  The ability to control for the kinds of information encoded in neural
representation has a variety of use cases, especially in light of the challenge
of interpreting these models. We present Iterative Null-space Projection
(INLP), a novel method for removing information from neural representations.
Our method is based on repeated training of linear classifiers that predict a
certain property we aim to remove, followed by projection of the
representations on their null-space. By doing so, the classifiers become
oblivious to that target property, making it hard to linearly separate the data
according to it. While applicable for multiple uses, we evaluate our method on
bias and fairness use-cases, and show that our method is able to mitigate bias
in word embeddings, as well as to increase fairness in a setting of multi-class
classification.
