AI alignment aims to make AI systems behave in line with human intentions and values. As
AI systems grow more capable, so do risks from misalignment. To provide a comprehensive …

Multimodal machine learning is a vibrant multi-disciplinary research field that aims to design
computer agents with intelligent capabilities such as understanding, reasoning, and learning …

Practitioners increasingly use machine learning (ML) models, yet models have become
more complex and harder to understand. To understand complex models, researchers have …

Transformer, one of the latest technological advances of deep learning, has gained
prevalence in natural language processing or computer vision. Since medical imaging bear …

Abstract Large Language Models (LLMs) have demonstrated remarkable capabilities in
performing complex tasks. Moreover, recent research has shown that incorporating human …

Recent advances in machine learning have enabled the development of next-generation
predictive models for complex computational biology problems, thereby spurring the use of …

Abstract Interpretable Machine Learning (IML) has rapidly advanced in recent years, offering
new opportunities to improve our understanding of the complex Earth system. IML goes …

A critical problem in the field of post hoc explainability is the lack of a common foundational
goal among methods. For example, some methods are motivated by function approximation …

The literature addressing bias and fairness in AI models (fair-AI) is growing at a fast pace,
making it difficult for novel researchers and practitioners to have a bird's-eye view picture of …

Large language models (LLMs) such as ChatGPT have demonstrated superior performance
on a variety of natural language processing (NLP) tasks including sentiment analysis …