[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 …
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 …
[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 …
[HTML][HTML] 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 …
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 …
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 …
[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 …
End: Entangling and disentangling deep representations for bias correction
E Tartaglione, CA Barbano… - Proceedings of the …, 2021 - openaccess.thecvf.com
Artificial neural networks perform state-of-the-art in an ever-growing number of tasks, and
nowadays they are used to solve an incredibly large variety of tasks. There are problems …
nowadays they are used to solve an incredibly large variety of tasks. There are problems …
A case-based interpretable deep learning model for classification of mass lesions in digital mammography
Interpretability in machine learning models is important in high-stakes decisions such as
whether to order a biopsy based on a mammographic exam. Mammography poses …
whether to order a biopsy based on a mammographic exam. Mammography poses …