[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123151-en":3,"doc-seo-123151-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},123151,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Improving Labour Inspection Efficiency via Machine Learning","Labour inspections performed by governmental agencies enforce decent working conditions and help prevent workplace injuries, but growing workplace diversity makes efficient inspections increasingly challenging. This thesis investigates how machine learning can support inspection tasks by identifying suitable data and ML methods, benchmarking baseline approaches for workplace selection and checklist selection, and testing feature selection to improve performance. It also proposes ML-based approaches for generating new, dynamic checklists with inspector-friendly explanations, then evaluates them using both cross-validation and a field study, showing higher violation detection and improved efficiency in practice.","Doctoral theses at NTNU, 2024:130  \nEirik Lund Flogard  \nImproving Labour Inspection Efficiency via Machine Learning  \nDoctora l thesis  \nNT NU  \nNorwegian University of Science and Technology Thesis for the Degree of Ph ilosophiae Doctor  \nFaculty of Informat ion Technology and Electrical Engineering  \nDepartment of Computer Science  \nEirik Lund Flogard  \nImproving Labour Inspection Efficiency via Machine Learning  \nThesis for the Degree of Philosophiae Doctor Trondheim, June 2024  \nNorwegian University of Science and Technology  \nFaculty of Information Technology and Electrical Engineering Department of Computer Science  \nNTNU  \nNorwegian University of Science and Technology Thesis for the Degree of Philosophiae Doctor  \nFaculty of Information Technology and Electrical Engineering Department of Computer Science  \n© Eirik Lund Flogard  \nISBN 978-82-326-7854-9 (printed ver.)  \nISBN 978-82-326-7853-2 (electronic ver.)  \nISSN 1503-8181 (printed ver.)  \nISSN 2703-8084 (online ver.) Doctoral theses at NTNU, 2024:130 Printed by NTNU Grafisk senter  \nDedicated to my family  \nAbstract  \nLabour inspections are carried out nationwide by governmental agencies in countries that have ratified the International Labour Organization’s Labour Inspection Convention (1947), to enforce decent working conditions and prevent injuries in workplaces. The inspections are conducted by individual inspectors, typically using checklists to survey inspected workplaces for non-compliance to health, environment, and safetyrelated regulations. Carrying out inspections efficiently is becoming increasingly more difficult, as workplaces are becoming more diversified and complex.  \nThis thesis therefore investigates the potential for improving the efficiency of labour inspections via machine learning (ML). Current research into this topic is very limited, so we first investigate what kind of data and ML methods that could be used to support labour inspection tasks. The investigation also involves assessing different baseline ML methods for selecting workplaces for inspection, and for selecting relevant (predefined) labour inspection checklists. We also assess various feature selection methods to maximize the performance of the baselines. Although the initial results are promising, we found that it was difficult to achieve good prediction accuracy even for the best-performing methods.  \nWe also propose ML methods for generating new checklists that could efficiently aid inspectors in identifying working environment violations. One of these methods can be used to generate dynamic checklists, which can be continuously adapted to any new information that surfaces during inspections. Our work also includes proposed explanation approaches to make the dynamic checklists more interpretable for inspectors. We then look further into how such ML-based checklists should be evaluated, by comparing the results from cross-validation performance estimates on existing data to the results from a field study where the checklists are tested in real-world labour inspections. The results of the comparison suggest that the cross-validation performance may not reflect the real-world field performance of the checklists. However, the results from the field study also show that ML-based dynamic checklists significantly increase the number of violations found in the inspections, improving inspection efficiency.  \nThe overall results from this Ph.D. suggest a great potential for using ML in labour inspection tasks. Therefore, our work could promote more independent research on the topic.  \nPreface  \nThis thesis is submitted to satisfy the requirements for the degree of Philosophiae Doctor in computer science at the Faculty of Information Technology and Electrical Engineering at the Norwegian University of Science and Technology. The Ph.D. project was funded by the Norwegian Labour Inspection Authority and the Norwegian Research Council, grant number 299928. The main supervisor for the Ph.D p","cbCaiuZvxD7hOzaX","https://ap.wps.com/l/cbCaiuZvxD7hOzaX","pdf",17343041,1,156,"English","en",105,"# Abstract\n# Preface\n# Acknowledgements\n# Part I Thesis\n## Chapter 1 Introduction\n## Chapter 2 Background","[{\"question\":\"What problem does the thesis address in labour inspection work?\",\"answer\":\"The thesis tackles the difficulty of carrying out labour inspections efficiently as workplaces become more diversified and complex.\"},{\"question\":\"How does the thesis use machine learning to support inspection tasks?\",\"answer\":\"It studies data and ML methods for improving workplace selection and the selection of relevant (predefined) checklists, and it proposes ML methods for generating new dynamic checklists.\"},{\"question\":\"How are the ML-based checklists evaluated, and what do the results show?\",\"answer\":\"The evaluation compares cross-validation performance estimates with a field study in real inspections. Cross-validation may not match field performance, but field results show dynamic ML checklists significantly increase the number of violations found, improving efficiency.\"}]","Improving Labour Inspection Efficiency via Machine Learning | PDF",1785814930,393,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"improving-labour-inspection-efficiency-via-machine-learning","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/improving-labour-inspection-efficiency-via-machine-learning/123151/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-04",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does the thesis address in labour inspection work?","Question",{"text":76,"@type":77},"The thesis tackles the difficulty of carrying out labour inspections efficiently as workplaces become more diversified and complex.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the thesis use machine learning to support inspection tasks?",{"text":81,"@type":77},"It studies data and ML methods for improving workplace selection and the selection of relevant (predefined) checklists, and it proposes ML methods for generating new dynamic checklists.",{"name":83,"@type":74,"acceptedAnswer":84},"How are the ML-based checklists evaluated, and what do the results show?",{"text":85,"@type":77},"The evaluation compares cross-validation performance estimates with a field study in real inspections. Cross-validation may not match field performance, but field results show dynamic ML checklists significantly increase the number of violations found, improving efficiency.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]