Model Agnostic Interpretability for Multiple Instance Learning

Joseph Early, Christine Evers, SArvapali Ramchurn

Keywords: multiple instance learning, interpretability, model-agnostic

Abstract: In Multiple Instance Learning (MIL), models are trained using bags of instances, where only a single label is provided for each bag. A bag label is often only determined by a handful of key instances within a bag, making it difficult to interpret what information a classifier is using to make decisions. In this work, we establish the key requirements for interpreting MIL models. We then go on to develop several model-agnostic approaches that meet these requirements. Our methods are compared against existing inherently interpretable MIL models on several datasets, and achieve an increase in interpretability accuracy of up to 30%. We also examine the ability of the methods to identify interactions between instances and scale to larger datasets, improving their applicability to real-world problems.

One-sentence Summary: We propose and compare several methods for model-agnostic interpretability for multiple instance learning.

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:2201.11701/code)