  Pre-trained transformer-based language models are becoming increasingly
popular due to their exceptional performance on various benchmarks. However,
concerns persist regarding the presence of hidden biases within these models,
which can lead to discriminatory outcomes and reinforce harmful stereotypes. To
address this issue, we propose Finspector, a human-centered visual inspection
tool designed to detect biases in different categories through log-likelihood
scores generated by language models. The goal of the tool is to enable
researchers to easily identify potential biases using visual analytics,
ultimately contributing to a fairer and more just deployment of these models in
both academic and industrial settings. Finspector is available at
https://github.com/IBM/finspector.
