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

Showing 951971 of 971 papers

TitleStatusHype
Few-Shot Multimodal Explanation for Visual Question AnsweringCode0
Rule Extraction in Unsupervised Anomaly Detection for Model Explainability: Application to OneClass SVMCode0
Doctor XAvIer: Explainable Diagnosis on Physician-Patient Dialogues and XAI EvaluationCode0
Finding the right XAI method -- A Guide for the Evaluation and Ranking of Explainable AI Methods in Climate ScienceCode0
Assessing Fidelity in XAI post-hoc techniques: A Comparative Study with Ground Truth Explanations DatasetsCode0
A Review of Multimodal Explainable Artificial Intelligence: Past, Present and FutureCode0
FitCF: A Framework for Automatic Feature Importance-guided Counterfactual Example GenerationCode0
Applying Genetic Programming to Improve Interpretability in Machine Learning ModelsCode0
CohEx: A Generalized Framework for Cohort ExplanationCode0
Coherent Local Explanations for Mathematical OptimizationCode0
Foiling Explanations in Deep Neural NetworksCode0
For Better or Worse: The Impact of Counterfactual Explanations' Directionality on User Behavior in xAICode0
Cartan moving frames and the data manifoldsCode0
Meta-evaluating stability measures: MAX-Senstivity & AVG-SensitivityCode0
A Co-design Study for Multi-Stakeholder Job Recommender System ExplanationsCode0
Clinical Domain Knowledge-Derived Template Improves Post Hoc AI Explanations in Pneumothorax ClassificationCode0
A novel approach to generate datasets with XAI ground truth to evaluate image modelsCode0
CARE: Coherent Actionable Recourse based on Sound Counterfactual ExplanationsCode0
Mitigating belief projection in explainable artificial intelligence via Bayesian TeachingCode0
TripleTree: A Versatile Interpretable Representation of Black Box Agents and their EnvironmentsCode0
From Flexibility to Manipulation: The Slippery Slope of XAI EvaluationCode0
Show:102550
← PrevPage 20 of 20Next →

No leaderboard results yet.