[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124579-en":3,"doc-seo-124579-105":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},124579,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","Connecting Fairness in Machine Learning with Public Health Equity - Abstract and Framework","Machine learning (ML) is a key tool in public health, with the potential to improve population health, support diagnosis and treatment selection, and increase health system efficiency. Biases in data and model design can create disparities for protected groups and worsen existing inequalities. This study reviews foundational work on ML fairness and proposes a framework to detect and mitigate bias across the ML pipeline, including data processing, model design, deployment, and evaluation, illustrated through examples showing amplified bias via model predictions.","Connecting Fairness in Machine Learning with Public  \nHealth Equity  \nShaina Raza  \nUniversity of Toronto  \nToronto, Canada  \n[shaina.raza@utoronto.ca](shaina.raza@utoronto.ca)  \nAbstract— Machine learning (ML) has become a critical tool in public health, offering the potential to improve population health, diagnosis, treatment selection, and health system efficiency. However, biases in data and model design can result in disparities for certain protected groups and amplify existing inequalities in healthcare. To address this challenge, this study summarizes seminal literature on ML fairness and presents a framework for identifying and mitigating biases in the data and model. The framework provides guidance on incorporating fairness into different stages ofthe typical ML pipeline, such as data processing, model design, deployment, and evaluation. To illustrate the impact of biases in data on ML models, we present examples that demonstrate how systematic biases can be amplified through model predictions. These case studies suggest how the framework can be used to prevent these biases and highlight the need for fair and equitable ML models in public health. This work aims to inform and guide the use of ML in public health towards a more ethical and equitable outcome for all populations.  \nKeywords—Fairness; Equity; Public Health; Machine Learning.  \nI. INTRODUCTION  \nHealth equity [1] is a crucial principle in public health, which aims to eliminate disparities in health outcomes and healthcare access among various populations. The World Health Organization (WHO) [2] and the United Nations (UN) [3] both prioritize health equity as a key aspect of their missions to improve global health outcomes. However, despite these efforts, disparities in health outcomes continue to persist, particularly among marginalized and underserved populations [4] .  \nMachine learning (ML) has the potential to transform the way we approach health and healthcare with its advanced analytical and predictive capabilities. ML can aid in comprehending complex health systems, identifying disease patterns and trends, and improving patient outcomes [5] . However, it is essential to exercise caution when employing ML and consider potential biases and inequalities that may be present in the data used to train these models [6], [7] . Such biases can lead to discrimination and unjust outcomes for specific populations, exacerbating existing health disparities [8] .  \nThis study aims to promote health equity through ML by reviewing the literature on ML fairness and presenting a novel ML pipeline approach to integrate fairness into various stages ofa standard ML pipeline. Although fair ML has been explored in artificial intelligence (AI) literature [9], [10], its implementation in public health has received limited attention. This study endeavors to provide the public health community with accessible methods for ensuring equitable outcomes when using ML. The specific contributions of this research are:  \n- Summarizing the concepts of fair ML and presenting an ML pipeline approach for public health use to achieve equitable outcomes.  \n- Providing examples that demonstrate the importance of the pipeline approach and how disparities can be amplified and mitigated through ML.  \n- Offering straightforward and accessible methods for the public health community to incorporate fairness in their use of ML.  \nUnlike previous studies [6], [11]–[13] that have primarily focused on specific applications of ML in healthcare, this review adopts a different approach by presenting a methodology for incorporating fairness during various stages of a standard ML pipeline. This pipeline idea is proposed based on a thorough review of pertinent literature on fair ML. The study primary focus is on promoting health equity for marginalized and underserved populations and providing straightforward and accessible methods for the public health community.  \nII. BACKGROUND  \nHealth equity [1], [14] i","cbCaigahWgxoWfFf","https://ap.wps.com/l/cbCaigahWgxoWfFf","pdf",289857,1,6,"English","en",105,"# Introduction\n## Health equity and disparities\n## ML potential and bias risks\n## Study aims and contributions\n# Background\n## Health equity fundamentals\n## Fairness in machine learning","[{\"question\":\"Why is health equity central to public health and how does it relate to ML?\",\"answer\":\"Health equity targets eliminating disparities in health outcomes and healthcare access among populations. Because ML can shape predictions and decisions in healthcare, fairness is necessary to avoid reproducing or amplifying inequities.\"},{\"question\":\"What problem does the document address regarding ML fairness?\",\"answer\":\"It addresses how biases in training data and model design can produce discriminatory outcomes for protected groups, thereby worsening existing health disparities.\"},{\"question\":\"How does the proposed framework support fair and equitable ML in public health?\",\"answer\":\"The framework helps identify and mitigate bias at multiple stages of a standard ML pipeline—data processing, model design, deployment, and evaluation—and uses case examples to show how systematic bias can be amplified through predictions.\"}]","Connecting Fairness in Machine Learning with Public Health Equity - Abstract and Framework | PDF",1785893107,15,{"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},"connecting-fairness-in-machine-learning-with-public-health-equity-abstract-and-framework","",{"@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/connecting-fairness-in-machine-learning-with-public-health-equity-abstract-and-framework/124579/",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","2026-08-05",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 health equity central to public health and how does it relate to ML?","Question",{"text":75,"@type":76},"Health equity targets eliminating disparities in health outcomes and healthcare access among populations. Because ML can shape predictions and decisions in healthcare, fairness is necessary to avoid reproducing or amplifying inequities.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What problem does the document address regarding ML fairness?",{"text":80,"@type":76},"It addresses how biases in training data and model design can produce discriminatory outcomes for protected groups, thereby worsening existing health disparities.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the proposed framework support fair and equitable ML in public health?",{"text":84,"@type":76},"The framework helps identify and mitigate bias at multiple stages of a standard ML pipeline—data processing, model design, deployment, and evaluation—and uses case examples to show how systematic bias can be amplified through predictions.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]