Explainable Artificial Intelligence (XAI)
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Benchmarks
ADNI
Most implemented
RISE: Randomized Input Sampling for Explanation of Black-box Models
Proposed Guidelines for the Responsible Use of Explainable Machine Learning
Software for Dataset-wide XAI: From Local Explanations to Global Insights with Zennit, CoRelAy, and ViRelAy
Explanations Based on Item Response Theory (eXirt): A Model-Specific Method to Explain Tree-Ensemble Model in Trust Perspective
AudioMNIST: Exploring Explainable Artificial Intelligence for Audio Analysis on a Simple Benchmark
Time series saliency maps: explaining models across multiple domains
Papers
NeuroXAI: Adaptive, robust, explainable surrogate framework for determination of channel importance in EEG application
Electroencephalogram (EEG)-based applications often require numerous channels to achieve high performance, which limits their widespread use. Various channel selection methods have been proposed to identify minimum EEG c…
channel selectionEEGElectroencephalogram (EEG)Explainable Artificial Intelligence (XAI)+1Explainable Artificial Intelligence in Biomedical Image Analysis: A Comprehensive Survey
Explainable artificial intelligence (XAI) has become increasingly important in biomedical image analysis to promote transparency, trust, and clinical adoption of DL models. While several surveys have reviewed XAI techniq…
Explainable artificial intelligenceExplainable Artificial Intelligence (XAI)SurveyCan "consciousness" be observed from large language model (LLM) internal states? Dissecting LLM representations obtained from Theory of Mind test with Integrated Information Theory and Span Representation analysis
Integrated Information Theory (IIT) provides a quantitative framework for explaining consciousness phenomenon, positing that conscious systems comprise elements integrated through causal properties. We apply IIT 3.0 and …
Explainable Artificial Intelligence (XAI)Interpretable Machine LearningLanguage ModelingLanguage Modelling+1Towards Transparent AI: A Survey on Explainable Large Language Models
Large Language Models (LLMs) have played a pivotal role in advancing Artificial Intelligence (AI). However, despite their achievements, LLMs often struggle to explain their decision-making processes, making them a 'black…
DecoderExplainable artificial intelligenceExplainable Artificial Intelligence (XAI)SurveyCommunicating Smartly in the Molecular Domain: Neural Networks in the Internet of Bio-Nano Things
Recent developments in the Internet of Bio-Nano Things (IoBNT) are laying the groundwork for innovative applications across the healthcare sector. Nanodevices designed to operate within the body, managed remotely via the…
Dataset GenerationExplainable artificial intelligenceExplainable Artificial Intelligence (XAI)Toward the Explainability of Protein Language Models for Sequence Design
Transformer-based language models excel in a variety of protein-science tasks that range from structure prediction to the design of functional enzymes. However, these models operate as black boxes, and their underlying w…
Explainable artificial intelligenceExplainable Artificial Intelligence (XAI)Protein Design