Closing the Gap in the Trade-off between Fair Representations and
  Accuracy
Biswajit Rout Ananya B. Sai Arun Rajkumar
The rapid developments of various machine learning models and their
deployments in several applications has led to discussions around the
importance of looking beyond the accuracies of these models. Fairness of such
models is one such aspect that is deservedly gaining more attention. In this
work, we analyse the natural language representations of documents and
sentences (i.e., encodings) for any embedding-level bias that could potentially
also affect the fairness of the downstream tasks that rely on them. We identify
bias in these encodings either towards or against different sub-groups based on
the difference in their reconstruction errors along various subsets of
principal components. We explore and recommend ways to mitigate such bias in
the encodings while also maintaining a decent accuracy in classification models
that use them.