Understanding and Mitigating Gender Bias in Information Retrieval Systems

Abstract

Recent studies have shown that information retrieval systems may exhibit stereotypical gender biases in outcomes which may lead to discrimination against minority groups, such as different genders, and impact users’ decision making and judgements. In this tutorial, we inform the audience of studies that have systematically reported the presence of stereotypical gender biases in Information Retrieval (IR) systems and different pre-trained Natural Language Processing (NLP) models. We further classify existing work on gender biases in IR systems and NLP models as being related to (1) relevance judgement datasets, (2) structure of retrieval methods, (3) representations learnt for queries and documents, (4) and pre-trained embedding models. Based on the aforementioned categories, we present a host of methods from the literature that can be leveraged to measure, control, or mitigate the existence of stereotypical biases within IR systems and different NLP models that are used for down-stream tasks. Besides, we introduce available datasets and collections that are widely used for studying the existence of gender biases in IR systems and NLP models, the evaluation metrics that can be used for measuring the level of bias and utility of the models, and de-biasing methods that can be leveraged to mitigate gender biases within those models.