Generating Clues for Gender based Occupation De-biasing in Text
This addresses the issue of gender bias in AI and text generation for developers and writers, though it is incremental as it builds on existing de-biasing efforts with a specific focus on occupations.
The paper tackles the problem of gender bias in AI models by introducing a system that identifies gender stereotypes in text related to occupations and provides counter-evidence to assist in de-biasing, enabling human-in-the-loop correction for both AI training and story writing.
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.