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Register a new userGender Bias in Text: Origin, Taxonomy, and Implications
التحيز بين الجنسين في النص: الأصل والتصنيف والآثار
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يمثل عدم المساواة بين الجنسين خسارة كبيرة في الإمكانات البشرية وإدامة ثقافة العنف، وارتفاع الفجوات في مجال الأجور بين الجنسين، وعدم وجود تمثيل المرأة في المناصب العليا والقيادية. يتم استخدام التطبيقات المدعومة من الذكاء الاصطناعي (AI) بشكل متزايد في العالم الحقيقي لتوفير قرارات نقدية حول من الذي سيتم توظيفه، ومنح قرضا، مقبول في الكلية، إلخ. ومع ذلك، فإن الركائز الرئيسية ل AI ومعالجة اللغة الطبيعية ( تم عرض NLP) وتعلم الجهاز (ML) تعكس وحتى تضخيم التحيزات والقوالب النمطية للجنسين، والتي تورثها أساسا من بيانات التدريب التاريخية. في محاولة لتسهيل تحديد الهوية والتخفيف من التحيز بين الجنسين في نص اللغة الإنجليزية، نطور تصنيفا شاملا للتصنيفات التي تعتمد على أنواع المنحات بين الجنسين التالية: الضمائر العامة، الجنسية، التحيز المهني، التحيز الاستبعاد، والدليل. ونحن نقدم أيضا نظرة عامة على نحو من القاعدة من خلال التحيز بين الجنسين، من أصلها المجتمعي إلى اللغة إلى اللغة. أخيرا، نربط الآثار المجتمعية على التحيز بين الجنسين من النوع (الأنواع) المقابلة في التصنيف المقترح. الدافع الأساسي لعملنا هو المساعدة في تمكين المجتمع الفني لتحديد والتخفيف من التحيزات ذات الصلة من التدريب كورسا لتحسين الإنصاف في أنظمة NLP.
Gender inequality represents a considerable loss of human potential and perpetuates a culture of violence, higher gender wage gaps, and a lack of representation of women in higher and leadership positions. Applications powered by Artificial Intelligence (AI) are increasingly being used in the real world to provide critical decisions about who is going to be hired, granted a loan, admitted to college, etc. However, the main pillars of AI, Natural Language Processing (NLP) and Machine Learning (ML) have been shown to reflect and even amplify gender biases and stereotypes, which are mainly inherited from historical training data. In an effort to facilitate the identification and mitigation of gender bias in English text, we develop a comprehensive taxonomy that relies on the following gender bias types: Generic Pronouns, Sexism, Occupational Bias, Exclusionary Bias, and Semantics. We also provide a bottom-up overview of gender bias, from its societal origin to its spillover onto language. Finally, we link the societal implications of gender bias to their corresponding type(s) in the proposed taxonomy. The underlying motivation of our work is to help enable the technical community to identify and mitigate relevant biases from training corpora for improved fairness in NLP systems.
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