SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 751–800 of 971 papers

TitleStatusHype
A Transformer variant for multi-step forecasting of water level and hydrometeorological sensitivity analysis based on explainable artificial intelligence technology—0
Attributions Beyond Neural Networks: The Linear Program Case—0
A Turing Test for Transparency—0
Audio-visual cross-modality knowledge transfer for machine learning-based in-situ monitoring in laser additive manufacturing—0
Augmented cross-selling through explainable AI -- a case from energy retailing—0
A Unified Framework for Evaluating the Effectiveness and Enhancing the Transparency of Explainable AI Methods in Real-World Applications—0
A User-Centred Framework for Explainable Artificial Intelligence in Human-Robot Interaction—0
AUTOLYCUS: Exploiting Explainable AI (XAI) for Model Extraction Attacks against Interpretable Models—0
Automated detection of motion artifacts in brain MR images using deep learning and explainable artificial intelligence—0
Automated Explanation Selection for Scientific Discovery—0
Automated facial recognition system using deep learning for pain assessment in adults with cerebral palsy—0
Automated Processing of eXplainable Artificial Intelligence Outputs in Deep Learning Models for Fault Diagnostics of Large Infrastructures—0
Automated Quality Control of Vacuum Insulated Glazing by Convolutional Neural Network Image Classification—0
Automatic Diagnosis of Myocarditis Disease in Cardiac MRI Modality using Deep Transformers and Explainable Artificial Intelligence—0
Automatic explanation of the classification of Spanish legal judgments in jurisdiction-dependent law categories with tree estimators—0
XXAI: Towards eXplicitly eXplainable Artificial Intelligence—0
Backtracking Counterfactuals—0
BayesNAM: Leveraging Inconsistency for Reliable Explanations—0
Benchmark data to study the influence of pre-training on explanation performance in MR image classification—0
Better Model Selection with a new Definition of Feature Importance—0
Beyond Explaining: Opportunities and Challenges of XAI-Based Model Improvement—0
Beyond explaining: XAI-based Adaptive Learning with SHAP Clustering for Energy Consumption Prediction—0
Beyond One-Size-Fits-All: Adapting Counterfactual Explanations to User Objectives—0
Beyond XAI:Obstacles Towards Responsible AI—0
Biomarker Investigation using Multiple Brain Measures from MRI through XAI in Alzheimer's Disease Classification—0
Breaking Down Financial News Impact: A Novel AI Approach with Geometric Hypergraphs—0
Bridging Human Concepts and Computer Vision for Explainable Face Verification—0
BSED: Baseline Shapley-Based Explainable Detector—0
Multihop: Leveraging Complex Models to Learn Accurate Simple Models—0
CACTUS: a Comprehensive Abstraction and Classification Tool for Uncovering Structures—0
CAManim: Animating end-to-end network activation maps—0
Can Explainable AI Explain Unfairness? A Framework for Evaluating Explainable AI—0
Can Requirements Engineering Support Explainable Artificial Intelligence? Towards a User-Centric Approach for Explainability Requirements—0
Carefully choose the baseline: Lessons learned from applying XAI attribution methods for regression tasks in geoscience—0
Case-based Explainability for Random Forest: Prototypes, Critics, Counter-factuals and Semi-factuals—0
CAT: Concept-level backdoor ATtacks for Concept Bottleneck Models—0
Causal Explanations and XAI—0
Causal versus Marginal Shapley Values for Robotic Lever Manipulation Controlled using Deep Reinforcement Learning—0
Challenges and Opportunities in Text Generation Explainability—0
Challenges for cognitive decoding using deep learning methods—0
Challenges in Applying Explainability Methods to Improve the Fairness of NLP Models—0
ChatGPT or Human? Detect and Explain. Explaining Decisions of Machine Learning Model for Detecting Short ChatGPT-generated Text—0
Clash of the Explainers: Argumentation for Context-Appropriate Explanations—0
Classification and Explanation of Distributed Denial-of-Service (DDoS) Attack Detection using Machine Learning and Shapley Additive Explanation (SHAP) Methods—0
Classification of Viral Pneumonia X-ray Images with the Aucmedi Framework—0
Classifying Simulated Gait Impairments using Privacy-preserving Explainable Artificial Intelligence and Mobile Phone Videos—0
CNN-based explanation ensembling for dataset, representation and explanations evaluation—0
A Perspective on Explainable Artificial Intelligence Methods: SHAP and LIME—0
Comparative Analysis of Hyperspectral Image Reconstruction Using Deep Learning for Agricultural and Biological Applications—0
Comparing interpretation methods in mental state decoding analyses with deep learning models—0
Show:102550
← PrevPage 16 of 20Next →

No leaderboard results yet.