There has recently been a surge of work in explanatory artificial intelligence (XAI). This
research area tackles the important problem that complex machines and algorithms often …

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) …

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 …

Deploying large language models (LLMs) is challenging because they are memory
inefficient and compute-intensive for practical applications. In reaction, researchers train …

At the dawn of the fourth industrial revolution, we are witnessing a fast and widespread
adoption of artificial intelligence (AI) in our daily life, which contributes to accelerating the …

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 …

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 …

Attention mechanisms have seen wide adoption in neural NLP models. In addition to
improving predictive performance, these are often touted as affording transparency: models …

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) …

Machine-learning models have demonstrated great success in learning complex patterns
that enable them to make predictions about unobserved data. In addition to using models for …