PNNL

Abstract

The fields of machine learning and deep learning have grown rapidly in recent years. With the speed of advancement, it is virtually impossible for a single data scientist to both be on the leading edge of conducting sound, systematic research and maintain awareness of the most efficient methods and practices for developing and deploying deep learning solutions. This study applies human factors methods to the field of machine learning to address these difficulties. Using semi-structured interviews with data scientists at a national laboratory, we sought to understand the process used when working with machine learning models, the challenges encountered, and the ways that human factors might contribute to addressing those challenges. Results of the interviews were analyzed to create a generalized process of working with machine learning models. Issues encountered during each process step are described. Finally, recommendations and areas for collaboration between data scientists and human factors experts are provided, with the goal of creating better tools, knowledge, and guidance for machine learning scientists.