Abstract Evaluation practices in natural language generation (NLG) have many known flaws,
but improved evaluation approaches are rarely widely adopted. This issue has become …

Abstract We introduce the Segment Anything (SA) project: a new task, model, and dataset for
image segmentation. Using our efficient model in a data collection loop, we built the largest …

Generative AI systems across modalities, ranging from text, image, audio, and video, have
broad social impacts, but there exists no official standard for means of evaluating those …

Computer vision models have known performance disparities across attributes such as
gender and skin tone. This means during tasks such as classification and detection, model …

This paper presents a community-centered study of cultural limitations of text-to-image (T2I)
models in the South Asian context. We theorize these failures using scholarship on …

Technology companies continue to invest in efforts to incorporate responsibility in their
Artificial Intelligence (AI) advancements, while efforts to audit and regulate AI systems …

Social stereotypes negatively impact individuals' judgments about different groups and may
have a critical role in understanding language directed toward marginalized groups. Here …

Calls for representation in artificial intelligence (AI) and machine learning (ML) are
widespread, with" representation" or" representativeness" generally understood to be both …

The labels used to train machine learning (ML) models are of paramount importance.
Typically for ML classification tasks, datasets contain hard labels, yet learning using soft …

Large language models (LLMs) trained on real-world data can inadvertently reflect harmful
societal biases, particularly toward historically marginalized communities. While previous …