eXplainable Artificial Intelligence (XAI) has garnered significant attention for enhancing transparency and trust in machine learning models. However, the scopes of most existing explanation techniques focus either on offering a holistic view of the explainee model (global explanation) or on individual instances (local explanation), while the middle ground, i.e., cohort-based explanation, is less explored. Cohort explanations offer insights into the explainee's behavior on a specific group or cohort of instances, enabling a deeper understanding of model decisions within a defined context. In this paper, we discuss the unique challenges and opportunities associated with measuring cohort explanations, define their desired properties, and create a generalized framework for generating cohort explanations based on supervised clustering.
@article{arxiv.2410.13190,
title = {CohEx: A Generalized Framework for Cohort Explanation},
author = {Fanyu Meng and Xin Liu and Zhaodan Kong and Xin Chen},
journal= {arXiv preprint arXiv:2410.13190},
year = {2024}
}