Natural language processing models tend to learn and encode social biases
present in the data. One popular approach for addressing such biases is to
eliminate encoded information from the model's representations. However,
current methods are restricted to removing only linearly encoded information.
In this work, we propose Iterative Gradient-Based Projection (IGBP), a novel
method for removing non-linear encoded concepts from neural representations.
Our method consists of iteratively training neural classifiers to predict a
particular attribute we seek to eliminate, followed by a projection of the
representation on a hypersurface, such that the classifiers become oblivious to
the target attribute. We evaluate the effectiveness of our method on the task
of removing gender and race information as sensitive attributes. Our results
demonstrate that IGBP is effective in mitigating bias through intrinsic and
extrinsic evaluations, with minimal impact on downstream task accuracy.

本文通过引入一种新的方法 IGBP，提出了一种在自然语言处理模型中去除非线性编码概念的方案，实验结果表明，该方法可以有效缓解社会偏见，并对下游任务的准确性没有太大的影响。