CohEx: A Generalized Framework for Cohort Explanation

• URL: http://arxiv.org/abs/2410.13190v1
• Date: Thu, 17 Oct 2024 03:36:18 GMT
• Title: CohEx: A Generalized Framework for Cohort Explanation
• Authors: Fanyu Meng, Xin Liu, Zhaodan Kong, Xin Chen,
• Abstract summary: Cohort explanations offer insights into the explainee's behavior on a specific group or cohort of instances. In this paper, we discuss the unique challenges and opportunities associated with measuring cohort explanations.
• Score: 5.269665407562217
• License:
• Abstract: 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.

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