:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	De Bona, Francesco Bombassei, Dominici, Gabriele, Miller, Tim, Langheinrich, Marc, Gjoreski, Martin
Format:	Preprint
Published:	2024
Subjects:	Artificial Intelligence
Online Access:	https://arxiv.org/abs/2410.17781
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Counterfactual Concept Bottleneck Models
by: Dominici, Gabriele, et al.
Published: (2024)

Causal Concept Graph Models: Beyond Causal Opacity in Deep Learning
by: Dominici, Gabriele, et al.
Published: (2024)

Neuron-Anchored Rule Extraction for Large Language Models via Contrastive Hierarchical Ablation
by: Sovrano, Francesco, et al.
Published: (2026)

Counterfactual Explanations for Clustering Models
by: Spagnol, Aurora, et al.
Published: (2024)

A foundation model for electrodermal activity data
by: Alchieri, Leonardo, et al.
Published: (2026)

Towards Transparent Reasoning: What Drives Faithfulness in Large Language Models?
by: McMillan, Teague, et al.
Published: (2025)

Federated Behavioural Planes: Explaining the Evolution of Client Behaviour in Federated Learning
by: Fenoglio, Dario, et al.
Published: (2024)

Differential Privacy for Anomaly Detection: Analyzing the Trade-off Between Privacy and Explainability
by: Ezzeddine, Fatima, et al.
Published: (2024)

Mitigating Prompt-Induced Cognitive Biases in General-Purpose AI for Software Engineering
by: Sovrano, Francesco, et al.
Published: (2026)

Federated Concept-Based Models: Interpretable models with distributed supervision
by: Fenoglio, Dario, et al.
Published: (2026)

In-Context Symbolic Regression for Robustness-Improved Kolmogorov-Arnold Networks
by: Sovrano, Francesco, et al.
Published: (2026)

AnyCBMs: How to Turn Any Black Box into a Concept Bottleneck Model
by: Dominici, Gabriele, et al.
Published: (2024)

On the interplay of Explainability, Privacy and Predictive Performance with Explanation-assisted Model Extraction
by: Ezzeddine, Fatima, et al.
Published: (2025)

Federated Learning with Profile Mapping under Distribution Shifts and Drifts
by: Li, Mohan, et al.
Published: (2026)

Is General-Purpose AI Reasoning Sensitive to Data-Induced Cognitive Biases? Dynamic Benchmarking on Typical Software Engineering Dilemmas
by: Sovrano, Francesco, et al.
Published: (2025)

A User Study Evaluating Argumentative Explanations in Diagnostic Decision Support
by: Liedeker, Felix, et al.
Published: (2025)

Interpretable Hierarchical Concept Reasoning through Attention-Guided Graph Learning
by: Debot, David, et al.
Published: (2025)

Rule-Bottleneck Reinforcement Learning: Joint Explanation and Decision Optimization for Resource Allocation with Language Agents
by: Tec, Mauricio, et al.
Published: (2025)

Evaluating Counterfactual Explanation Methods on Incomplete Inputs
by: Leofante, Francesco, et al.
Published: (2026)

A Comparative User Evaluation of XRL Explanations using Goal Identification
by: Towers, Mark, et al.
Published: (2025)

Beyond One-Size-Fits-All: Adapting Counterfactual Explanations to User Objectives
by: Mastromichalakis, Orfeas Menis, et al.
Published: (2024)

Digital Socrates: Evaluating LLMs through Explanation Critiques
by: Gu, Yuling, et al.
Published: (2023)

Explanation User Interfaces: A Systematic Literature Review
by: Cappuccio, Eleonora, et al.
Published: (2025)

ERIS: Enhancing Privacy and Scalability in Federated Learning via Federated Shard Aggregation
by: Fenoglio, Dario, et al.
Published: (2026)

Visual Evaluative AI: A Hypothesis-Driven Tool with Concept-Based Explanations and Weight of Evidence
by: Le, Thao, et al.
Published: (2024)

Textual Explanations and Their Evaluations for Reinforcement Learning Policy
by: Terra, Ahmad, et al.
Published: (2026)

Time2Vec Transformer for Robust Gesture Recognition from Low-Density sEMG
by: Hristov, Blagoj, et al.
Published: (2026)

Implicit Intelligence -- Evaluating Agents on What Users Don't Say
by: Sirdeshmukh, Ved, et al.
Published: (2026)

M2-PALE: A Framework for Explaining Multi-Agent MCTS--Minimax Hybrids via Process Mining and LLMs
by: Qian, Yiyu, et al.
Published: (2026)

Automatic Evaluation of Healthcare LLMs Beyond Question-Answering
by: Arias-Duart, Anna, et al.
Published: (2025)

LLMs Should Not Yet Be Credited with Decision Explanation
by: Wang, Wenshuo
Published: (2026)

A Taxonomy for Design and Evaluation of Prompt-Based Natural Language Explanations
by: Nejadgholi, Isar, et al.
Published: (2025)

FLUX: Efficient Descriptor-Driven Clustered Federated Learning under Arbitrary Distribution Shifts
by: Fenoglio, Dario, et al.
Published: (2025)

Avoiding Leakage Poisoning: Concept Interventions Under Distribution Shifts
by: Zarlenga, Mateo Espinosa, et al.
Published: (2025)

Local Explanations and Self-Explanations for Assessing Faithfulness in black-box LLMs
by: Fragkathoulas, Christos, et al.
Published: (2024)

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

R-ConstraintBench: Evaluating LLMs on NP-Complete Scheduling
by: Jain, Raj, et al.
Published: (2025)

Beyond Ethical Alignment: Evaluating LLMs as Artificial Moral Assistants
by: Galatolo, Alessio, et al.
Published: (2025)

Towards Piece-by-Piece Explanations for Chess Positions with SHAP
by: Spinnato, Francesco
Published: (2025)

Illocutionary Explanation Planning for Source-Faithful Explanations in Retrieval-Augmented Language Models
by: Sovrano, Francesco, et al.
Published: (2026)