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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 101–125 of 971 papers

TitleStatusHype
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
Recent Advances in Medical Image Classification—0
A Comprehensive Study on Medical Image Segmentation using Deep Neural Networks—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
Most General Explanations of Tree Ensembles (Extended Version)—0
WebXAII: an open-source web framework to study human-XAI interaction—0
Heart2Mind: Human-Centered Contestable Psychiatric Disorder Diagnosis System using Wearable ECG MonitorsCode0
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
Discovering Concept Directions from Diffusion-based Counterfactuals via Latent Clustering—0
Explainable Artificial Intelligence Techniques for Software Development Lifecycle: A Phase-specific Survey—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
XBreaking: Explainable Artificial Intelligence for Jailbreaking LLMs—0
DiCE-Extended: A Robust Approach to Counterfactual Explanations in Machine Learning—0
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