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 226–250 of 971 papers

TitleStatusHype
Attributions Beyond Neural Networks: The Linear Program Case—0
Deep Learning for predicting rate-induced tipping—0
Deep Learning, Natural Language Processing, and Explainable Artificial Intelligence in the Biomedical Domain—0
Deep Learning Reproducibility and Explainable AI (XAI)—0
Deep Unsupervised Identification of Selected SNPs between Adapted Populations on Pool-seq Data—0
Adapting the Biological SSVEP Response to Artificial Neural Networks—0
Advancing Nearest Neighbor Explanation-by-Example with Critical Classification Regions—0
Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma—0
Designing ECG Monitoring Healthcare System with Federated Transfer Learning and Explainable AI—0
Designing explainable artificial intelligence with active inference: A framework for transparent introspection and decision-making—0
Designing Explainable Predictive Machine Learning Artifacts: Methodology and Practical Demonstration—0
Detecting AI-Generated Text in Educational Content: Leveraging Machine Learning and Explainable AI for Academic Integrity—0
Detecting Anomalies in Blockchain Transactions using Machine Learning Classifiers and Explainability Analysis—0
AUTOLYCUS: Exploiting Explainable AI (XAI) for Model Extraction Attacks against Interpretable Models—0
Détection d'objets célestes dans des images astronomiques par IA explicable—0
Developing Guidelines for Functionally-Grounded Evaluation of Explainable Artificial Intelligence using Tabular Data—0
Diagnosis of Acute Poisoning Using Explainable Artificial Intelligence—0
Diagnosis of Paratuberculosis in Histopathological Images Based on Explainable Artificial Intelligence and Deep Learning—0
DiCE-Extended: A Robust Approach to Counterfactual Explanations in Machine Learning—0
DiCoFlex: Model-agnostic diverse counterfactuals with flexible control—0
Directions for Explainable Knowledge-Enabled Systems—0
Eclectic Rule Extraction for Explainability of Deep Neural Network based Intrusion Detection Systems—0
Disagreement amongst counterfactual explanations: How transparency can be deceptive—0
Discovering Concept Directions from Diffusion-based Counterfactuals via Latent Clustering—0
ApproXAI: Energy-Efficient Hardware Acceleration of Explainable AI using Approximate Computing—0
Show:102550
← PrevPage 10 of 39Next →

No leaderboard results yet.