Feb. 25, 2022, 2:11 a.m. | Marc-André Zöller, Waldemar Titov, Thomas Schlegel, Marco F. Huber

cs.LG updates on arXiv.org arxiv.org

In the last ten years, various automated machine learning (AutoML) systems
have been proposed to build end-to-end machine learning (ML) pipelines with
minimal human interaction. Even though such automatically synthesized ML
pipelines are able to achieve a competitive performance, recent studies have
shown that users do not trust models constructed by AutoML due to missing
transparency of AutoML systems and missing explanations for the constructed ML
pipelines. In a requirements analysis study with 26 domain experts, data
scientists, and AutoML …

analytics arxiv automated machine learning learning machine machine learning tool trust visual analytics

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