  We investigate the impact of political ideology biases in training data.
Through a set of comparison studies, we examine the propagation of biases in
several widely-used NLP models and its effect on the overall retrieval
accuracy. Our work highlights the susceptibility of large, complex models to
propagating the biases from human-selected input, which may lead to a
deterioration of retrieval accuracy, and the importance of controlling for
these biases. Finally, as a way to mitigate the bias, we propose to learn a
text representation that is invariant to political ideology while still judging
topic relevance.
