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

Automated data-driven decision-making systems are ubiquitous across a widespread of online as well as offline services. These systems, depend onsophisticated learning algorithms and available data, to optimize the servicefunction for decision support assistance. However, there is a growing concernabout the accountability and fairness of the employed models by the fact thatoften the available historic data is intrinsically discriminatory, i.e., theproportion of members sharing one or more sensitive attributes is higher thanthe proportion in the population as a whole when receiving positiveclassification, which leads to a lack of fairness in decision support system. Anumber of fairness-aware learning methods have been proposed to handle thisconcern. However, these methods tackle fairness as a static problem and do nottake the evolution of the underlying stream population into consideration. Inthis paper, we introduce a learning mechanism to design a fair classifier foronline stream based decision-making. Our learning model, FAHT (Fairness-AwareHoeffding Tree), is an extension of the well-known Hoeffding Tree algorithm fordecision tree induction over streams, that also accounts for fairness. Ourexperiments show that our algorithm is able to deal with discrimination instreaming environments, while maintaining a moderate predictive performanceover the stream.

FAHT: An Adaptive Fairness-aware Decision Tree Classifier