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From anecdotal evidence to quantitative evaluation methods: A systematic review on evaluating explainable ai
The rising popularity of explainable artificial intelligence (XAI) to understand high-performing
black boxes raised the question of how to evaluate explanations of machine learning (ML) …
black boxes raised the question of how to evaluate explanations of machine learning (ML) …
Interpretable and explainable machine learning: A methods‐centric overview with concrete examples
Interpretability and explainability are crucial for machine learning (ML) and statistical
applications in medicine, economics, law, and natural sciences and form an essential …
applications in medicine, economics, law, and natural sciences and form an essential …
[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 …
Distilling step-by-step! outperforming larger language models with less training data and smaller model sizes
Deploying large language models (LLMs) is challenging because they are memory
inefficient and compute-intensive for practical applications. In reaction, researchers train …
inefficient and compute-intensive for practical applications. In reaction, researchers train …
Foundational challenges in assuring alignment and safety of large language models
This work identifies 18 foundational challenges in assuring the alignment and safety of large
language models (LLMs). These challenges are organized into three different categories …
language models (LLMs). These challenges are organized into three different categories …
Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems
Deep learning has been shown to be an effective tool in solving partial differential equations
(PDEs) through physics-informed neural networks (PINNs). PINNs embed the PDE residual …
(PDEs) through physics-informed neural networks (PINNs). PINNs embed the PDE residual …
Explainability of artificial intelligence methods, applications and challenges: A comprehensive survey
The continuous advancement of Artificial Intelligence (AI) has been revolutionizing the
strategy of decision-making in different life domains. Regardless of this achievement, AI …
strategy of decision-making in different life domains. Regardless of this achievement, AI …
Underspecification presents challenges for credibility in modern machine learning
Machine learning (ML) systems often exhibit unexpectedly poor behavior when they are
deployed in real-world domains. We identify underspecification in ML pipelines as a key …
deployed in real-world domains. We identify underspecification in ML pipelines as a key …
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 …
Explaining deep neural networks and beyond: A review of methods and applications
With the broader and highly successful usage of machine learning (ML) in industry and the
sciences, there has been a growing demand for explainable artificial intelligence (XAI) …
sciences, there has been a growing demand for explainable artificial intelligence (XAI) …