[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-116861-en":3,"doc-seo-116861-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},116861,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","Opportunities for human factors in machine learning","Machine learning and deep learning progress rapidly, making it difficult for data scientists to keep up with evolving tools, architectures, optimization methods, and deployment practices. This study applies human factors methods to analyze how data scientists work with machine learning models at a National Laboratory, focusing on the workflow, encountered challenges, and opportunities for human factors to contribute. Semi-structured interview results are synthesized into a generalized model-building process, and issues at each step are described, followed by recommendations for collaboration to improve tools, knowledge, and guidance.","TYPE Original Research PUBLISHED 20 April 2023  \nDOI 10. 3389/frai.2023.1130190  \nOPEN ACCESS  \nEDITED BY  \nTheodore Allen,  \nThe Ohio State University, United States  \nREVIEWED BY  \nSayak Roychowdhury,  \nIndian Institute of Technology Kharagpur, India Dominic DiCostanzo,  \nThe Ohio State University, United States  \n*CORRESPONDENCE  \nJessica A. Baweja  \n [Jessica.baweja@pnnl.gov](Jessica.baweja@pnnl.gov)  \nRECEIVED 30 December 2022  \nACCEPTED 03 April 2023  \nPUBLISHED 20 April 2023  \nCITATION  \nBaweja JA, Fallon CK and Je􀀀erson BA (2023) Opportunities for human factors in machine learning. Front. Artif. Intell. 6:1130190 .  \ndoi: 10.3389/frai.2023.1130190  \nCOPYRIGHT  \n© 2023 Baweja, Fallon and Je􀀀erson. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nOpportunities for human factors in machine learning  \nJessica A. Baweja*, Corey K. Fallon and Brett A. Je􀀀erson  \nPacific Northwest National Laboratory, Richland, WA, United States  \nIntroduction: The field of machine learning and its subfield of deep learning have grown rapidly in recent years. With the speed of advancement, it is nearly impossible for data scientists to maintain expert knowledge of cutting-edge techniques. This study applies human factors methods to the field of machine learning to address these di􀀈culties.  \nMethods: 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.  \nResults: Results of the interviews were analyzed to create a generalization of the process of working with machine learning models. Issues encountered during each process step are described.  \nDiscussion: 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.  \nKEYWORDS  \nhuman factors, machine learning, neural networks, data science, artificial intelligence  \nIntroduction  \nData science has grown at an astounding rate in recent years, especially the sub􀀂elds of machine learning. With the speed of advancement, there is a need for data scientists to quickly learn new tools, architectures, techniques, and technologies and to learn to address rapidly changing accompanying problems. It is an ongoing challenge for data scientists to remain informed about the latest model architectures and optimization techniques, to develop engineering methods for appropriately handling the volume of available data (or lack thereof), and to use emerging computational and sensor technologies appropriately. With over 250 deep learning articles being released on arXiv alone each month (where peer review is minimal), it is virtually impossible for a single scientist to both be on the leading edge of conducting sound, systematic research and also maintain awareness of the most e􀀔cient methods and practices for developing and deploying deep learning solutions. It raises the question of how humans can e􀀔ciently conduct meaningful research in this area.  \nHuman factors is de􀀂ned as the study of human interactions with other elements of a system—and is therefore uniquely situated to evaluate, model, and propose some solutions to the ways that data scientists work and the challenges that they face (De Winter and Hancock, 2021) . The understanding provided by a human factors analysis of the machine learning work􀀃ow presents an opportunity to develop new tools, best practi","cbCaikBmhaBc5vsu","https://ap.wps.com/l/cbCaikBmhaBc5vsu","pdf",696683,1,13,"English","en",105,"# Introduction\n## Motivation and background\n## Human factors and relevance to ML workflow\n# Methods\n## Semi-structured interviews with data scientists\n# Results\n## Generalized workflow and step-by-step issues\n# Discussion\n## Recommendations and collaboration opportunities","[{\"question\":\"Why are human factors methods needed in machine learning research and development?\",\"answer\":\"The rapid pace of new techniques makes it hard for individual scientists to maintain expertise and awareness, creating ongoing workflow and practice challenges that human factors can help analyze and improve.\"},{\"question\":\"What research approach does the study use to understand data scientists’ work with ML models?\",\"answer\":\"The study uses semi-structured interviews with data scientists at a National Laboratory to understand the workflow, the challenges encountered, and where human factors could contribute.\"},{\"question\":\"What outputs does the study provide from the interview analysis?\",\"answer\":\"Interview results are analyzed to generalize the process of working with machine learning models, with described issues at each step, and then recommendations for collaboration between data scientists and human factors experts.\"}]","Opportunities for human factors in machine learning | 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