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[HTML][HTML] Survey of explainable artificial intelligence techniques for biomedical imaging with deep neural networks
Artificial Intelligence (AI) techniques of deep learning have revolutionized the disease
diagnosis with their outstanding image classification performance. In spite of the outstanding …
diagnosis with their outstanding image classification performance. In spite of the outstanding …
[HTML][HTML] Beyond explaining: Opportunities and challenges of XAI-based model improvement
Abstract Explainable Artificial Intelligence (XAI) is an emerging research field bringing
transparency to highly complex and opaque machine learning (ML) models. Despite the …
transparency to highly complex and opaque machine learning (ML) models. Despite the …
[HTML][HTML] Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence
Artificial intelligence (AI) is currently being utilized in a wide range of sophisticated
applications, but the outcomes of many AI models are challenging to comprehend and trust …
applications, but the outcomes of many AI models are challenging to comprehend and trust …
Can language models learn from explanations in context?
Language Models (LMs) can perform new tasks by adapting to a few in-context examples.
For humans, explanations that connect examples to task principles can improve learning …
For humans, explanations that connect examples to task principles can improve learning …
Interpretable machine learning: Fundamental principles and 10 grand challenges
Interpretability in machine learning (ML) is crucial for high stakes decisions and
troubleshooting. In this work, we provide fundamental principles for interpretable ML, and …
troubleshooting. In this work, we provide fundamental principles for interpretable ML, and …
From attribution maps to human-understandable explanations through concept relevance propagation
The field of explainable artificial intelligence (XAI) aims to bring transparency to today's
powerful but opaque deep learning models. While local XAI methods explain individual …
powerful but opaque deep learning models. While local XAI methods explain individual …
Large pre-trained language models contain human-like biases of what is right and wrong to do
Artificial writing is permeating our lives due to recent advances in large-scale, transformer-
based language models (LMs) such as BERT, GPT-2 and GPT-3. Using them as pre-trained …
based language models (LMs) such as BERT, GPT-2 and GPT-3. Using them as pre-trained …
A review on explainability in multimodal deep neural nets
Artificial Intelligence techniques powered by deep neural nets have achieved much success
in several application domains, most significantly and notably in the Computer Vision …
in several application domains, most significantly and notably in the Computer Vision …
Knowing about knowing: An illusion of human competence can hinder appropriate reliance on AI systems
The dazzling promises of AI systems to augment humans in various tasks hinge on whether
humans can appropriately rely on them. Recent research has shown that appropriate …
humans can appropriately rely on them. Recent research has shown that appropriate …
Improving performance of deep learning models with axiomatic attribution priors and expected gradients
Recent research has demonstrated that feature attribution methods for deep networks can
themselves be incorporated into training; these attribution priors optimize for a model whose …
themselves be incorporated into training; these attribution priors optimize for a model whose …