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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 1–25 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
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