Design biases in NLP systems, such as performance differences for different populations, often stem from their creator's positionality, i.e., views and lived experiences shaped by identity and background. Despite the prevalence and risks of design biases, they are hard to quantify because researcher, system, and dataset positionality is often unobserved. We introduce NLPositionality, a framework for characterizing design biases and quantifying the positionality of NLP datasets and models. Our framework continuously collects annotations from a diverse pool of volunteer participants on LabintheWild, and statistically quantifies alignment with dataset labels and model predictions. We apply NLPositionality to existing datasets and models for two tasks -- social acceptability and hate speech detection. To date, we have collected 16,299 annotations in over a year for 600 instances from 1,096 annotators across 87 countries. We find that datasets and models align predominantly with Western, White, college-educated, and younger populations. Additionally, certain groups, such as non-binary people and non-native English speakers, are further marginalized by datasets and models as they rank least in alignment across all tasks. Finally, we draw from prior literature to discuss how researchers can examine their own positionality and that of their datasets and models, opening the door for more inclusive NLP systems.

翻译：自然语言处理系统中的设计偏见，如针对不同人群的性能差异，往往源于其创建者的立场性，即由身份和背景塑造的观点与生活经验。尽管设计偏见普遍存在且具有风险，但由于研究者、系统和数据集的立场性难以被观察，此类偏见难以量化。我们提出NLPositionality框架，用于描绘设计偏见并量化NLP数据集与模型的立场性。该框架通过LabintheWild平台持续收集来自多元志愿者参与者的标注数据，并统计性地量化其与数据集标签及模型预测的对齐程度。我们将NLPositionality应用于社会可接受性与仇恨言论检测两项任务的现有数据集与模型。截至目前，我们在一年内收集了来自87个国家1096名标注者对600个实例的16299条标注。研究发现，数据集与模型主要与西方、白人、受过大学教育及青年群体对齐。此外，非二元性别者与非英语母语者等特定群体在各项任务中的对齐度最低，被数据集与模型进一步边缘化。最后，我们借鉴已有文献，探讨研究者如何审视自身立场性及其数据集与模型的立场性，从而为构建更具包容性的NLP系统开辟路径。