:: Library Catalog

Imagen de Portada

Guardado en:

Detalles Bibliográficos
Autores principales:	Boge, Florian J., Schuster, Annika
Formato:	Preprint
Publicado:	2025
Materias:	Machine Learning Artificial Intelligence
Acceso en línea:	https://arxiv.org/abs/2508.12623
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Who can we trust? LLM-as-a-jury for Comparative Assessment
por: Qian, Mengjie, et al.
Publicado: (2026)

Introducing δ-XAI: a novel sensitivity-based method for local AI explanations
por: De Carlo, Alessandro, et al.
Publicado: (2024)

Why do explanations fail? A typology and discussion on failures in XAI
por: Bove, Clara, et al.
Publicado: (2024)

Can we trust the evaluation on ChatGPT?
por: Aiyappa, Rachith, et al.
Publicado: (2023)

The future of human-centric eXplainable Artificial Intelligence (XAI) is not post-hoc explanations
por: Swamy, Vinitra, et al.
Publicado: (2023)

Aligned, Orthogonal or In-conflict: When can we safely optimize Chain-of-Thought?
por: Kaufmann, Max, et al.
Publicado: (2026)

How Reliable and Stable are Explanations of XAI Methods?
por: Ribeiro, José, et al.
Publicado: (2024)

Model Science: getting serious about verification, explanation and control of AI systems
por: Biecek, Przemyslaw, et al.
Publicado: (2025)

Are we still able to recognize pearls? Machine-driven peer review and the risk to creativity: An explainable RAG-XAI detection framework with markers extraction
por: Văduva, Alin-Gabriel, et al.
Publicado: (2026)

Toward the application of XAI methods in EEG-based systems
por: Apicella, Andrea, et al.
Publicado: (2022)

Towards plausibility in time series counterfactual explanations
por: Kostrzewa, Marcin, et al.
Publicado: (2026)

Actionable and diverse counterfactual explanations incorporating domain knowledge and plausibility constraints
por: Bobek, Szymon, et al.
Publicado: (2025)

How adversarial attacks can disrupt seemingly stable accurate classifiers
por: Sutton, Oliver J., et al.
Publicado: (2023)

A Mechanistic Explanatory Strategy for XAI
por: Rabiza, Marcin
Publicado: (2024)

Dual feature-based and example-based explanation methods
por: Konstantinov, Andrei V., et al.
Publicado: (2024)

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

Can I trust my anomaly detection system? A case study based on explainable AI
por: Rashid, Muhammad, et al.
Publicado: (2024)

Local Universal Explainer (LUX) -- a rule-based explainer with factual, counterfactual and visual explanations
por: Bobek, Szymon, et al.
Publicado: (2023)

XAI-Units: Benchmarking Explainability Methods with Unit Tests
por: Lee, Jun Rui, et al.
Publicado: (2025)

Axe the X in XAI: A Plea for Understandable AI
por: Páez, Andrés
Publicado: (2024)

IMPACTX: improving model performance by appropriately constraining the training with teacher explanations
por: Apicella, Andrea, et al.
Publicado: (2025)

Towards a perturbation-based explanation for medical AI as differentiable programs
por: Abe, Takeshi, et al.
Publicado: (2025)

HyConEx: Hypernetwork classifier with counterfactual explanations for tabular data
por: Marszałek, Patryk, et al.
Publicado: (2025)

Limits of trust in medical AI
por: Hatherley, Joshua
Publicado: (2025)

STRIDE: Subset-Free Functional Decomposition for XAI in Tabular Settings
por: Ko, Chaeyun
Publicado: (2025)

OpenXAI: Towards a Transparent Evaluation of Model Explanations
por: Agarwal, Chirag, et al.
Publicado: (2022)

Reliable Explanations or Random Noise? A Reliability Metric for XAI
por: Sengupta, Poushali, et al.
Publicado: (2026)

Informative Semi-Factuals for XAI: The Elaborated Explanations that People Prefer
por: Aryal, Saugat, et al.
Publicado: (2026)

XAI for In-hospital Mortality Prediction via Multimodal ICU Data
por: Li, Xingqiao, et al.
Publicado: (2023)

CHILLI: A data context-aware perturbation method for XAI
por: Anwar, Saif, et al.
Publicado: (2024)

Making deep neural networks right for the right scientific reasons by interacting with their explanations
por: Schramowski, Patrick, et al.
Publicado: (2020)

Which LIME should I trust? Concepts, Challenges, and Solutions
por: Knab, Patrick, et al.
Publicado: (2025)

NormEnsembleXAI: Unveiling the Strengths and Weaknesses of XAI Ensemble Techniques
por: Hryniewska-Guzik, Weronika, et al.
Publicado: (2024)

Explaining deep neural network models for electricity price forecasting with XAI
por: Pesenti, Antoine, et al.
Publicado: (2025)

Towards Explainable Artificial Intelligence (XAI): A Data Mining Perspective
por: Xiong, Haoyi, et al.
Publicado: (2024)

Modular addition without black-boxes: Compressing explanations of MLPs that compute numerical integration
por: Yip, Chun Hei, et al.
Publicado: (2024)

Guiding the generation of counterfactual explanations through temporal background knowledge for Predictive Process Monitoring
por: Buliga, Andrei, et al.
Publicado: (2024)

EXACT: Towards a platform for empirically benchmarking Machine Learning model explanation methods
por: Clark, Benedict, et al.
Publicado: (2024)

Observation-specific explanations through scattered data approximation
por: Ghidini, Valentina, et al.
Publicado: (2024)

Are self-explanations from Large Language Models faithful?
por: Madsen, Andreas, et al.
Publicado: (2024)