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 451–475 of 971 papers

TitleStatusHype
Impact of Feature Encoding on Malware Classification Explainability—0
False Sense of Security: Leveraging XAI to Analyze the Reasoning and True Performance of Context-less DGA ClassifiersCode0
A Novel Explainable Artificial Intelligence Model in Image Classification problem—0
On Formal Feature Attribution and Its ApproximationCode0
Human in the AI loop via xAI and Active Learning for Visual Inspection—0
The future of human-centric eXplainable Artificial Intelligence (XAI) is not post-hoc explanations—0
The Effect of Balancing Methods on Model Behavior in Imbalanced Classification ProblemsCode0
Explainability is NOT a Game—0
Classification and Explanation of Distributed Denial-of-Service (DDoS) Attack Detection using Machine Learning and Shapley Additive Explanation (SHAP) Methods—0
Delivering Inflated ExplanationsCode0
Model-contrastive explanations through symbolic reasoningCode1
WBCAtt: A White Blood Cell Dataset Annotated with Detailed Morphological AttributesCode1
Evolutionary approaches to explainable machine learning—0
XAI-TRIS: Non-linear image benchmarks to quantify false positive post-hoc attribution of feature importanceCode0
Evaluation of Popular XAI Applied to Clinical Prediction Models: Can They be Trusted?—0
Benchmark data to study the influence of pre-training on explanation performance in MR image classification—0
Designing Explainable Predictive Machine Learning Artifacts: Methodology and Practical Demonstration—0
ExpPoint-MAE: Better interpretability and performance for self-supervised point cloud transformersCode1
Towards Interpretability in Audio and Visual Affective Machine Learning: A Review—0
iPDP: On Partial Dependence Plots in Dynamic Modeling ScenariosCode0
For Better or Worse: The Impact of Counterfactual Explanations' Directionality on User Behavior in xAICode0
Multimodal Explainable Artificial Intelligence: A Comprehensive Review of Methodological Advances and Future Research Directions—0
HiTZ@Antidote: Argumentation-driven Explainable Artificial Intelligence for Digital MedicineCode0
Strategies to exploit XAI to improve classification systems—0
Explainable Predictive Maintenance—0
Show:102550
← PrevPage 19 of 39Next →

No leaderboard results yet.