Bias in textual data can lead to skewed interpretations and outcomes when the
data is used. These biases could perpetuate stereotypes, discrimination, or
other forms of unfair treatment. An algorithm trained on biased data ends up
making decisions that disproportionately impact a certain group of people.
Therefore, it is crucial to detect and remove these biases to ensure the fair
and ethical use of data. To this end, we develop a comprehensive and robust
framework \textsc{Nbias} that consists of a data layer, corpus contruction,
model development layer and an evaluation layer. The dataset is constructed by
collecting diverse data from various fields, including social media,
healthcare, and job hiring portals. As such, we applied a transformer-based
token classification model that is able to identify bias words/ phrases through
a unique named entity. In the assessment procedure, we incorporate a blend of
quantitative and qualitative evaluations to gauge the effectiveness of our
models. We achieve accuracy improvements ranging from 1% to 8% compared to
baselines. We are also able to generate a robust understanding of the model
functioning, capturing not only numerical data but also the quality and
intricacies of its performance. The proposed approach is applicable to a
variety of biases and contributes to the fair and ethical use of textual data.

通过开发一个全面而健壮的框架 “Nbias”，我们能够检测和消除文本数据中的偏见，以确保数据的公正和道德使用。该框架包括数据层、语料库构建、模型开发层和评估层，并应用了基于 transformer 的标记分类模型识别具有独特命名实体的偏见词语 / 短语。通过定量和定性评估的混合方法，我们取得了 1% 至 8% 的准确率改进，同时还能全面理解模型的性能，捕捉其数量数据以及质量和复杂性的特征。该方法适用于各种偏见，并促进了文本数据的公正和道德使用。