Machine learning models can perpetuate unintended biases from unfair and imbalanced datasets. Evaluating and debiasing these datasets and models is especially hard in text datasets where sensitive attributes such as race, gender, and sexual orientation may not be available. When these models are deployed into society, they can lead to unfair outcomes for historically underrepresented groups. In this paper, we present a dataset coupled with an approach to improve text fairness in classifiers and language models. We create a new, more comprehensive identity lexicon, TIDAL, which includes 15,123 identity terms and associated sense context across three demographic categories. We leverage TIDAL to develop an identity annotation and augmentation tool that can be used to improve the availability of identity context and the effectiveness of ML fairness techniques. We evaluate our approaches using human contributors, and additionally run experiments focused on dataset and model debiasing. Results show our assistive annotation technique improves the reliability and velocity of human-in-the-loop processes. Our dataset and methods uncover more disparities during evaluation, and also produce more fair models during remediation. These approaches provide a practical path forward for scaling classifier and generative model fairness in real-world settings.

翻译：机器学习模型可能延续来自不公正和不平衡数据集的非预期偏见。在文本数据集中，由于种族、性别和性取向等敏感属性可能缺失，评估和去偏这些数据集与模型尤为困难。当这些模型被部署到社会后，可能导致历史上被边缘化群体遭受不公平结果。本文提出了一种结合数据集的方法，以提升分类器和语言模型的文本公平性。我们构建了一个新的、更全面的身份词典TIDAL，该词典涵盖15，123个身份术语及其在三个人口统计类别中的相关语义语境。我们利用TIDAL开发了一套身份标注与增强工具，可用于改善身份上下文的可用性及机器学习公平性技术的有效性。我们通过人工评审员评估了所提方法，并开展了聚焦于数据集与模型去偏的实验。结果表明，我们的辅助标注技术提升了人机交互流程的可靠性与速度。我们的数据集与方法在评估阶段能揭示更多差异，并在修正阶段生成更公平的模型。这些方法为在实际场景中扩展分类器与生成模型公平性提供了切实可行的路径。