  Vast availability of text data has enabled widespread training and use of AI
systems that not only learn and predict attributes from the text but also
generate text automatically. However, these AI models also learn gender, racial
and ethnic biases present in the training data. In this paper, we present the
first system that discovers the possibility that a given text portrays a gender
stereotype associated with an occupation. If the possibility exists, the system
offers counter-evidences of opposite gender also being associated with the same
occupation in the context of user-provided geography and timespan. The system
thus enables text de-biasing by assisting a human-in-the-loop. The system can
not only act as a text pre-processor before training any AI model but also help
human story writers write stories free of occupation-level gender bias in the
geographical and temporal context of their choice.
