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

Machine learning (ML) is playing an increasing role in decision-making tasksthat directly affect individuals, e.g., loan approvals, or job applicantscreening. Significant concerns arise that, without special provisions,individuals from under-privileged backgrounds may not get equitable access toservices and opportunities. Existing research studies fairness with respect toprotected attributes such as gender, race or income, but the impact of locationdata on fairness has been largely overlooked. With the widespread adoption ofmobile apps, geospatial attributes are increasingly used in ML, and theirpotential to introduce unfair bias is significant, given their high correlationwith protected attributes. We propose techniques to mitigate location bias inmachine learning. Specifically, we consider the issue of miscalibration whendealing with geospatial attributes. We focus on spatial group fairness and wepropose a spatial indexing algorithm that accounts for fairness. Our KD-treeinspired approach significantly improves fairness while maintaining highlearning accuracy, as shown by extensive experimental results on real data.

Fair Spatial Indexing: A paradigm for Group Spatial Fairness