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

Text classifiers are applied at scale in the form of one-size-fits-allsolutions. Nevertheless, many studies show that classifiers are biasedregarding different languages and dialects. When measuring and discoveringthese biases, some gaps present themselves and should be addressed. First,``Does language, dialect, and topical content vary across geographicalregions?'' and secondly ``If there are differences across the regions, do theyimpact model performance?''. We introduce a novel dataset called GeoOLID withmore than 14 thousand examples across 15 geographically and demographicallydiverse cities to address these questions. We perform a comprehensive analysisof geographical-related content and their impact on performance disparities ofoffensive language detection models. Overall, we find that current models donot generalize across locations. Likewise, we show that while offensivelanguage models produce false positives on African American English, modelperformance is not correlated with each city's minority population proportions.Warning: This paper contains offensive language.

Measuring Geographic Performance Disparities of Offensive Language Classifiers