[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118045-en":3,"doc-seo-118045-105":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},118045,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Navigating the Ethical and Privacy Concerns of Big Data and Machine Learning in Decision Making","Big data analytics and machine learning are increasingly used to make decisions across business, finance, healthcare, and social policy by extracting insights from large, complex datasets. This review summarizes key concepts and workflows from 2017 to 2022, including data collection, preprocessing, feature selection, model training, and evaluation. It also analyzes benefits and constraints, with emphasis on transparency and fairness in decision-making algorithms and on safeguarding individuals’ privacy rights. The paper highlights future research needs for more robust, interpretable models and deeper integration of human judgment with machine learning systems.","Intelligent and Converged Networks ISSN 2708-6240  \n2023, 4(4): 280−295 DOI: 10.23919/ICN.2023.0023  \nNavigating the ethical and privacy concerns of big data and machine learning in decision making  \nHamed Taherdoost*  \nAbstract: In recent years, the fields of big data and machine learning have gained significant attention for their potential to revolutionize decision-making processes. The vast amounts of data generated by various sources can provide valuable insights to inform decisions across a range of domains, from business and finance to healthcare and social policy. Machine learning algorithms enable computers to learn from data and improve their performance overtime, thereby enhancing their ability to make predictions and identify patterns. This article provides a comprehensive overview of how big data and machine learning can improve decision-making processes between 2017–2022. It covers key concepts and techniques involved in these tools, including data collection, data preprocessing, featureselection, model training, and evaluation. The article also discusses the potential benefits and limitations of these tools and explores the ethical and privacy concerns associated with their use. In particular, it highlights the need for transparency and fairness in decision-making algorithms and the importance of protecting individuals’ privacy rights.  \nThe review concludes by highlighting future research opportunities and challenges in this rapidly evolving field, including the need for more robust and interpretable models, as well as the integration of human decision making with machine learning algorithms. Ultimately, this review aims to provide insights for researchers and practitioners seeking to leverage big data and machine learning to improve decision-making processes in various domains.  \nKey words: privacy; big data; machine learning; cybersecurity; decision making  \n1 Introduction  \nThe decision-making process holds significant importance in both personal and professional domains, as it entails selecting the most optimal course of action from a range of available alternatives[1] . The ability to make effective decisions is crucial in attaining personal and organizational objectives, enhancing productivity, and upholding competitiveness in the current dynamic corporate landscape[2] . Conventional methods of decision making rely on subjective judgment, practical knowledge, and non-numerical information to arrive atwell-informed decisions[3] . Nevertheless, the efficacy of these techniques may be constrained in their  \n Hamed Taherdoost is with the Department of Arts, Communications & Social Sciences, University Canada West, Vancouver V6Z O5E, Canada. E-mail: hamed.taherdoost@ [gmail.com](gmail.com).  \n* To whom correspondence should be addressed. Manuscript received: 2023-04-21; accepted: 2023-06-14  \ncapacity to furnish precise and all-encompassing perspectives, particularly when confronted with voluminous datasets or intricate predicaments.  \nBy offering a more data-driven and objective approach to decision making, big data analytics can solve some of the drawbacks of conventional decisionmaking methods[4] . Big data analytics can assist in resolving decision-making issues by improving visibility, arranging and filtering data, locating crucial insights, and producing more precise predictions[5] . While big data analytics can provide valuable insights to inform decision making, there are also several issues that organizations should be aware of when using big data for decision making[6] . Making decisions can be difficult when using big data because of problems with data quality, bias, privacy concerns, complexity, expense, and security hazards. To ensure the efficient and moral application of big data for decision making, it is critical for enterprises to be aware of these  \n© All articles included in the journal are copyrighted to the ITU and TUP. This work is available under the CC BY-NC-ND 3.0 IGO license:  \n[http","cbCaia2M9NDsfGo6","https://ap.wps.com/l/cbCaia2M9NDsfGo6","pdf",1030327,1,16,"English","en",105,"# Introduction\n## Decision-making importance\n## Big data analytics as a decision support approach\n## Machine learning and big data concepts\n## Applications and benefits across domains\n## Key challenges: ethics and privacy concerns","[{\"question\":\"How do big data analytics improve decision-making compared with conventional methods?\",\"answer\":\"Big data analytics provides more objective, data-driven support by improving visibility, organizing and filtering data, discovering crucial insights, and generating more accurate predictions. It helps address limitations of subjective judgment and incomplete perspectives.\"},{\"question\":\"Which machine learning workflow steps are covered in the review?\",\"answer\":\"The review summarizes key steps such as data collection, data preprocessing, feature selection, model training, and evaluation. These elements form the basis for building and assessing decision-related models.\"},{\"question\":\"What ethical and privacy concerns does the review emphasize?\",\"answer\":\"The review highlights the need for transparency and fairness in decision-making algorithms and stresses protecting individuals’ privacy rights. It also notes that using sensitive personal information can intensify ethical and privacy risks.\"}]","Navigating the Ethical and Privacy Concerns of Big Data and Machine Learning in Decision Making | PDF",1785680979,40,{"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},"navigating-the-ethical-and-privacy-concerns-of-big-data-and-machine-learning-in-decision-making","",{"@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/navigating-the-ethical-and-privacy-concerns-of-big-data-and-machine-learning-in-decision-making/118045/",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","2026-08-02",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},"How do big data analytics improve decision-making compared with conventional methods?","Question",{"text":75,"@type":76},"Big data analytics provides more objective, data-driven support by improving visibility, organizing and filtering data, discovering crucial insights, and generating more accurate predictions. It helps address limitations of subjective judgment and incomplete perspectives.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning workflow steps are covered in the review?",{"text":80,"@type":76},"The review summarizes key steps such as data collection, data preprocessing, feature selection, model training, and evaluation. These elements form the basis for building and assessing decision-related models.",{"name":82,"@type":73,"acceptedAnswer":83},"What ethical and privacy concerns does the review emphasize?",{"text":84,"@type":76},"The review highlights the need for transparency and fairness in decision-making algorithms and stresses protecting individuals’ privacy rights. 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