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 …

As Artificial Intelligence (AI) continues to advance rapidly, it becomes increasingly important
to consider AI's ethical and societal implications. In this paper, we present a bottom-up …

As new forms of data capture emerge to power new AI applications, questions abound about
the ethical implications of these data collection practices. In this paper, we present clinicians' …

We present the results of a survey fielded in June of 2022 as a lens to examine recent data
reliability issues on Amazon Mechanical Turk. We contrast bad data from this survey with …

Human-centric computer vision (HCCV) data curation practices often neglect privacy and
bias concerns, leading to dataset retractions and unfair models. HCCV datasets constructed …

AI tools are increasingly deployed in community contexts. However, datasets used to
evaluate AI are typically created by developers and annotators outside a given community …

While human-centered approaches to machine learning explore various human roles within
the interaction loop, the notion of Interactive Machine Teaching (IMT) emerged with a focus …

Model-induced distribution shifts (MIDS) occur as previous model outputs pollute new model
training sets over generations of models. This is known as model collapse in the case of …

Data annotation remains the sine qua non of machine learning and AI. Recent empirical
work on data annotation has begun to highlight the importance of rater diversity for fairness …

Recent advancements in general-purpose AI have highlighted the importance of guiding AI
systems towards the intended goals, ethical principles, and values of individuals and …