Cover Image

An explainable approach to detect case law on housing and eviction issues within the HUDOC database

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Mohammadi, Mohammad, Wieling, Martijn, Vols, Michel
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Combining topic modelling and citation network analysis to study case law from the European Court on Human Rights on the right to respect for private and family life
by: Mohammadi, M., et al.
Published: (2024)

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

An explainable machine learning approach for energy forecasting at the household level
by: Béraud, Pauline, et al.
Published: (2024)

Illuminate: A novel approach for depression detection with explainable analysis and proactive therapy using prompt engineering
by: Agrawal, Aryan
Published: (2024)

Continual Adversarial Reinforcement Learning (CARL) of False Data Injection detection: forgetting and explainability
by: Aslami, Pooja, et al.
Published: (2024)

Toward Efficient Influence Function: Dropout as a Compression Tool
by: Zhang, Yuchen, et al.
Published: (2025)

Predicting life satisfaction using machine learning and explainable AI
by: Khan, Alif Elham, et al.
Published: (2025)

Leveraging GPT-4o Efficiency for Detecting Rework Anomaly in Business Processes
by: Derakhshan, Mohammad, et al.
Published: (2025)

A multi-criteria approach for selecting an explanation from the set of counterfactuals produced by an ensemble of explainers
by: Stępka, Ignacy, et al.
Published: (2024)

DeepLogit: A sequentially constrained explainable deep learning modeling approach for transport policy analysis
by: Oon, Jeremy, et al.
Published: (2025)

Counterfactual explainability and analysis of variance
by: Gao, Zijun, et al.
Published: (2024)

Disentangled Structural and Featural Representation for Task-Agnostic Graph Valuation
by: Falahati, Ali, et al.
Published: (2024)

Optimal Singular Damage: Efficient LLM Inference in Low Storage Regimes
by: Alipour, Mohammadsajad, et al.
Published: (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
by: Văduva, Alin-Gabriel, et al.
Published: (2026)

The Alignment Game: A Theory of Long-Horizon Alignment Through Recursive Curation
by: Falahati, Ali, et al.
Published: (2025)

Curated Synthetic Data Doesn't Have to Collapse: A Theoretical Study of Generative Retraining with Pluralistic Preferences
by: Falahati, Ali, et al.
Published: (2026)

Class-specific feature selection for classification explainability
by: Aguilar-Ruiz, Jesus S.
Published: (2024)

DTOR: Decision Tree Outlier Regressor to explain anomalies
by: Crupi, Riccardo, et al.
Published: (2024)

Holistic Artificial Intelligence in Medicine; improved performance and explainability
by: Petridis, Periklis, et al.
Published: (2025)

Learning the greatest common divisor: explaining transformer predictions
by: Charton, François
Published: (2023)

Learning Dynamic Selection and Pricing of Out-of-Home Deliveries
by: Akkerman, Fabian, et al.
Published: (2023)

Towards Few-shot Self-explaining Graph Neural Networks
by: Peng, Jingyu, et al.
Published: (2024)

An explainable model to support the decision about the therapy protocol for AML
by: Almeida, Jade M., et al.
Published: (2023)

survex: an R package for explaining machine learning survival models
by: Spytek, Mikołaj, et al.
Published: (2023)

FedMSE: Semi-supervised federated learning approach for IoT network intrusion detection
by: Nguyen, Van Tuan, et al.
Published: (2024)

A constraints-based approach to fully interpretable neural networks for detecting learner behaviors
by: Pinto, Juan D., et al.
Published: (2025)

When LLMs get significantly worse: A statistical approach to detect model degradations
by: Kübler, Jonas, et al.
Published: (2026)

AI Epidemiology: achieving explainable AI through expert oversight patterns
by: Tempest-Walters, Kit
Published: (2025)

ByteStorm: a multi-step data-driven approach for Tropical Cyclones detection and tracking
by: Donno, Davide, et al.
Published: (2025)

User-centric evaluation of explainability of AI with and for humans: a comprehensive empirical study
by: Bobek, Szymon, et al.
Published: (2024)

PruneGCRN: Minimizing and explaining spatio-temporal problems through node pruning
by: García-Sigüenza, Javier, et al.
Published: (2025)

Distill n' Explain: explaining graph neural networks using simple surrogates
by: Pereira, Tamara, et al.
Published: (2023)

Automatic generation of insights from workers' actions in industrial workflows with explainable Machine Learning
by: de Arriba-Pérez, Francisco, et al.
Published: (2024)

CNN-TFT explained by SHAP with multi-head attention weights for time series forecasting
by: Stefenon, Stefano F., et al.
Published: (2025)

Towards Faithful Class-level Self-explainability in Graph Neural Networks by Subgraph Dependencies
by: Liu, Fanzhen, et al.
Published: (2025)

A redescription mining framework for post-hoc explaining and relating deep learning models
by: Mihelčić, Matej, et al.
Published: (2025)

BACON: A fully explainable AI model with graded logic for decision making problems
by: Bai, Haishi, et al.
Published: (2025)

Explaining Large Language Models Decisions Using Shapley Values
by: Mohammadi, Behnam
Published: (2024)

An explainable transformer circuit for compositional generalization
by: Tang, Cheng, et al.
Published: (2025)

A Multisource Fusion Framework for Cryptocurrency Price Movement Prediction
by: Dashtaki, Saeed Mohammadi, et al.
Published: (2024)