On the Application of a Common Theoretical Explainability Framework in Information Retrieval

Most of the current state-of-the-art models used to solve the search and ranking tasks in Information Retrieval (IR) are considered “black boxes” due to the enormous number of parameters employed, which makes it difficult for humans to understand the relation between input and output. Thus, in the current literature, several approaches are proposed to explain their outputs, trying to make the models more explainable while maintaining the high level of effectiveness achieved. Even though many methods have been developed, there is still a lack of a common way of describing and evaluating the models and methods of the Explainabile IR (ExIR) field. This work shows how a common theoretical framework for explainability (previously presented in the biomedical field) can be applied to IR. We first describe the general framework and then focus on specific explanation techniques in the IR field, focusing on core IR tasks: search and ranking. We show how well-known methods in ExIR fit into the framework and how specific IR explainability evaluation metrics can be described using this new setting.