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We introduce bipol, a new metric with explainability, for estimating socialbias in text data. Harmful bias is prevalent in many online sources of datathat are used for training machine learning (ML) models. In a step to addressthis challenge we create a novel metric that involves a two-step process:corpus-level evaluation based on model classification and sentence-levelevaluation based on (sensitive) term frequency (TF). After creating new modelsto detect bias along multiple axes using SotA architectures, we evaluate twopopular NLP datasets (COPA and SQUAD). As additional contribution, we created alarge dataset (with almost 2 million labelled samples) for training models inbias detection and make it publicly available. We also make public our codes.

Bipol: A Novel Multi-Axes Bias Evaluation Metric with Explainability for NLP