Imagen de Portada

On the Definition and Detection of Cherry-Picking in Counterfactual Explanations

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Hinns, James, Goethals, Sofie, Van der Veeken, Stephan, Evgeniou, Theodoros, Martens, David
Formato:	Preprint
Publicado:	2026
Materias:	Machine Learning Artificial Intelligence
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Tell Me a Story! Narrative-Driven XAI with Large Language Models
por: Martens, David, et al.
Publicado: (2023)

Prompt-Counterfactual Explanations for Generative AI System Behavior
por: Goethals, Sofie, et al.
Publicado: (2026)

Aggregating Local Saliency Maps for Semi-Global Explainable Image Classification
por: Hinns, James, et al.
Publicado: (2025)

Exposing Image Classifier Shortcuts with Counterfactual Frequency (CoF) Tables
por: Hinns, James, et al.
Publicado: (2024)

Cash or Comfort? How LLMs Value Your Inconvenience
por: Cedro, Mateusz, et al.
Publicado: (2025)

A Survey on Graph Counterfactual Explanations: Definitions, Methods, Evaluation, and Research Challenges
por: Prado-Romero, Mario Alfonso, et al.
Publicado: (2022)

Tabular Diffusion based Actionable Counterfactual Explanations for Network Intrusion Detection
por: Galwaduge, Vinura, et al.
Publicado: (2025)

Calibrated Explanations: with Uncertainty Information and Counterfactuals
por: Lofstrom, Helena, et al.
Publicado: (2023)

Distributional Counterfactual Explanations With Optimal Transport
por: You, Lei, et al.
Publicado: (2024)

Galaxy Morphology Classification with Counterfactual Explanation
por: Cao, Zhuo, et al.
Publicado: (2025)

The Effect of Data Poisoning on Counterfactual Explanations
por: Artelt, André, et al.
Publicado: (2024)

Interval Abstractions for Robust Counterfactual Explanations
por: Jiang, Junqi, et al.
Publicado: (2024)

How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives
por: Ichmoukhamedov, Timour, et al.
Publicado: (2024)

Beware of "Explanations" of AI
por: Martens, David, et al.
Publicado: (2025)

On the Hardness of Computing Counterfactual and Semifactual Explanations in XAI
por: Artelt, André, et al.
Publicado: (2026)

Counterfactual Explanations for Continuous Action Reinforcement Learning
por: Dong, Shuyang, et al.
Publicado: (2025)

Rigorous Probabilistic Guarantees for Robust Counterfactual Explanations
por: Marzari, Luca, et al.
Publicado: (2024)

Generating Counterfactual Explanations Using Cardinality Constraints
por: Ruiz-Torrubiano, Rubén
Publicado: (2024)

Deep Backtracking Counterfactuals for Causally Compliant Explanations
por: Kladny, Klaus-Rudolf, et al.
Publicado: (2023)

Robust Stochastic Graph Generator for Counterfactual Explanations
por: Prado-Romero, Mario Alfonso, et al.
Publicado: (2023)

Flexible Counterfactual Explanations with Generative Models
por: Hellemans, Stig, et al.
Publicado: (2025)

Counterfactual Training: Teaching Models Plausible and Actionable Explanations
por: Altmeyer, Patrick, et al.
Publicado: (2026)

The Impact of Machine Learning Uncertainty on the Robustness of Counterfactual Explanations
por: Christodoulou, Leonidas, et al.
Publicado: (2026)

Counterfactual Explanations for Deep Learning-Based Traffic Forecasting
por: Wang, Rushan, et al.
Publicado: (2024)

FLEX: Feature Importance from Layered Counterfactual Explanations
por: Keshtmand, Nawid, et al.
Publicado: (2025)

RobustX: Robust Counterfactual Explanations Made Easy
por: Jiang, Junqi, et al.
Publicado: (2025)

Robust Counterfactual Explanations in Machine Learning: A Survey
por: Jiang, Junqi, et al.
Publicado: (2024)

Game-theoretic Counterfactual Explanation for Graph Neural Networks
por: Chhablani, Chirag, et al.
Publicado: (2024)

Graph Edits for Counterfactual Explanations: A comparative study
por: Dimitriou, Angeliki, et al.
Publicado: (2024)

Explaining k-Nearest Neighbors: Abductive and Counterfactual Explanations
por: Barceló, Pablo, et al.
Publicado: (2025)

Counterfactual Explanations with Probabilistic Guarantees on their Robustness to Model Change
por: Stępka, Ignacy, et al.
Publicado: (2024)

Probabilistically Plausible Counterfactual Explanations with Normalizing Flows
por: Wielopolski, Patryk, et al.
Publicado: (2024)

Magnitude and Rotation Invariant Detection of Transportation Modes with Missing Data Modalities
por: Van Der Donckt, Jeroen, et al.
Publicado: (2024)

What-If Explanations Over Time: Counterfactuals for Time Series Classification
por: Schlegel, Udo, et al.
Publicado: (2026)

Provably Robust Bayesian Counterfactual Explanations under Model Changes
por: Duell, Jamie, et al.
Publicado: (2026)

A Probabilistic Consensus-Driven Approach for Robust Counterfactual Explanations
por: Kostrzewa, Marcin, et al.
Publicado: (2026)

Enhancing Counterfactual Explanation Search with Diffusion Distance and Directional Coherence
por: Domnich, Marharyta, et al.
Publicado: (2024)

Counterfactual Explanations for Hypergraph Neural Networks
por: Veglianti, Fabiano, et al.
Publicado: (2026)

Counterfactual Explanations for Clustering Models
por: Spagnol, Aurora, et al.
Publicado: (2024)

From What Ifs to Insights: Counterfactuals in Causal Inference vs. Explainable AI
por: Shmueli, Galit, et al.
Publicado: (2025)