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

These notes shows how to do inference on the Demographic Parity (DP) metric.Although the metric is a complex statistic involving min and max computations,we propose a smooth approximation of those functions and derive its asymptoticdistribution. The limit of these approximations and their gradients converge tothose of the true max and min functions, wherever they exist. More importantly,when the true max and min functions are not differentiable, the approximationsstill are, and they provide valid asymptotic inference everywhere in thedomain. We conclude with some directions on how to compute confidence intervalsfor DP, how to test if it is under 0.8 (the U.S. Equal Employment OpportunityCommission fairness threshold), and how to do inference in an A/B test.

Identification and Inference with Min-over-max Estimators for the Measurement of Labor Market Fairness