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

With the widespread adoption of machine learning in the real world, theimpact of the discriminatory bias has attracted attention. In recent years,various methods to mitigate the bias have been proposed. However, most of themhave not considered intersectional bias, which brings unfair situations wherepeople belonging to specific subgroups of a protected group are treated worsewhen multiple sensitive attributes are taken into consideration. To mitigatethis bias, in this paper, we propose a method called One-vs.-One Mitigation byapplying a process of comparison between each pair of subgroups related tosensitive attributes to the fairness-aware machine learning for binaryclassification. We compare our method and the conventional fairness-awarebinary classification methods in comprehensive settings using three approaches(pre-processing, in-processing, and post-processing), six metrics (the ratioand difference of demographic parity, equalized odds, and equal opportunity),and two real-world datasets (Adult and COMPAS). As a result, our methodmitigates the intersectional bias much better than conventional methods in allthe settings. With the result, we open up the potential of fairness-awarebinary classification for solving more realistic problems occurring when thereare multiple sensitive attributes.

One-vs.-One Mitigation of Intersectional Bias: A General Method to Extend Fairness-Aware Binary Classification