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 increasing complexity of AI systems has led to the growth of the field ofexplainable AI (XAI), which aims to provide explanations and justifications forthe outputs of AI algorithms. These methods mainly focus on feature importanceand identifying changes that can be made to achieve a desired outcome.Researchers have identified desired properties for XAI methods, such asplausibility, sparsity, causality, low run-time, etc. The objective of thisstudy is to conduct a review of existing XAI research and present aclassification of XAI methods. The study also aims to connect XAI users withthe appropriate method and relate desired properties to current XAI approaches.The outcome of this study will be a clear strategy that outlines how to choosethe right XAI method for a particular goal and user and provide a personalizedexplanation for users.

Who wants what and how: a Mapping Function for Explainable Artificial Intelligence