Process-based modelling offers interpretability and physical consistency in many domains of
geosciences but struggles to leverage large datasets efficiently. Machine-learning methods …

Given the tremendous potential and influence of artificial intelligence (AI) and algorithmic
decision‐making (DM), these systems have found wide‐ranging applications across diverse …

Recently, the use of machine learning in meteorology has increased greatly. While many
machine learning methods are not new, university classes on machine learning are largely …

Benchmark datasets and benchmark problems have been a key aspect for the success of
modern machine learning applications in many scientific domains. Consequently, an active …

Artificial-intelligence tools are transforming data-driven science—better ethical standards
and more robust data curation are needed to fuel the boom and prevent a bust. Components …

Demands to manage the risks of artificial intelligence (AI) are growing. These demands and
the government standards arising from them both call for trustworthy AI. In response, we …

This paper provides a systematic account of how artificial intelligence (AI) technologies
could harm nonhuman animals and explains why animal harms, often neglected in AI ethics …

Climate variability and weather phenomena can cause extremes and pose significant risk to
society and ecosystems, making continued advances in our physical understanding of such …

This paper reframes algorithmic systems as intimately connected to and part of social and
ecological systems, and proposes a first-of-its-kind methodology for environmental justice …

Deep learning (DL)-assisted inverse mapping has shown promise in hydrological model
calibration by directly estimating parameters from observations. However, the increasing …