English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Understanding different human attributes and how they affect model behaviormay become a standard need for all model creation and usage, from traditionalcomputer vision tasks to the newest multimodal generative AI systems. Incomputer vision specifically, we have relied on datasets augmented withperceived attribute signals (e.g., gender presentation, skin tone, and age) andbenchmarks enabled by these datasets. Typically labels for these tasks comefrom human annotators. However, annotating attribute signals, especially skintone, is a difficult and subjective task. Perceived skin tone is affected bytechnical factors, like lighting conditions, and social factors that shape anannotator's lived experience. This paper examines the subjectivity of skin toneannotation through a series of annotation experiments using the Monk Skin Tone(MST) scale, a small pool of professional photographers, and a much larger poolof trained crowdsourced annotators. Along with this study we release the MonkSkin Tone Examples (MST-E) dataset, containing 1515 images and 31 videos spreadacross the full MST scale. MST-E is designed to help train human annotators toannotate MST effectively. Our study shows that annotators can reliably annotateskin tone in a way that aligns with an expert in the MST scale, even underchallenging environmental conditions. We also find evidence that annotatorsfrom different geographic regions rely on different mental models of MSTcategories resulting in annotations that systematically vary across regions.Given this, we advise practitioners to use a diverse set of annotators and ahigher replication count for each image when annotating skin tone for fairnessresearch.

Consensus and Subjectivity of Skin Tone Annotation for ML Fairness