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 current literature on AI-advised decision making -- involving explainableAI systems advising human decision makers -- presents a series of inconclusiveand confounding results. To synthesize these findings, we propose a simpletheory that elucidates the frequent failure of AI explanations to engenderappropriate reliance and complementary decision making performance. We argueexplanations are only useful to the extent that they allow a human decisionmaker to verify the correctness of an AI's prediction, in contrast to otherdesiderata, e.g., interpretability or spelling out the AI's reasoning process.Prior studies find in many decision making contexts AI explanations do notfacilitate such verification. Moreover, most tasks fundamentally do not alloweasy verification, regardless of explanation method, limiting the potentialbenefit of any type of explanation. We also compare the objective ofcomplementary performance with that of appropriate reliance, decomposing thelatter into the notions of outcome-graded and strategy-graded reliance.

In Search of Verifiability: Explanations Rarely Enable Complementary Performance in AI-Advised Decision Making