفعلية في تلك السياقات الجغرافية. نحن أيضا إجراء تحليل نموذج تدرب على مجموعة بيانات مع ملصقات الحقيقة الأرضية لفهم هذه التحيزات بشكل أفضل، وتقديم تجارب التخفيف الأولي.
Online social media platforms increasingly rely on Natural Language Processing (NLP) techniques to detect abusive content at scale in order to mitigate the harms it causes to their users. However, these techniques suffer from various sampling and association biases present in training data, often resulting in sub-par performance on content relevant to marginalized groups, potentially furthering disproportionate harms towards them. Studies on such biases so far have focused on only a handful of axes of disparities and subgroups that have annotations/lexicons available. Consequently, biases concerning non-Western contexts are largely ignored in the literature. In this paper, we introduce a weakly supervised method to robustly detect lexical biases in broader geo-cultural contexts. Through a case study on a publicly available toxicity detection model, we demonstrate that our method identifies salient groups of cross-geographic errors, and, in a follow up, demonstrate that these groupings reflect human judgments of offensive and inoffensive language in those geographic contexts. We also conduct analysis of a model trained on a dataset with ground truth labels to better understand these biases, and present preliminary mitigation experiments.
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