Title: CohEx: A Generalized Framework for Cohort Explanation
🔗 https://ojs.aaai.org/index.php/AAAI/article/view/34140
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.
Speaker: Eliana Pastor
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🗓️ Friday, June 6, 2025, Time 12:00-13:00
📍 Meeting Room 3 – DAUIN
💻 Zoom Meeting