[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124157-en":3,"doc-seo-124157-105":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},124157,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Transparency and Interpretability in Cloud-based Machine Learning with Explainable AI - Issue 7","With the increased complexity of machine learning models and their widespread use in cloud applications, interpretability and transparency of decision-making become critical. Explainable AI (XAI) methods aim to illuminate how models work, producing human-understandable explanations that support transparent and accountable choices. The article outlines the role of XAI in cloud-computer environments, reviews current explainability techniques, and evaluates their feasibility and issues in cloud settings. It also examines implications for developers, end-users, and regulatory authorities, and points to future research directions in this fast-growing field.","e-ISSN:2582-7219  \nINTERNATIONAL JOURNAL OF MULTIDISCIPLINARY RESEARCH IN SCIENCE, ENGINEERING AND TECHNOLOGY  \nVolume 7, Issue 7 , July 2024  \nImpact Factor: 7.521  \n6381 907 438  6381 907 438  [ijmrset@gmail.com](ijmrset@gmail.com @ www.ijmrset.com)[ @](ijmrset@gmail.com @ www.ijmrset.com)[ www.ijmrset.com](ijmrset@gmail.com @ www.ijmrset.com)  \nInternational Journal of Multidisciplinary Research in Science, Engineering and Technology (IJMRSET)  \n| ISSN: [2582-7219 |](2582-7219 | www.ijmrset.com | Impact Factor:)[ ](2582-7219 | www.ijmrset.com | Impact Factor:)[www.ijmrset.com](2582-7219 | www.ijmrset.com | Impact Factor:)[ | Impact Factor:](2582-7219 | www.ijmrset.com | Impact Factor:) 7.521| Monthly, Peer Reviewed & Referred Journal|  \n| Volume 7, Issue 7, July 2024 |  \n| DOI:10.15680/IJMRSET.2024.0707002 |  \nTransparency and Interpretability in Cloudbased Machine Learning with Explainable AI  \nDhruvitkumar V. Talati  \nAAMC, Washington, D.C., USA  \nORCID ID: 0009-0005-2916-4054  \nABSTRACT: With the increased complexity of machine learning models and their widespread use in cloud applications, interpretability and transparency of decision-making are the highest priority. Explainable AI (XAI) methods seek to shed light on the inner workings of machine learning models, hence making them more interpretable and enabling users to rely on them. In this article, we explain the importance of XAI in cloud-computer environments, specifically with regards to having interpretable models and explainable decision-making. [1] XAI is the essence of a paradigm shift in cloud-based ML, promoting transparency, accountability, and ethical decision-making. As cloudbased ML keeps becoming mainstream, the need for XAI increases, highlighting the need for continued innovation and cooperation for realizing the full potential of interpretable AI systems. We speak about current techniques for realizing explainability in AI systems and their feasibility and issues in cloud environments. Additionally, we discuss the implications of XAI among different stakeholders such as developers, end-users, and regulatory authorities and identify future research directions in this fast-growing area.  \nKEYWORDS: Explainable AI (XAI), Cloud-based Machine Learning, Interpretable Models, Transparency, Decision Making, Model Interpretability, Cloud Computing, Machine Learning Explainability.  \nI. INTRODUCTION  \nOver the last few years, machine learning (ML) algorithm applications in the cloud have transformed decision-making on data. From predictive analytics to recommendation systems, ML models deployed in the cloud provide unparalleled scalability, accessibility, and efficiency. However, along with the benefit of automation and optimization comes an imminent challenge: the black box nature of the models and transparency of their decisions. As ML algorithms increase increasingly complex, usually black boxes, stakeholders cannot see how these models produce their predictions. Not only does this uninterpretability block understanding but also raises ethical issues, especially in areas where decisions affect people's lives, e.g., healthcare, finance, and criminal justice. [2],[3] Clear ML models also obstruct accountability, entrench biases, and foster distrust among end-users and also among regulatory agencies.  \nAs an answer to these issues, Explainable AI (XAI) became a leading thrust area, which seeks to open up the inner workings of ML models and make their choices understandable to humans. XAI methods provide interpretability by generating explanations of model predictions, thus raising transparency, accountability, and trust. In ML in the cloud, wherein models would presumably get deployed at scale and across applications, the necessity for XAI only increases.  \nCloud computing has transformed the deployment of ML models into scalable, accessible, and cheap. Yet, the blackbox nature of most ML algorithms makes it difficult to understand and interpret their decision","cbCaiezVOxXQVOFA","https://ap.wps.com/l/cbCaiezVOxXQVOFA","pdf",1998469,1,11,"English","en",105,"# I. Introduction\n## The black-box challenge in cloud ML\n## Why XAI is needed\n# II. Interpretability in Machine Learning in Cloud Environments\n## Interpretability approaches in cloud settings\n## Trade-offs and governance concerns","[{\"question\":\"Why is interpretability and transparency a priority for cloud-based machine learning?\",\"answer\":\"As ML models become more complex and are deployed at scale in the cloud, their decisions often function as black boxes. This limits understanding and creates ethical and accountability concerns when outcomes affect people’s lives.\"},{\"question\":\"What is the role of Explainable AI (XAI) in cloud environments?\",\"answer\":\"XAI helps make model decisions understandable by generating explanations for predictions. This increases transparency, accountability, and trust among stakeholders using cloud-deployed models.\"},{\"question\":\"What challenges do XAI methods still face in cloud-based systems?\",\"answer\":\"Even with XAI efforts, issues remain such as scalability, privacy, and finding an effective balance between explanation quality and model complexity. These challenges motivate continued collaborative research.\"}]","Transparency and Interpretability in Cloud-based Machine Learning with Explainable AI - Issue 7 | PDF",1785820784,28,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"transparency-and-interpretability-in-cloud-based-machine-learning-with-explainable-ai-issue-7","",{"@graph":36,"@context":85},[37,54,68],{"@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/transparency-and-interpretability-in-cloud-based-machine-learning-with-explainable-ai-issue-7/124157/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is interpretability and transparency a priority for cloud-based machine learning?","Question",{"text":75,"@type":76},"As ML models become more complex and are deployed at scale in the cloud, their decisions often function as black boxes. This limits understanding and creates ethical and accountability concerns when outcomes affect people’s lives.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is the role of Explainable AI (XAI) in cloud environments?",{"text":80,"@type":76},"XAI helps make model decisions understandable by generating explanations for predictions. This increases transparency, accountability, and trust among stakeholders using cloud-deployed models.",{"name":82,"@type":73,"acceptedAnswer":83},"What challenges do XAI methods still face in cloud-based systems?",{"text":84,"@type":76},"Even with XAI efforts, issues remain such as scalability, privacy, and finding an effective balance between explanation quality and model complexity. 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