[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124152-en":3,"doc-seo-124152-105":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},124152,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","EXPLAINABLE ARTIFICIAL INTELLIGENCE (XAI) - Enhancing Transparency and Trust in Machine Learning Models","This research reviews explanation and interpretation methods for Explainable Artificial Intelligence (XAI) to improve complex machine learning model interpretability. The study examines how XAI influences user belief and trust in AI systems, while focusing on ethical concerns such as fairness and biasedness in nontransparent models. It also identifies limitations in current XAI techniques, emphasizing opportunities for broader scope, enhancement, and scalability. Key issues requiring further work include standardization, user-centered design, and interdisciplinary strategies.","EXPLAINABLE ARTIFICIAL INTELLIGENCE (XAI): ENHANCING TRANSPARENCY AND TRUST IN MACHINE LEARNING MODELS  \nThulasiram Prasad Pasam  \nNTT DATA, Inc. USA  \nAbstract– This research reviews explanation and interpretation for Explainable Artificial Intelligence (XAI) methods in order to boost complex machine learning model interpretability. The study shows the influence and belief of XAI in users that trust an Artificial Intelligence system and investigates ethical concerns, particularly fairness and biasedness of all the nontransparent models. It discusses the shortfalls related to XAI techniques, putting crucial emphasis on extended scope, enhancement and scalability potential. A number of outstanding issuesespecially in need of further work can involve standardization, user-centered design and interdisciplinary in strategies for improving the practical utility of XAI.  \nKeywords: Ethical implications, Explainable Artificial Intelligence (XAI), Machine learning interpretability, User trust, Scalability.  \nI. Introduction  \nExplainable Artificial Intelligence (XAI) has grown out of the need for improvements in the understandability and explainability of machine learning models. AI systems have grown more complex and there has been increasing concern about trusting these black-box-like systems, with implications of accountability, especially in high-stakes applications. Explainable Artificial Intelligence (XAI) techniques have developed ways to make such models more understandable so that their decisions can be easily explained to human users [1] . Other indirect objectives, such as improved user trust and model performance fairness, are researched regarding various XAI methods. This research performed case study analysis, expert interview, and empirical analysis to identify the importance of XAI toward AI deployment.  \nII. Aims and Objective  \nThe aim of the research is to assess the efficacy of Explainable Artificial Intelligence (XAI) strategies in improving transparency, trust and accountability in machine learning models.  \n● To evaluate the efficiency of several Explainable Artificial Intelligence (XAI) strategies in increasing the interpretability of complicated machine learning models  \n● To determine the effect of XAI approaches on user trust and confidence while dealing with AI systems in real-world settings  \n● To investigate the ethical implications of opaque machine learning models and the way XAI can address concerns of fairness and partiality  \n● To identify the constraints and limits of current XAI approaches and  \nprovide improvements to improve their scalability and applicability  \nIII. Research Questions  \n● What are the best Explainable Artificial Intelligence (XAI) methodologies for improving the interpretability of complicated machine learning models?  \n● How can XAI strategies affect user trust and confidence when dealing with AI systems in real-world applications?  \n● What ethical challenges arise from opaque machine learning models, and the way can XAI handle issues of fairness and bias?  \n● What are the primary limits of existing XAI techniques, and the way can their scalability and applicability be enhanced for wider use?  \nIV. Research rationale  \nThe problem is that most of the sophisticated machine learning models are actually black boxes. Lack of transparency can result in bad decisions in sensitive areas such as health, finance, and law enforcement. Users can mistrust AI systems and be hesitant to use them in the absence of an explanation. The integration of AI technologies into important sectors has raised this issue as one of urgency [2] . The key concerns, such as fairness, accountability and bias in AI models, are raised by today ’s regulatory frameworks in the time of the calls for increased transparency. A solution to this problem has  \nmajor implications for the responsible and ethical deployment of AI models into realworld applications.  \nV. Literature Review  \nEvaluation of Explainable Artific","cbCaijbG1auSzsoF","https://ap.wps.com/l/cbCaijbG1auSzsoF","pdf",379279,1,10,"English","en",105,"# I. Introduction\n# II. Aims and Objective\n# III. Research Questions\n# IV. Research rationale\n# V. Literature Review\n## Evaluation of Explainable Artificial Intelligence (XAI) Strategies for Enhancing Model Interpretability","[{\"question\":\"What problem does Explainable Artificial Intelligence (XAI) address in machine learning models?\",\"answer\":\"XAI addresses the lack of transparency in black-box machine learning models, aiming to improve understandability and explainability for human users. It supports accountability, especially in high-stakes applications.\"},{\"question\":\"Which goals does the research set for evaluating XAI strategies?\",\"answer\":\"The research evaluates XAI strategies for improving model interpretability, assessing their effect on user trust in real-world use, and examining ethical implications such as fairness and bias. It also identifies constraints and limits to improve scalability and applicability.\"},{\"question\":\"What are examples of XAI methods discussed in the literature review?\",\"answer\":\"The review discusses LIME (Local Interpretable Model-agnostic Explanations) and SHAP (Shapley Additive explanations), both aimed at explaining predictions and estimating feature importance. It also mentions attention mechanisms and feature importance ranking approaches in deep learning models.\"}]","EXPLAINABLE ARTIFICIAL INTELLIGENCE (XAI) - Enhancing Transparency and Trust in Machine Learning Models | PDF",1785820759,25,{"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},"explainable-artificial-intelligence-xai-enhancing-transparency-and-trust-in-machine-learning-models","",{"@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/explainable-artificial-intelligence-xai-enhancing-transparency-and-trust-in-machine-learning-models/124152/",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},"What problem does Explainable Artificial Intelligence (XAI) address in machine learning models?","Question",{"text":75,"@type":76},"XAI addresses the lack of transparency in black-box machine learning models, aiming to improve understandability and explainability for human users. It supports accountability, especially in high-stakes applications.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which goals does the research set for evaluating XAI strategies?",{"text":80,"@type":76},"The research evaluates XAI strategies for improving model interpretability, assessing their effect on user trust in real-world use, and examining ethical implications such as fairness and bias. It also identifies constraints and limits to improve scalability and applicability.",{"name":82,"@type":73,"acceptedAnswer":83},"What are examples of XAI methods discussed in the literature review?",{"text":84,"@type":76},"The review discusses LIME (Local Interpretable Model-agnostic Explanations) and SHAP (Shapley Additive explanations), both aimed at explaining predictions and estimating feature importance. 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