[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118409-en":3,"doc-seo-118409-105":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},118409,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","Interpretability, Explainability and Trustworthiness in Machine Learning - Master of Science Thesis","Interpretability and explainability are critical aspects of machine learning models, especially when deployed in high-stakes domains where trustworthiness must be ensured. This thesis studies how interpretability, explainability, and trustworthiness intersect within machine learning systems. A comprehensive literature and methodology review clarifies the need for transparent, user- and expert-friendly models. It analyzes interpretability techniques and evaluation metrics while mapping transparency levels from white-box to black-box models. The work emphasizes interpretability in neural networks and applies it to computer vision, including practical video processing demonstrations and evaluations to confirm real-world applicability.","Master in Business Analytics and Data Science  \nDEPARTMENT OF BUSINESS ADMINISTRATION  \nMaster of Science Thesis  \n“Interpretability, Explainability and Trustworthiness in Machine Learning”  \nBy:  \nEvgenia Stergioula  \nSupervisor:  \nDr. Kaparis Konstantinos, Associate Professor, Department of Business Administration  \nExaminers:  \nDr. Kaparis Konstantinos, Associate Professor, Department of Business Administration Dr. Georgiou Andreas, Professor, Department of Business Administration Dr. Ioannis Konstantaras, Associate Professor, Department of Business Administration  \nThis was submitted as a requirement for obtaining the postgraduate diploma in Business Analytics & Data  \nScience, University of Macedonia.  \nTo my beloved family & close friends.  \nYour endless support and encouragement have been my guiding light.  \nAcknowledgments  \nI would like to express my sincere appreciation to my supervisors, Dr. Kaparis Konstantinos and Dr. Georgiou Andreas, whose unwavering guidance and encouragement were instrumental in the completion of this thesis. Their insightful feedback and support significantly enriched the quality of my research. I am also grateful to Ph.D. student, Konstantinos Tarkasis, for his invaluable contributions and thoughtful suggestions, which enhanced the last part of this dissertation. Lastly, I would like to express my deeply gratitude to my family and friends for their endless support and understanding throughout this journey.  \nAbstract  \nInterpretability and explainability are critical aspects of machine learning models, particularly as they are deployed in sensitive domains where trustworthiness is paramount. This thesis explores the intersection of interpretability, explainability, and trustworthiness in machine learning systems. Through a comprehensive review of existing literature and methodologies, it elucidates the importance of making machine learning models transparent and understandable to end-users and domain experts. The investigation encompasses a comprehensive analysis of interpretability methods and evaluation metrics, unraveling the intricacies of transparency levels ranging from white to black-box models. Special emphasis is placed on interpretability within neural networks, with a particular focus on its application in the realm of computer vision. Furthermore, the thesis culminates in a practical demonstration of interpretability principles applied to video processing and analysis, leveraging insights gleaned from a conference proceeding. Through the implementation and evaluation of interpretability techniques in this context, the efficacy and relevance of interpretability methodologies are underscored, offering valuable insights into their real-world applicability.  \nKeywords: artificial intelligence, explainable artificial intelligence, machine learning, interpretability, explainability, trustworthiness, neural networks, computer vision, video processing, YOLOv7  \nContents  \nACKNOWLEDGMENTS ___________________________________________________III  \nABSTRACT _____________________________________________________________ IV  \nCONTENTS ______________________________________________________________ V  \nLIST OF FIGURES _______________________________________________________ VII  \nLIST OF TABLES _______________________________________________________ VIII  \n1 INTRODUCTION ______________________________________________________ 1  \n1.1 Background and Motivation _____________________________________________ 1  \n1.2 Research Questions and Objectives _______________________________________2  \n1.3 Thesis Layout ________________________________________________________2  \n2 LITERATURE REVIEW _________________________________________________4  \n2.1 Introduction__________________________________________________________4  \n2.2 Overview____________________________________________________________6  \n2.2.1 Interpretability______________________________________________________7  \n2.2.2 Explainability _____________","cbCaio45S9Gyn4gF","https://ap.wps.com/l/cbCaio45S9Gyn4gF","pdf",3922635,1,74,"English","en",105,"# Acknowledgments\n# Abstract\n# Contents\n# List of Figures\n# List of Tables\n# 1 Introduction\n## 1.1 Background and Motivation\n## 1.2 Research Questions and Objectives\n## 1.3 Thesis Layout\n# 2 Literature Review\n## 2.1 Introduction\n## 2.2 Overview\n## 2.3 Interpretability Methods\n## 2.4 Levels of Model Transparency and Interpretability\n## 2.5 Interpretability of Neural Networks and Computer Vision\n# 3 Methodology\n## 3.1 Introduction","[{\"question\":\"Why are interpretability and explainability important in machine learning?\",\"answer\":\"They are essential for building trust when models are used in sensitive or high-stakes domains. This thesis frames interpretability and explainability as foundations for model trustworthiness.\"},{\"question\":\"How does the thesis organize interpretability across transparency levels?\",\"answer\":\"It reviews approaches spanning transparency from white-box to black-box models, and examines how different interpretability levels relate to explainability and trustworthiness.\"},{\"question\":\"What practical application does the thesis include beyond literature review?\",\"answer\":\"It demonstrates interpretability principles through implementation and evaluation in a video processing and analysis context, leveraging insights from a conference proceeding.\"}]","Interpretability, Explainability and Trustworthiness in Machine Learning - Master of Science Thesis | PDF",1785683478,186,{"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},"interpretability-explainability-and-trustworthiness-in-machine-learning-master-of-science-thesis","",{"@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/interpretability-explainability-and-trustworthiness-in-machine-learning-master-of-science-thesis/118409/",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","2026-08-02",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},"Why are interpretability and explainability important in machine learning?","Question",{"text":76,"@type":77},"They are essential for building trust when models are used in sensitive or high-stakes domains. This thesis frames interpretability and explainability as foundations for model trustworthiness.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the thesis organize interpretability across transparency levels?",{"text":81,"@type":77},"It reviews approaches spanning transparency from white-box to black-box models, and examines how different interpretability levels relate to explainability and trustworthiness.",{"name":83,"@type":74,"acceptedAnswer":84},"What practical application does the thesis include beyond literature review?",{"text":85,"@type":77},"It demonstrates interpretability principles through implementation and evaluation in a video processing and analysis context, leveraging insights from a conference proceeding.","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"]