:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	De Carlo, Alessandro, Parimbelli, Enea, Melillo, Nicola, Nicora, Giovanna
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Artificial Intelligence
Online Access:	https://arxiv.org/abs/2407.18343
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

How can we trust opaque systems? Criteria for robust explanations in XAI
by: Boge, Florian J., et al.
Published: (2025)

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

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

Evaluation of Predictive Reliability to Foster Trust in Artificial Intelligence. A case study in Multiple Sclerosis
by: Peracchio, Lorenzo, et al.
Published: (2024)

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

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

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

Finding the right XAI method -- A Guide for the Evaluation and Ranking of Explainable AI Methods in Climate Science
by: Bommer, Philine, et al.
Published: (2023)

Overcoming Anchoring Bias: The Potential of AI and XAI-based Decision Support
by: Haag, Felix, et al.
Published: (2024)

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

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

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

ExplainReduce: Generating global explanations from many local explanations
by: Seppäläinen, Lauri, et al.
Published: (2025)

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

Introducing User Feedback-based Counterfactual Explanations (UFCE)
by: Suffian, Muhammad, et al.
Published: (2024)

A method for the systematic generation of graph XAI benchmarks via Weisfeiler-Leman coloring
by: Fontanesi, Michele, et al.
Published: (2025)

PAX-TS: Model-agnostic multi-granular explanations for time series forecasting via localized perturbations
by: Kreuzer, Tim, et al.
Published: (2025)

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

Igea: a Decoder-Only Language Model for Biomedical Text Generation in Italian
by: Buonocore, Tommaso Mario, et al.
Published: (2024)

Mind the XAI Gap: A Human-Centered LLM Framework for Democratizing Explainable AI
by: Paraschou, Eva, et al.
Published: (2025)

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

A Mechanistic Explanatory Strategy for XAI
by: Rabiza, Marcin
Published: (2024)

PSO-XAI: A PSO-Enhanced Explainable AI Framework for Reliable Breast Cancer Detection
by: Raquib, Mirza, et al.
Published: (2025)

In defence of post-hoc explanations in medical AI
by: Hatherley, Joshua, et al.
Published: (2025)

A Bayesian explanation of machine learning models based on modes and functional ANOVA
by: Long, Quan
Published: (2024)

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

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

Towards consistency of rule-based explainer and black box model -- fusion of rule induction and XAI-based feature importance
by: Kozielski, Michał, et al.
Published: (2024)

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

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

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

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

Advancing Italian Biomedical Information Extraction with Transformers-based Models: Methodological Insights and Multicenter Practical Application
by: Crema, Claudio, et al.
Published: (2023)

Explainable AI Through a Democratic Lens: DhondtXAI for D'Hondt-Projected Feature Attribution
by: Donmez, Turker Berk
Published: (2024)

Improving Health Professionals' Onboarding with AI and XAI for Trustworthy Human-AI Collaborative Decision Making
by: Lee, Min Hun, et al.
Published: (2024)

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

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

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

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

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