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Explainable artificial intelligence
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XAI refers to methods and techniques in the application of artificial intelligence (AI) such that the results of the solution can be understood by humans. It contrasts with the concept of the "black box" in machine learning where even its designers cannot explain why an AI arrived at a specific decision. XAI may be an implementation of the social right to explanation. XAI is relevant even if there is no legal right or regulatory requirement—for example, XAI can improve the user experience of a product or service by helping end users trust that the AI is making good decisions. This way the aim of XAI is to explain what has been done, what is done right now, what will be done next and unveil the information the actions are based on. These characteristics make it possible (i) to confirm existing knowledge (ii) to challenge existing knowledge and (iii) to generate new assumptions.
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From Motion to Meaning: Biomechanics-Informed Neural Network for Explainable Cardiovascular Disease Identification
IXAII: An Interactive Explainable Artificial Intelligence Interface for Decision Support Systems
Towards Transparent AI: A Survey on Explainable Large Language Models
Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach
Communicating Smartly in the Molecular Domain: Neural Networks in the Internet of Bio-Nano Things
Toward the Explainability of Protein Language Models for Sequence Design
When concept-based XAI is imprecise: Do people distinguish between generalisations and misrepresentations?
A Systematic Review of User-Centred Evaluation of Explainable AI in Healthcare
Unsupervised risk factor identification across cancer types and data modalities via explainable artificial intelligence
A Lightweight IDS for Early APT Detection Using a Novel Feature Selection Method
midr: Learning from Black-Box Models by Maximum Interpretation Decomposition
Transfer Learning and Explainable AI for Brain Tumor Classification: A Study Using MRI Data from Bangladesh
Do Protein Transformers Have Biological Intelligence?
Applying XAI based unsupervised knowledge discovering for Operation modes in a WWTP. A real case: AQUAVALL WWTP
Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts
TIMING: Temporality-Aware Integrated Gradients for Time Series Explanation
A Comprehensive Study on Medical Image Segmentation using Deep Neural Networks
Recent Advances in Medical Image Classification
Explainable-AI powered stock price prediction using time series transformers: A Case Study on BIST100
DiCoFlex: Model-agnostic diverse counterfactuals with flexible control
Explanation User Interfaces: A Systematic Literature Review
From What Ifs to Insights: Counterfactuals in Causal Inference vs. Explainable AI
Addressing the Scarcity of Benchmarks for Graph XAI
Most General Explanations of Tree Ensembles (Extended Version)
Heart2Mind: Human-Centered Contestable Psychiatric Disorder Diagnosis System using Wearable ECG Monitors
WebXAII: an open-source web framework to study human-XAI interaction
Financial Fraud Detection Using Explainable AI and Stacking Ensemble Methods
PnPXAI: A Universal XAI Framework Providing Automatic Explanations Across Diverse Modalities and Models
SIM-Shapley: A Stable and Computationally Efficient Approach to Shapley Value Approximation
Tuning for Trustworthiness -- Balancing Performance and Explanation Consistency in Neural Network Optimization
Explainable Artificial Intelligence Techniques for Software Development Lifecycle: A Phase-specific Survey
Discovering Concept Directions from Diffusion-based Counterfactuals via Latent Clustering
Privacy Risks and Preservation Methods in Explainable Artificial Intelligence: A Scoping Review
Multimodal Doctor-in-the-Loop: A Clinically-Guided Explainable Framework for Predicting Pathological Response in Non-Small Cell Lung Cancer
Machine Learning Meets Transparency in Osteoporosis Risk Assessment: A Comparative Study of ML and Explainability Analysis
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