In this paper, we examine computational approaches for measuring the" fairness" of image
tagging systems, finding that they cluster into five distinct categories, each with its own …

There are increasing expectations that algorithms should behave in a manner that is socially
just. We consider the case of image tagging APIs and their interpretations of people images …

Machine-learned computer vision algorithms for tagging images are increasingly used by
developers and researchers, having become popularized as easy-to-use" cognitive …

Abstract We present the Recognize Anything Model (RAM): a strong foundation model for
image tagging. RAM makes a substantial step for foundation models in computer vision …

The advent of mobile devices and media cloud services has led to the unprecedented
growth of personal photo collections. One of the fundamental problems in managing the …

Massive efforts are made to reduce biases in both data and algorithms to render AI
applications fair. These efforts are propelled by various high-profile cases where biased …

Automatic image annotation has been an important research topic in facilitating large scale
image management and retrieval. Existing methods focus on learning image-tag correlation …

Interpretability and fairness are critical in computer vision and machine learning
applications, in particular when dealing with human outcomes, eg inviting or not inviting for a …

By looking at the politics of classification within machine learning systems, this article
demonstrates why the automated interpretation of images is an inherently social and …

This paper contains images and descriptions that are offensive in nature. Large datasets
underlying much of current machine learning raise serious issues concerning inappropriate …