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

Counterfactual explanations are a popular type of explanation for making theoutcomes of a decision making system transparent to the user. Counterfactualexplanations tell the user what to do in order to change the outcome of thesystem in a desirable way. However, it was recently discovered that therecommendations of what to do can differ significantly in their complexitybetween protected groups of individuals. Providing more difficultrecommendations of actions to one group leads to a disadvantage of this groupcompared to other groups.  In this work we propose a model-agnostic method for computing counterfactualexplanations that do not differ significantly in their complexity betweenprotected groups.

"Explain it in the Same Way!" -- Model-Agnostic Group Fairness of Counterfactual Explanations