  As machine learning algorithms are increasingly deployed for high-impact
automated decision making, ethical and increasingly also legal standards demand
that they treat all individuals fairly, without discrimination based on their
age, gender, race or other sensitive traits. In recent years much progress has
been made on ensuring fairness and reducing bias in standard machine learning
settings. Yet, for network embedding, with applications in vulnerable domains
ranging from social network analysis to recommender systems, current options
remain limited both in number and performance. We thus propose DeBayes: a
conceptually elegant Bayesian method that is capable of learning debiased
embeddings by using a biased prior. Our experiments show that these
representations can then be used to perform link prediction that is
significantly more fair in terms of popular metrics such as demographic parity
and equalized opportunity.
