Shielded Representations: Protecting Sensitive Attributes Through
  Iterative Gradient-Based Projection
Shadi Iskander Kira Radinsky Yonatan Belinkov
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.