The pervasive presence and wide-ranging variety of artificial intelligence (AI) systems
underscore the necessity for inclusivity and diversity in their design and implementation, to …

We present a novel task and dataset for evaluating the ability of vision and language models
to conduct visio-linguistic compositional reasoning, which we call Winoground. Given two …

State-of-the-art computer vision systems are trained to predict a fixed set of predetermined
object categories. This restricted form of supervision limits their generality and usability since …

AI models are increasingly applied in high-stakes domains like health and conservation.
Data quality carries an elevated significance in high-stakes AI due to its heightened …

Data is a crucial component of machine learning. The field is reliant on data to train, validate,
and test models. With increased technical capabilities, machine learning research has …

In this paper we investigate problematic practices and consequences of large scale vision
datasets (LSVDs). We examine broad issues such as the question of consent and justice as …

In response to growing concerns of bias, discrimination, and unfairness perpetuated by
algorithmic systems, the datasets used to train and evaluate machine learning models have …

Although essential to revealing biased performance, well intentioned attempts at algorithmic
auditing can have effects that may harm the very populations these measures are meant to …

Recent work identified a concerning trend of disproportional gender representation in
research participants in Human–Computer Interaction (HCI). Motivated by the fact that …

Online communities provide spaces for people who are vulnerable and underserved to seek
support and build community, such as LGBTQ+ people. Today, some online community …