Language models (LMs) are becoming the foundation for almost all major language
technologies, but their capabilities, limitations, and risks are not well understood. We present …

Large language models (LLMs) have been shown to be able to perform new tasks based on
a few demonstrations or natural language instructions. While these capabilities have led to …

Large language models (LLMs) represent a major advance in artificial intelligence (AI)
research. However, the widespread use of LLMs is also coupled with significant ethical and …

Background: Many published machine learning studies are irreproducible. Issues with
methodology and not properly accounting for variation introduced by the algorithm …

Understanding the landscape of potential harms from algorithmic systems enables
practitioners to better anticipate consequences of the systems they build. It also supports the …

Current approaches to building general-purpose AI systems tend to produce systems with
both beneficial and harmful capabilities. Further progress in AI development could lead to …

Machine learning (ML) methods are proliferating in scientific research. However, the
adoption of these methods has been accompanied by failures of validity, reproducibility, and …

Research in Fairness, Accountability, Transparency, and Ethics (FATE) 1 has established
many sources and forms of algorithmic harm, in domains as diverse as health care, finance …

Foundation models have rapidly permeated society, catalyzing a wave of generative AI
applications spanning enterprise and consumer-facing contexts. While the societal impact of …

As governments and industry turn to increased use of automated decision systems, it
becomes essential to consider how closely such systems can reproduce human judgment …