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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

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Showing 351–375 of 971 papers

TitleStatusHype
Explainable Multimodal Sentiment Analysis on Bengali Memes—0
Concept-based Explainable Artificial Intelligence: A Survey—0
Explainable artificial intelligence approaches for brain-computer interfaces: a review and design spaceCode0
Locally-Minimal Probabilistic ExplanationsCode0
CAManim: Animating end-to-end network activation maps—0
An Interpretable Deep Learning Approach for Skin Cancer CategorizationCode0
Explainable AI in Grassland Monitoring: Enhancing Model Performance and Domain Adaptability—0
Clash of the Explainers: Argumentation for Context-Appropriate Explanations—0
Anytime Approximate Formal Feature Attribution—0
Explain To Decide: A Human-Centric Review on the Role of Explainable Artificial Intelligence in AI-assisted Decision Making—0
How much informative is your XAI? A decision-making assessment task to objectively measure the goodness of explanations—0
Precision of Individual Shapley Value Explanations—0
AS-XAI: Self-supervised Automatic Semantic Interpretation for CNNCode0
Explainable AI in Diagnosing and Anticipating Leukemia Using Transfer Learning Method—0
Elucidating Discrepancy in Explanations of Predictive Models Developed using EMR—0
Justifiable Artificial Intelligence: Engineering Large Language Models for Legal Applications—0
Local Concept Embeddings for Analysis of Concept Distributions in Vision DNN Feature SpacesCode0
Machine Learning For An Explainable Cost Prediction of Medical Insurance—0
Explaining Deep Learning Models for Age-related Gait Classification based on time series accelerationCode0
Peeking Inside the Schufa Blackbox: Explaining the German Housing Scoring System—0
Détection d'objets célestes dans des images astronomiques par IA explicable—0
Forms of Understanding of XAI-Explanations—0
The Disagreement Problem in Faithfulness Metrics—0
A Hypothesis on Good Practices for AI-based Systems for Financial Time Series Forecasting: Towards Domain-Driven XAI Methods—0
Explaining black boxes with a SMILE: Statistical Model-agnostic Interpretability with Local ExplanationsCode1
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