Model-based Counterfactual Generator for Gender Bias Mitigation
This work addresses gender bias in natural language processing, which is a critical issue for fairness in AI applications, but it is incremental as it builds on existing counterfactual data augmentation techniques.
The paper tackled the problem of gender bias mitigation in natural language models by proposing a model-based counterfactual generator to overcome limitations of dictionary-based methods, such as ungrammatical compositions and lack of generalization, and showed through empirical evaluation that it alleviates these shortcomings.
Counterfactual Data Augmentation (CDA) has been one of the preferred techniques for mitigating gender bias in natural language models. CDA techniques have mostly employed word substitution based on dictionaries. Although such dictionary-based CDA techniques have been shown to significantly improve the mitigation of gender bias, in this paper, we highlight some limitations of such dictionary-based counterfactual data augmentation techniques, such as susceptibility to ungrammatical compositions, and lack of generalization outside the set of predefined dictionary words. Model-based solutions can alleviate these problems, yet the lack of qualitative parallel training data hinders development in this direction. Therefore, we propose a combination of data processing techniques and a bi-objective training regime to develop a model-based solution for generating counterfactuals to mitigate gender bias. We implemented our proposed solution and performed an empirical evaluation which shows how our model alleviates the shortcomings of dictionary-based solutions.