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

We propose a novel data-driven framework for algorithmic recourse that offersusers interventions to change their predicted outcome. Existing approaches tocompute recourse find a set of points that satisfy some desiderata -- e.g. anintervention in the underlying causal graph, or minimizing a cost function.Satisfying these criteria, however, requires extensive knowledge of theunderlying model structure, often an unrealistic amount of information inseveral domains. We propose a data-driven, computationally efficient approachto computing algorithmic recourse. We do so by suggesting directions in thedata manifold that users can take to change their predicted outcome. We presentStepwise Explainable Paths (StEP), an axiomatically justified framework tocompute direction-based algorithmic recourse. We offer a thorough empirical andtheoretical investigation of StEP. StEP offers provable privacy and robustnessguarantees, and outperforms the state-of-the-art on several establishedrecourse desiderata.

Simple Steps to Success: Axiomatics of Distance-Based Algorithmic Recourse