The COVID-19 pandemic has exposed an interconnected and tightly coupled globalized
world in rapid change. This article sets the scientific stage for understanding and responding …

This paper investigates the direct risks and harms associated with modern text-to-image
generative models, such as DALL-E and Midjourney, through a comprehensive literature …

As machine learning methods are deployed in real-world settings such as healthcare, legal
systems, and social science, it is crucial to recognize how they shape social biases and …

Automated decision making and predictive analytics through artificial intelligence, in
combination with rapid progress in technologies such as sensor technology and robotics are …

Large language models have led to remarkable progress on many NLP tasks, and
researchers are turning to ever-larger text corpora to train them. Some of the largest corpora …

Pretrained neural language models (LMs) are prone to generating racist, sexist, or otherwise
toxic language which hinders their safe deployment. We investigate the extent to which …

We survey 146 papers analyzing" bias" in NLP systems, finding that their motivations are
often vague, inconsistent, and lacking in normative reasoning, despite the fact that …

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

As government pressure on major technology companies builds, both firms and legislators
are searching for technical solutions to difficult platform governance puzzles such as hate …

Large language models (LM) generate remarkably fluent text and can be efficiently adapted
across NLP tasks. Measuring and guaranteeing the quality of generated text in terms of …