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

The impossibility theorem of fairness is a foundational result in thealgorithmic fairness literature. It states that outside of special cases, onecannot exactly and simultaneously satisfy all three common and intuitivedefinitions of fairness - demographic parity, equalized odds, and predictiverate parity. This result has driven most works to focus on solutions for one ortwo of the metrics. Rather than follow suit, in this paper we present aframework that pushes the limits of the impossibility theorem in order tosatisfy all three metrics to the best extent possible. We develop aninteger-programming based approach that can yield a certifiably optimalpost-processing method for simultaneously satisfying multiple fairness criteriaunder small violations. We show experiments demonstrating that ourpost-processor can improve fairness across the different definitionssimultaneously with minimal model performance reduction. We also discussapplications of our framework for model selection and fairness explainability,thereby attempting to answer the question: who's the fairest of them all?

Pushing the limits of fairness impossibility: Who's the fairest of them all?