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Explainable artificial intelligence

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.

Papers

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Showing 1–50 of 971 papers

TitleStatusHype
Explainable Artificial Intelligence in Biomedical Image Analysis: A Comprehensive Survey—0
From Motion to Meaning: Biomechanics-Informed Neural Network for Explainable Cardiovascular Disease Identification—0
Towards Transparent AI: A Survey on Explainable Large Language Models—0
IXAII: An Interactive Explainable Artificial Intelligence Interface for Decision Support Systems—0
Communicating Smartly in the Molecular Domain: Neural Networks in the Internet of Bio-Nano ThingsCode0
Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach—0
Toward the Explainability of Protein Language Models for Sequence Design—0
When concept-based XAI is imprecise: Do people distinguish between generalisations and misrepresentations?—0
A Systematic Review of User-Centred Evaluation of Explainable AI in Healthcare—0
Unsupervised risk factor identification across cancer types and data modalities via explainable artificial intelligence—0
A Lightweight IDS for Early APT Detection Using a Novel Feature Selection Method—0
midr: Learning from Black-Box Models by Maximum Interpretation DecompositionCode0
Transfer Learning and Explainable AI for Brain Tumor Classification: A Study Using MRI Data from Bangladesh—0
Do Protein Transformers Have Biological Intelligence?Code0
Applying XAI based unsupervised knowledge discovering for Operation modes in a WWTP. A real case: AQUAVALL WWTP—0
TIMING: Temporality-Aware Integrated Gradients for Time Series ExplanationCode1
A Comprehensive Study on Medical Image Segmentation using Deep Neural Networks—0
Recent Advances in Medical Image Classification—0
Explainable-AI powered stock price prediction using time series transformers: A Case Study on BIST100—0
DiCoFlex: Model-agnostic diverse counterfactuals with flexible control—0
Explanation User Interfaces: A Systematic Literature Review—0
From What Ifs to Insights: Counterfactuals in Causal Inference vs. Explainable AI—0
Addressing the Scarcity of Benchmarks for Graph XAICode0
WebXAII: an open-source web framework to study human-XAI interaction—0
Heart2Mind: Human-Centered Contestable Psychiatric Disorder Diagnosis System using Wearable ECG MonitorsCode0
Most General Explanations of Tree Ensembles (Extended Version)—0
PnPXAI: A Universal XAI Framework Providing Automatic Explanations Across Diverse Modalities and ModelsCode2
Financial Fraud Detection Using Explainable AI and Stacking Ensemble Methods—0
SIM-Shapley: A Stable and Computationally Efficient Approach to Shapley Value ApproximationCode0
Tuning for Trustworthiness -- Balancing Performance and Explanation Consistency in Neural Network Optimization—0
Explainable Artificial Intelligence Techniques for Software Development Lifecycle: A Phase-specific Survey—0
Discovering Concept Directions from Diffusion-based Counterfactuals via Latent Clustering—0
Privacy Risks and Preservation Methods in Explainable Artificial Intelligence: A Scoping Review—0
Multimodal Doctor-in-the-Loop: A Clinically-Guided Explainable Framework for Predicting Pathological Response in Non-Small Cell Lung Cancer—0
Machine Learning Meets Transparency in Osteoporosis Risk Assessment: A Comparative Study of ML and Explainability Analysis—0
Explainable AI in Spatial AnalysisCode2
XBreaking: Explainable Artificial Intelligence for Jailbreaking LLMs—0
DiCE-Extended: A Robust Approach to Counterfactual Explanations in Machine Learning—0
ApproXAI: Energy-Efficient Hardware Acceleration of Explainable AI using Approximate Computing—0
Generative and Explainable AI for High-Dimensional Channel EstimationCode0
Explainable Artificial Intelligence techniques for interpretation of food datasets: a review—0
Towards an Evaluation Framework for Explainable Artificial Intelligence Systems for Health and Well-being—0
On Background Bias of Post-Hoc Concept Embeddings in Computer Vision DNNsCode0
Focal Cortical Dysplasia Type II Detection Using Cross Modality Transfer Learning and Grad-CAM in 3D-CNNs for MRI Analysis—0
Am I Being Treated Fairly? A Conceptual Framework for Individuals to Ascertain Fairness—0
Explainable AI-Based Interface System for Weather Forecasting Model—0
Which LIME should I trust? Concepts, Challenges, and Solutions—0
Reinforcing Clinical Decision Support through Multi-Agent Systems and Ethical AI Governance—0
Exploring Energy Landscapes for Minimal Counterfactual Explanations: Applications in Cybersecurity and Beyond—0
Unraveling Pedestrian Fatality Patterns: A Comparative Study with Explainable AI—0
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