  What we discover and see online, and consequently our opinions and decisions,
are becoming increasingly affected by automated machine learned predictions.
Similarly, the predictive accuracy of learning machines heavily depends on the
feedback data that we provide them. This mutual influence can lead to
closed-loop interactions that may cause unknown biases which can be exacerbated
after several iterations of machine learning predictions and user feedback.
Machine-caused biases risk leading to undesirable social effects ranging from
polarization to unfairness and filter bubbles.
  In this paper, we study the bias inherent in widely used recommendation
strategies such as matrix factorization. Then we model the exposure that is
borne from the interaction between the user and the recommender system and
propose new debiasing strategies for these systems.
  Finally, we try to mitigate the recommendation system bias by engineering
solutions for several state of the art recommender system models.
  Our results show that recommender systems are biased and depend on the prior
exposure of the user. We also show that the studied bias iteratively decreases
diversity in the output recommendations. Our debiasing method demonstrates the
need for alternative recommendation strategies that take into account the
exposure process in order to reduce bias.
  Our research findings show the importance of understanding the nature of and
dealing with bias in machine learning models such as recommender systems that
interact directly with humans, and are thus causing an increasing influence on
human discovery and decision making
