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 may end 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 NBIAS that consists of four main layers: data, corpus construction, model development and an evaluation layer. The dataset is constructed by collecting diverse data from various domains, 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 BIAS. In the evaluation procedure, we incorporate a blend of quantitative and qualitative measures 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. The proposed approach is applicable to a variety of biases and contributes to the fair and ethical use of textual data.

翻译：文本数据中的偏见可能导致数据使用时产生扭曲的解释与结果。这些偏见可能固化刻板印象、歧视或其他形式的不公正待遇。基于偏见数据训练的算法最终可能做出对特定群体产生不成比例影响的决策。因此，检测并消除这些偏见对于确保数据使用的公平性与伦理性至关重要。为此，我们开发了综合且鲁棒的NBIAS框架，该框架包含四个主要层次：数据层、语料构建层、模型开发层与评估层。数据集通过收集社交媒体、医疗保健和招聘门户网站等不同领域的多样化数据构建。在此基础上，我们应用了基于Transformer的标记分类模型，该模型能够通过独特的命名实体BIAS识别偏见词汇/短语。评估过程中，我们结合定量与定性指标衡量模型的有效性。与基线相比，准确率提升幅度达到1%至8%，同时我们能够对模型功能形成深入理解。所提方法适用于多种偏见类型，有助于促进文本数据的公平与道德使用。