[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118788-en":3,"doc-seo-118788-105":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},118788,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Fairness in Machine Learning Meets with Equity in Healthcare","As machine learning is increasingly deployed in healthcare, it can improve outcomes while also introducing bias from data quality, labeling, and model design, potentially harming demographic groups defined by age, gender, or race. The study presents an artificial intelligence framework, grounded in software engineering principles, to identify and mitigate bias in both data and models while supporting fairness in healthcare use. A healthcare case study illustrates how systemic data bias can amplify bias in predictions. Machine learning methods are proposed to prevent these effects, and future work targets validation in real clinical settings to measure impact on health equity.","arXiv :2305 .07041v2 [ cs .LG] 14 Aug 2023  \nFairness in Machine Learning Meets with Equity in Healthcare  \nShaina Raza,1,* Parisa Osivand Pour,1 Syed Raza Bashir2  \n1 Vector Institute for Artificial Intelligence, Toronto, ON, Canada  \n2 Toronto Metropolitan University, Toronto, ON, Canada  \n* Correspondence: shaina. raza@vectorinstitute.ai  \nAbstract  \nWith the growing utilization of machine learning in healthcare, there is increasing potential to enhance healthcare outcomes. However, this also brings the risk of perpetuating biases in data and model design that can harm certain demographic groups based on factors such as age, gender, and race. This study proposes an artificial intelligence framework, grounded in software engineering principles, for identifying and mitigating biases in data and models while ensuring fairness in healthcare settings. A case study is presented to demonstrate how systematic biases in data can lead to amplified biases in model predictions, and machine learning methods are suggested to prevent such biases. Future research aims to test and validate the proposed ML framework in real-world clinical settings to evaluate its impact on promoting health equity.  \nINTRODUCTION  \nMachine learning (ML) offers immense potential to significantly enhance patient outcomes and transform the landscape of clinical healthcare 29. Utilizing its analytical and predictive capabilities, ML can help reveal disease patterns and trends, and optimize patient care. However, it is important to proceed with caution when leveraging ML in healthcare. This is because inherent biases and inequalities in the data may result in discrimination, which could result in worsening of pre-existing health disparities 20. For example, a model trained on biased data might inaccurately predict a higher risk of heart disease for specific racial or ethnic groups, leading to unequal treatment opportunities and poorer health outcomes 22.  \nIn the context of ML, the term“bias” refers to skewed outcomes caused by errors in the modeling process 10 . This often occurs when training data is unrepresentative or contains systemic errors, leading the model to learn and potentially replicate these biases in its predictions. “Disparity” in healthcare indicates inequalities in health status, healthcare access, or healthcare quality across different groups 25. It is very important to minimize these biases and disparities when applying ML to healthcare, to ensure equitable outcomes.  \nHealth equity 28 is a core principle in clinical healthcare that seeks to eliminate differences in health outcomes and access to equal healthcare among  \nvarious populations. This principle aims to ensure that all individuals, regardless of their demographic or socio-economic background, have equal opportunities to access care and maintain or improve their health. Both the World Health Organization (WHO) and the United Nations (UN) prioritize health equity as a critical element of their missions to enhance global health outcomes. This motivates us to pursue research in this domain.  \nIn this study, we introduce an Artificial Intelligence (AI) framework designed to ensure that ML models produce unbiased and equitable predictions for all populations. Specifically, we integrate software engineering principles into the framework to improve its modularity, maintainability, and scalability, making it adaptable and efficient for various applications. Our goal is to introduce fairness in the healthcare setting through ML. The term fairness typically refers to the algorithm’s ability to make decisions and predictions without unjust bias or discrimination 24. Fairness is a critical aspect of responsible AI and ML practices, especially in sensitive areas like healthcare.  \nWe put forth a fair ML framework, in this work, that is rooted in software engineering principles. Following that, we present a healthcare case study that demonstrates how biases can exacerbate disparities in healthcare a","cbCaibbiE5zAsAOT","https://ap.wps.com/l/cbCaibbiE5zAsAOT","pdf",665175,1,10,"English","en",105,"# Introduction\n## Bias, disparity, and health equity in healthcare\n## Proposed fair ML framework rooted in software engineering\n# Previous Works\n## Fairness guidelines and technical solutions\n## Equity lens and operationalizing fairness in medicine\n# Case Study and Framework Evaluation","[{\"question\":\"What problem does the study address in healthcare machine learning?\",\"answer\":\"It addresses how biased data and model design can cause discrimination and worsen existing health disparities across demographic groups.\"},{\"question\":\"How does the proposed framework work to improve fairness?\",\"answer\":\"It integrates software engineering principles to create a modular, maintainable, and scalable AI framework that identifies and mitigates bias in data and models.\"},{\"question\":\"What evidence is used to show how bias affects model predictions?\",\"answer\":\"The study presents a healthcare case study showing that systematic biases in data can be amplified in model predictions.\"}]","Fairness in Machine Learning Meets with Equity in Healthcare | PDF",1785720268,25,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"fairness-in-machine-learning-meets-with-equity-in-healthcare","",{"@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/fairness-in-machine-learning-meets-with-equity-in-healthcare/118788/",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","2026-08-04","2026-08-03",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},"What problem does the study address in healthcare machine learning?","Question",{"text":76,"@type":77},"It addresses how biased data and model design can cause discrimination and worsen existing health disparities across demographic groups.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the proposed framework work to improve fairness?",{"text":81,"@type":77},"It integrates software engineering principles to create a modular, maintainable, and scalable AI framework that identifies and mitigates bias in data and models.",{"name":83,"@type":74,"acceptedAnswer":84},"What evidence is used to show how bias affects model predictions?",{"text":85,"@type":77},"The study presents a healthcare case study showing that systematic biases in data can be amplified in model predictions.","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},[94,98,102,106,111,116,121,124,129,132,135],{"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":21,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":21,"slug":134},"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]