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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

In many contexts, it can be useful for domain experts to understand to whatextent predictions made by a machine learning model can be trusted. Inparticular, estimates of trustworthiness can be useful for fraud analysts whoprocess machine learning-generated alerts of fraudulent transactions. In thiswork, we present a case-based reasoning (CBR) approach that provides evidenceon the trustworthiness of a prediction in the form of a visualization ofsimilar previous instances. Different from previous works, we considersimilarity of local post-hoc explanations of predictions and show empiricallythat our visualization can be useful for processing alerts. Furthermore, ourapproach is perceived useful and easy to use by fraud analysts at a major Dutchbank.

Case-Based Reasoning for Assisting Domain Experts in Processing Fraud Alerts of Black-Box Machine Learning Models