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 231240 of 971 papers

TitleStatusHype
Bounded logit attention: Learning to explain image classifiersCode0
bLIMEy: Surrogate Prediction Explanations Beyond LIMECode0
Improving Deep Neural Network Classification Confidence using Heatmap-based eXplainable AICode0
On Formal Feature Attribution and Its ApproximationCode0
People Attribute Purpose to Autonomous Vehicles When Explaining Their Behavior: Insights from Cognitive Science for Explainable AICode0
PIC-XAI: Post-hoc Image Captioning Explanation using SegmentationCode0
Black Box Model Explanations and the Human Interpretability Expectations -- An Analysis in the Context of Homicide PredictionCode0
POTHER: Patch-Voted Deep Learning-Based Chest X-ray Bias Analysis for COVID-19 DetectionCode0
Detecting mental disorder on social media: a ChatGPT-augmented explainable approachCode0
Acquiring Qualitative Explainable Graphs for Automated Driving Scene InterpretationCode0
Show:102550
← PrevPage 24 of 98Next →

No leaderboard results yet.