Summary of Cohex: a Generalized Framework For Cohort Explanation, by Fanyu Meng et al.
CohEx: A Generalized Framework for Cohort Explanation
by Fanyu Meng, Xin Liu, Zhaodan Kong, Xin Chen
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel approach to explainable artificial intelligence (XAI) by developing a framework for generating cohort-based explanations. While existing XAI techniques focus on global or local explanations, this work aims to bridge the gap by providing insights into model decisions within specific groups of instances. The authors define the desired properties of cohort explanations and present a generalized framework for their generation based on supervised clustering. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about creating a new way to explain how artificial intelligence (AI) models make decisions, called cohort-based explanation. This helps us understand why AI models behave in certain ways when dealing with specific groups of things, like people or products. The authors want to figure out what makes these explanations good and create a method for generating them. |
Keywords
» Artificial intelligence » Clustering » Supervised
