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Explaining AI Decisions: Towards Achieving Human-Centered Explainability in Smart Home Environments
Smart home systems are gaining popularity as homeowners strive to enhance their living and working environments while minimizing energy consumption.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
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Peeking Inside the Schufa Blackbox: Explaining the German Housing Scoring System
Explainable Artificial Intelligence is a concept aimed at making complex algorithms transparent to users through a uniform solution.
Unveiling Black-boxes: Explainable Deep Learning Models for Patent Classification
Considering the relevance score, we then generate explanations by visualizing relevant words for the predicted patent class.
From Large Language Models to Knowledge Graphs for Biomarker Discovery in Cancer
Domain experts often rely on most recent knowledge for apprehending and disseminating specific biological processes that help them design strategies for developing prevention and therapeutic decision-making in various disease scenarios.
Arabic Sentiment Analysis with Noisy Deep Explainable Model
This paper proposes an explainable sentiment classification framework for the Arabic language by introducing a noise layer on Bi-Directional Long Short-Term Memory (BiLSTM) and Convolutional Neural Networks (CNN)-BiLSTM models that overcome over-fitting problem.
Textual Entailment Recognition with Semantic Features from Empirical Text Representation
In this paper, we propose a novel approach to identifying the textual entailment relationship between text and hypothesis, thereby introducing a new semantic feature focusing on empirical threshold-based semantic text representation.
Natural Language Inference
Natural Language Understanding
+2
