Papers Explainable artificial intelligence
“Explainable artificial intelligence” 태그가 달린 논문 971편 · 필터 해제
Explaining the Unexplainable: A Systematic Review of Explainable AI in Finance
Practitioners and researchers trying to strike a balance between accuracy and transparency center Explainable Artificial Intelligence (XAI) at the junction of finance. This paper offers a thorough overview of the changin…
Explainable artificial intelligenceExplainable Artificial Intelligence (XAI)Feature ImportanceExploring specialization and sensitivity of convolutional neural networks in the context of simultaneous image augmentations
Drawing parallels with the way biological networks are studied, we adapt the treatment--control paradigm to explainable artificial intelligence research and enrich it through multi-parametric input alterations. In this s…
Explainable artificial intelligenceSensitivityILLC: Iterative Layer-by-Layer Compression for Enhancing Structural Faithfulness in SpArX
In the field of Explainable Artificial Intelligence (XAI), argumentative XAI approaches have been proposed to represent the internal reasoning process of deep neural networks in a more transparent way by interpreting hid…
Explainable artificial intelligenceExplainable Artificial Intelligence (XAI)The Role of Deep Learning in Financial Asset Management: A Systematic Review
This review systematically examines deep learning applications in financial asset management. Unlike prior reviews, this study focuses on identifying emerging trends, such as the integration of explainable artificial int…
ArticlesAsset ManagementDeep LearningDeep Reinforcement Learning+4GNN-XAR: A Graph Neural Network for Explainable Activity Recognition in Smart Homes
Sensor-based Human Activity Recognition (HAR) in smart home environments is crucial for several applications, especially in the healthcare domain. The majority of the existing approaches leverage deep learning models. Wh…
Activity RecognitionExplainable artificial intelligenceExplainable Artificial Intelligence (XAI)Graph Neural Network+1Class-Dependent Perturbation Effects in Evaluating Time Series Attributions
As machine learning models become increasingly prevalent in time series applications, Explainable Artificial Intelligence (XAI) methods are essential for understanding their predictions. Within XAI, feature attribution m…
Explainable artificial intelligenceExplainable Artificial Intelligence (XAI)Time SeriesTime Series ClassificationDoctor-in-the-Loop: An Explainable, Multi-View Deep Learning Framework for Predicting Pathological Response in Non-Small Cell Lung Cancer
Non-small cell lung cancer (NSCLC) remains a major global health challenge, with high post-surgical recurrence rates underscoring the need for accurate pathological response predictions to guide personalized treatments. …
Decision MakingExplainable artificial intelligenceExplainable Artificial Intelligence Model for Evaluating Shear Strength Parameters of Municipal Solid Waste Across Diverse Compositional Profiles
Accurate prediction of shear strength parameters in Municipal Solid Waste (MSW) remains a critical challenge in geotechnical engineering due to the heterogeneous nature of waste materials and their temporal evolution thr…
Explainable artificial intelligenceExplainable Artificial Intelligence (XAI)FrictionExplainable AI-Driven Neural Activity Analysis in Parkinsonian Rats under Electrical Stimulation
Parkinson's disease (PD) is a neurodegenerative disorder characterized by motor dysfunction and abnormal neural oscillations. These symptoms can be modulated through electrical stimulation. Traditional neural activity an…
Explainable artificial intelligenceExplainable Artificial Intelligence (XAI)ExplainReduce: Summarising local explanations via proxies
Most commonly used non-linear machine learning methods are closed-box models, uninterpretable to humans. The field of explainable artificial intelligence (XAI) aims to develop tools to examine the inner workings of these…
Explainable artificial intelligenceExplainable Artificial Intelligence (XAI)This looks like what? Challenges and Future Research Directions for Part-Prototype Models
The growing interest in eXplainable Artificial Intelligence (XAI) has prompted research into models with built-in interpretability, the most prominent of which are part-prototype models. Part-Prototype Models (PPMs) make…
Explainable artificial intelligenceExplainable Artificial Intelligence (XAI)SurveyPixleepFlow: A Pixel-Based Lifelog Framework for Predicting Sleep Quality and Stress Level
The analysis of lifelogs can yield valuable insights into an individual's daily life, particularly with regard to their health and well-being. The accurate assessment of quality of life is necessitated by the use of dive…
Explainable artificial intelligenceExplainable Artificial Intelligence (XAI)Sleep QualityFinding Words Associated with DIF: Predicting Differential Item Functioning using LLMs and Explainable AI
We fine-tuned and compared several encoder-based Transformer large language models (LLM) to predict differential item functioning (DIF) from the item text. We then applied explainable artificial intelligence (XAI) method…
Explainable artificial intelligenceExplainable Artificial Intelligence (XAI)FairnessUsing agent-based models and EXplainable Artificial Intelligence (XAI) to simulate social behaviors and policy intervention scenarios: A case study of private well users in Ireland
Around 50 percent of Irelands rural population relies on unregulated private wells vulnerable to agricultural runoff and untreated wastewater. High national rates of Shiga toxin-producing Escherichia coli (STEC) and othe…
Explainable artificial intelligenceExplainable Artificial Intelligence (XAI)Coherent Local Explanations for Mathematical Optimization
The surge of explainable artificial intelligence methods seeks to enhance transparency and explainability in machine learning models. At the same time, there is a growing demand for explaining decisions taken through com…
Explainable artificial intelligenceA Wearable Device Dataset for Mental Health Assessment Using Laser Doppler Flowmetry and Fluorescence Spectroscopy Sensors
In this study, we introduce a novel method to predict mental health by building machine learning models for a non-invasive wearable device equipped with Laser Doppler Flowmetry (LDF) and Fluorescence Spectroscopy (FS) se…
Explainable artificial intelligenceExplainable Artificial Intelligence (XAI)Uncertainty Quantification of Wind Gust Predictions in the Northeast US: An Evidential Neural Network and Explainable Artificial Intelligence Approach
Machine learning has shown promise in reducing bias in numerical weather model predictions of wind gusts. Yet, they underperform to predict high gusts even with additional observations due to the right-skewed distributio…
Explainable artificial intelligenceExplainable Artificial Intelligence (XAI)Prediction IntervalsUncertainty QuantificationConcept-Based Explainable Artificial Intelligence: Metrics and Benchmarks
Concept-based explanation methods, such as concept bottleneck models (CBMs), aim to improve the interpretability of machine learning models by linking their decisions to human-understandable concepts, under the critical …
Explainable artificial intelligenceTowards Transparent and Accurate Diabetes Prediction Using Machine Learning and Explainable Artificial Intelligence
Diabetes mellitus (DM) is a global health issue of significance that must be diagnosed as early as possible and managed well. This study presents a framework for diabetes prediction using Machine Learning (ML) models, co…
Diabetes PredictionExplainable artificial intelligenceExplainable Artificial Intelligence (XAI)Is Conversational XAI All You Need? Human-AI Decision Making With a Conversational XAI Assistant
Explainable artificial intelligence (XAI) methods are being proposed to help interpret and understand how AI systems reach specific predictions. Inspired by prior work on conversational user interfaces, we argue that aug…
AllDecision MakingExplainable artificial intelligenceExplainable Artificial Intelligence (XAI)+3