Large language models (LLMs) have demonstrated remarkable capabilities across a broad
spectrum of tasks. They have attracted significant attention and been deployed in numerous …

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 …

Pretrained language models, especially masked language models (MLMs) have seen
success across many NLP tasks. However, there is ample evidence that they use the cultural …

Recent years have witnessed remarkable progress made in large language models (LLMs).
Such advancements, while garnering significant attention, have concurrently elicited various …

ChatGPT embarks on a new era of artificial intelligence and will revolutionize the way we
approach intelligent traffic safety systems. This paper begins with a brief introduction about …

Measuring bias is key for better understanding and addressing unfairness in NLP/ML
models. This is often done via fairness metrics, which quantify the differences in a model's …

Models often easily learn biases present in the training data, and their predictions directly
reflect this bias. We analyze gender bias in dialogue data, and examine how this bias is …

The rise of concern around Natural Language Processing (NLP) technologies containing
and perpetuating social biases has led to a rich and rapidly growing area of research …

We propose an analysis in fair learning that preserves the utility of the data while reducing
prediction disparities under the criteria of group sufficiency. We focus on the scenario where …

Machine learning models are trained to find patterns in data. NLP models can inadvertently
learn socially undesirable patterns when training on gender biased text. In this work, we …