On the Characteristics of Ranking-based Gender Bias Measures
Anja Klasnja, Negar Arabzadeh, Mahbod Mehrvarz, Ebrahim Bagheri · 2022
With increased recent awareness on the possible impact of retrieval techniques on intensifying gender biases, researchers have embarked on defining quantifiable gender bias metrics that can provide the means to concretely measure such biases in practice. While successful in allowing for identifying possible sources of gender bias, there has been little work that systematically explores the characteristics of these metrics. This paper argues that effective future works on gender biases in information retrieval require a careful understanding of the bias metrics in terms of their consistency, robustness, sensitivity and also their relation with psychological characteristics and what they actually measure. Through our experiments, we show that more rigorous work on gender bias metrics need to be pursued as existing metrics may not necessarily be consistent and robust and often capture differing psychological characteristics.