CohEx: A Generalized Framework for Cohort Explanation
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 17 of its 33 references that are in the map. First posted on arXiv 17 Oct 2024.
Builds on
17 of 33 references.
References · 17
- 2017A Unified Approach to Interpreting Model Predictionscited by 6,555
- 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
- 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
- 2013Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Mapscited by 2,371
- 2017SmoothGrad: removing noise by adding noisecited by 1,109
- 2017Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPRcited by 1,089
- 2019Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methodscited by 443
- 2017The (Un)reliability of saliency methodscited by 333
- 2019Explainability Fact Sheets: A Framework for Systematic Assessment of Explainable Approachescited by 101
- 2020Explainable k-Means and k-Medians Clusteringcited by 67
- 2020Model-agnostic Feature Importance and Effects with Dependent Features -- A Conditional Subgroup Approachcited by 37
- 2019Global Aggregations of Local Explanations for Black Box modelscited by 30
5 more
- 2022REPID: Regional Effect Plots with implicit Interaction Detectioncited by 17
- 2019VINE: Visualizing Statistical Interactions in Black Box Modelscited by 13
- 2021Label-Descriptive Patterns and Their Application to Characterizing Classification Errorscited by 10
- 2023Glocal Explanations of Expected Goal Models in Soccercited by 4
- 2024Towards Explainable Clustering: A Constrained Declarative based Approachcited by 2
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.