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 151175 of 971 papers

TitleStatusHype
Explainable artificial intelligence approaches for brain-computer interfaces: a review and design spaceCode0
Explainable Artificial Intelligence for Dependent Features: Additive Effects of CollinearityCode0
Explainable Data Poison Attacks on Human Emotion Evaluation Systems based on EEG SignalsCode0
CEnt: An Entropy-based Model-agnostic Explainability Framework to Contrast Classifiers' DecisionsCode0
Explainability of Predictive Process Monitoring Results: Can You See My Data Issues?Code0
Explainable AI for Comparative Analysis of Intrusion Detection ModelsCode0
Causal Discovery and Classification Using Lempel-Ziv ComplexityCode0
Explainability in Music Recommender SystemsCode0
Explainability in Practice: Estimating Electrification Rates from Mobile Phone Data in SenegalCode0
An explainable three dimension framework to uncover learning patterns: A unified look in variable sulci recognitionCode0
Cartan moving frames and the data manifoldsCode0
Explainability of Machine Learning Models under Missing DataCode0
Algorithm-Agnostic Explainability for Unsupervised ClusteringCode0
Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex ModelsCode0
Explainable Debugger for Black-box Machine Learning ModelsCode0
ExplainReduce: Summarising local explanations via proxiesCode0
Explainable Anomaly Detection for Industrial Control System CybersecurityCode0
Challenges facing the explainability of age prediction models: case study for two modalitiesCode0
Applying Genetic Programming to Improve Interpretability in Machine Learning ModelsCode0
Explainable Artificial Intelligence and Multicollinearity : A Mini Review of Current ApproachesCode0
Characterizing the contribution of dependent features in XAI methodsCode0
Explainable Artificial Intelligence for Bayesian Neural Networks: Towards trustworthy predictions of ocean dynamicsCode0
cito: An R package for training neural networks using torchCode0
Explainability in Process Outcome Prediction: Guidelines to Obtain Interpretable and Faithful ModelsCode0
CARE: Coherent Actionable Recourse based on Sound Counterfactual ExplanationsCode0
Show:102550
← PrevPage 7 of 39Next →

No leaderboard results yet.