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 …

In this paper, we review algorithmic bias in education, discussing the causes of that bias and
reviewing the empirical literature on the specific ways that algorithmic bias is known to have …

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 …

The mixture proportions of pretraining data domains (eg, Wikipedia, books, web text) greatly
affect language model (LM) performance. In this paper, we propose Domain Reweighting …

Selecting a suitable pretraining dataset is crucial for both general-domain (eg, GPT-3) and
domain-specific (eg, Codex) language models (LMs). We formalize this problem as selecting …

Hundreds of millions of people now interact with language models, with uses ranging from
help with writing, to informing hiring decisions. However, these language models are known …

AI is undergoing a paradigm shift with the rise of models (eg, BERT, DALL-E, GPT-3) that are
trained on broad data at scale and are adaptable to a wide range of downstream tasks. We …

Graph neural networks (GNNs) are an emerging research field. This specialized deep
neural network architecture is capable of processing graph structured data and bridges the …

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 …

Automated speech recognition (ASR) systems, which use sophisticated machine-learning
algorithms to convert spoken language to text, have become increasingly widespread …