With the recent surge in social applications relying on knowledge graphs, the
need for techniques to ensure fairness in KG based methods is becoming
increasingly evident. Previous works have demonstrated that KGs are prone to
various social biases, and have proposed multiple methods for debiasing them.
However, in such studies, the focus has been on debiasing techniques, while the
relations to be debiased are specified manually by the user. As manual
specification is itself susceptible to human cognitive bias, there is a need
for a system capable of quantifying and exposing biases, that can support more
informed decisions on what to debias. To address this gap in the literature, we
describe a framework for identifying biases present in knowledge graph
embeddings, based on numerical bias metrics. We illustrate the framework with
three different bias measures on the task of profession prediction, and it can
be flexibly extended to further bias definitions and applications. The
relations flagged as biased can then be handed to decision makers for judgement
upon subsequent debiasing.

本文提出了一种基于数值偏差度量的方法，用于识别和暴露知识图谱嵌入中存在的偏见，并在职业预测任务中展示了三种不同的偏见度量，从而弥补了目前文献中手动指定偏差关系的缺陷，以支持更明智的决策。