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

With the rapid development of eXplainable Artificial Intelligence (XAI),perturbation-based XAI algorithms have become quite popular due to theireffectiveness and ease of implementation. The vast majority ofperturbation-based XAI techniques face the challenge of Out-of-Distribution(OoD) data -- an artifact of randomly perturbed data becoming inconsistent withthe original dataset. OoD data leads to the over-confidence problem in modelpredictions, making the existing XAI approaches unreliable. To our bestknowledge, the OoD data problem in perturbation-based XAI algorithms has notbeen adequately addressed in the literature. In this work, we address this OoDdata problem by designing an additional module quantifying the affinity betweenthe perturbed data and the original dataset distribution, which is integratedinto the process of aggregation. Our solution is shown to be compatible withthe most popular perturbation-based XAI algorithms, such as RISE, OCCLUSION,and LIME. Experiments have confirmed that our methods demonstrate a significantimprovement in general cases using both computational and cognitive metrics.Especially in the case of degradation, our proposed approach demonstratesoutstanding performance comparing to baselines. Besides, our solution alsoresolves a fundamental problem with the faithfulness indicator, a commonly usedevaluation metric of XAI algorithms that appears to be sensitive to the OoDissue.

Resisting Out-of-Distribution Data Problem in Perturbation of XAI