Local Explanations of Global Rankings: Insights for Competitive Rankings

Explaining complex algorithms and models has recently received growing attention in various domains to support informed decisions. Ranking functions are widely used for almost every form of human activity to enable effective decision-making processes. Hence, explaining ranking indicators and their importance are essential properties to enhance performance. Local explanation techniques have recently become a prominent way to interpret individual predictions of machine learning models. However, there has been limited investigation into explaining competitive rankings. This work proposes a hierarchical ranking explanation framework to capture local explanations for competitive rankings by defining a proper neighborhood construction approach. We explore various explanation techniques to identify the local contribution of ranking indicators based on the position of an instance in the ranking as well as the size of the neighborhood around the instance of interest. We evaluate the generated explanations for the
Times Higher Education
university ranking dataset as a benchmark of competitive ranking. The results reveal insights for a wide range of instances in the ranking list and indicate the importance of local explanations for competitive rankings.