Fair Representation Learning for Heterogeneous Information Networks
  Recently, much attention has been paid to the societal impact of AI,
especially concerns regarding its fairness. A growing body of research has
identified unfair AI systems and proposed methods to debias them, yet many
challenges remain. Representation learning for Heterogeneous Information
Networks (HINs), a fundamental building block used in complex network mining,
has socially consequential applications such as automated career counseling,
but there have been few attempts to ensure that it will not encode or amplify
harmful biases, e.g. sexism in the job market. To address this gap, in this
paper we propose a comprehensive set of de-biasing methods for fair HINs
representation learning, including sampling-based, projection-based, and graph
neural networks (GNNs)-based techniques. We systematically study the behavior
of these algorithms, especially their capability in balancing the trade-off
between fairness and prediction accuracy. We evaluate the performance of the
proposed methods in an automated career counseling application where we
mitigate gender bias in career recommendation. Based on the evaluation results
on two datasets, we identify the most effective fair HINs representation
learning techniques under different conditions.
