  Knowledge Graphs are a widely used method to represent relations between
entities in various AI applications, and Graph Embedding has rapidly become a
standard technique to represent Knowledge Graphs in such a way as to facilitate
inferences and decisions. As this representation is obtained from behavioural
data, and is not in a form readable by humans, there is a concern that it might
incorporate unintended information that could lead to biases. We propose
EXTRACT: a suite of Explainable and Transparent methods to ConTrol bias in
knowledge graph embeddings, so as to assess and decrease the implicit presence
of protected information. Our method uses Canonical Correlation Analysis (CCA)
to investigate the presence, extent and origins of information leaks during
training, then decomposes embeddings into a sum of their private attributes by
solving a linear system. Our experiments, performed on the MovieLens1M dataset,
show that a range of personal attributes can be inferred from a user's viewing
behaviour and preferences, including gender, age, and occupation. Further
experiments, performed on the KG20C citation dataset, show that the information
about the conference in which a paper was published can be inferred from the
citation network of that article. We propose four transparent methods to
maintain the capability of the embedding to make the intended predictions
without retaining unwanted information. A trade-off between these two goals is
observed.
