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Machine learning models can perpetuate unintended biases from unfair andimbalanced datasets. Evaluating and debiasing these datasets and models isespecially hard in text datasets where sensitive attributes such as race,gender, and sexual orientation may not be available. When these models aredeployed into society, they can lead to unfair outcomes for historicallyunderrepresented groups. In this paper, we present a dataset coupled with anapproach to improve text fairness in classifiers and language models. We createa new, more comprehensive identity lexicon, TIDAL, which includes 15,123identity terms and associated sense context across three demographiccategories. We leverage TIDAL to develop an identity annotation andaugmentation tool that can be used to improve the availability of identitycontext and the effectiveness of ML fairness techniques. We evaluate ourapproaches using human contributors, and additionally run experiments focusedon dataset and model debiasing. Results show our assistive annotation techniqueimproves the reliability and velocity of human-in-the-loop processes. Ourdataset and methods uncover more disparities during evaluation, and alsoproduce more fair models during remediation. These approaches provide apractical path forward for scaling classifier and generative model fairness inreal-world settings.

TIDE: Textual Identity Detection for Evaluating and Augmenting Classification and Language Models