[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126825-en":3,"doc-seo-126825-105":30,"detail-sidebar-cat-0-en-105":95},{"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},126825,1099523885074,"Ivy","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Fairness and bias correction in machine learning for depression prediction across four study populations","A systematic study examines bias in machine learning (ML) models built to predict depression using four case studies spanning different countries and populations. Standard ML approaches are found to produce regular unfair bias, reflecting inequality present in scientific data collection when structural factors are not properly addressed. Multiple mitigation strategies, including a post-hoc method proposed by the authors, effectively reduce unfair bias. Results show no single best model guarantees equal outcome fairness, motivating transparent reporting and fairness analysis during selection and intervention evaluation.","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nFairness and bias correction in machine learning for depression prediction across four study populations  \nVien Ngoc Dang1*, Anna Cascarano1, Rosa H. Mulder2,3, Charlotte Cecil2,4,5, Maria A. Zuluaga6, Jerónimo Hernández‑González7 & Karim Lekadir1,8  \nA significant level of stigma and inequality exists in mental healthcare, especially in under‑served populations. Inequalities are reflected in the data collected for scientific purposes. When not properly accounted for, machine learning (ML) models learned from data can reinforce these structural inequalities or biases. Here, we present a systematic study of bias in ML models designed to predict depression in four different case studies covering different countries and populations. We find that standard ML approaches regularly present biased behaviors. We also show that mitigation techniques, both standard and our own post‑hoc method, can be effective in reducing the level of unfair bias. There is no one best ML model for depression prediction that provides equality of outcomes. This emphasizes the importance of analyzing fairness during model selection and transparent reporting about the impact of debiasing interventions. Finally, we also identify positive habits and open challenges that practitioners could follow to enhance fairness in their models.  \nKeywords Machine learning for depression prediction, Algorithmic fairness, Bias mitigation, Novel post-hoc method, Psychiatric healthcare equity  \nDepression is a leading cause of disability worldwide, a major risk factor for the global burden of disease, and can even lead to suicide1,2. Taking into account that the global prevalence of depression increased by 25% during the COVID-19 outbreak3, being able to identify those individuals at risk would be of great value in order to enable the application of personalized preventive measures. To this end, it is necessary to characterize the factors leading up to the development of depression. Research to date points to the importance of both genetic and environmental factors (along with their interactions) in the etiology of depression4,5. Furthermore, environmental factors have been shown to co-occur, exerting cumulative effects on depression risk. The totality of these environmental influences is often referred to as the exposome and includes environmental and lifestyle factors, as well as traumatic life events6. Exposome data does not only provide an alternative picture, it is also relatively inexpensive and easy to acquire, typically through questionnaires7. Motivated by the successful application of machine learning (ML) in different contexts in the domain of medicine, there has been a spike in the use of ML for the detection, diagnosis, and treatment of depression8–10. Specifically, supervised ML methods are commonly used to learn predictive models from historical data, which are then applied to predict the possible development of the illness in new cases and patients.  \nThere have been historical concerns about the fairness of automatic decision making systems11 and, with the growing adoption of machine learning in health care applications, these concerns have also extended to ML models’ potential unfair bias8. It has been shown12 that ML models can amplify unfair behaviors masked  \n1Departament de Matemàtiques i Informàtica, Facultat de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain. 2Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands. 3The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands. 4Department of Epidemiology, Erasmus MC, Rotterdam,University Medical Center Rotterdam, Rotterdam, The Netherlands. 5Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands. 6Data Science Dep","cbCaio0uEmlXwWFC","https://ap.wps.com/l/cbCaio0uEmlXwWFC","pdf",2741891,1,12,"English","en",105,"# Introduction\n## Depression burden and the role of exposome data\n## ML for depression detection and concerns about fairness\n# Methods and study scope\n## Four study populations and bias assessment\n# Findings\n## Biased behavior in standard ML models\n## Mitigation effectiveness and post-hoc method\n# Discussion\n## No single model ensures equal outcomes\n## Fairness analysis and transparent reporting\n## Positive habits and open challenges for practitioners","[{\"question\":\"Why is fairness analysis important when using ML to predict depression?\",\"answer\":\"Because ML trained on data can reinforce structural inequalities when biases are not accounted for. Assessing fairness helps evaluate and reduce unfair bias during model selection.\"},{\"question\":\"What do the authors find about standard ML models for depression prediction?\",\"answer\":\"Standard ML approaches regularly exhibit biased behaviors across the studied settings, leading to unfair differences in predictive power between population subgroups.\"},{\"question\":\"How effective are bias mitigation techniques in the study?\",\"answer\":\"Both standard mitigation techniques and the authors’ proposed post-hoc method can reduce unfair bias levels. The effectiveness supports integrating debiasing interventions in practice.\"},{\"question\":\"Is there a single best ML model that guarantees equal outcomes?\",\"answer\":\"No. The study concludes that no one ML model provides equality of outcomes, underscoring the need for fairness-aware selection and transparent reporting of debiasing impacts.\"}]","Fairness and bias correction in machine learning for depression prediction across four study populations | PDF",1785935049,30,{"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":90,"head_meta":92,"extra_data":94,"updated_unix":28},"fairness-and-bias-correction-in-machine-learning-for-depression-prediction-across-four-study-populations","",{"@graph":36,"@context":89},[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/fairness-and-bias-correction-in-machine-learning-for-depression-prediction-across-four-study-populations/126825/",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",[71,77,81,85],{"name":72,"@type":73,"acceptedAnswer":74},"Why is fairness analysis important when using ML to predict depression?","Question",{"text":75,"@type":76},"Because ML trained on data can reinforce structural inequalities when biases are not accounted for. Assessing fairness helps evaluate and reduce unfair bias during model selection.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What do the authors find about standard ML models for depression prediction?",{"text":80,"@type":76},"Standard ML approaches regularly exhibit biased behaviors across the studied settings, leading to unfair differences in predictive power between population subgroups.",{"name":82,"@type":73,"acceptedAnswer":83},"How effective are bias mitigation techniques in the study?",{"text":84,"@type":76},"Both standard mitigation techniques and the authors’ proposed post-hoc method can reduce unfair bias levels. The effectiveness supports integrating debiasing interventions in practice.",{"name":86,"@type":73,"acceptedAnswer":87},"Is there a single best ML model that guarantees equal outcomes?",{"text":88,"@type":76},"No. The study concludes that no one ML model provides equality of outcomes, underscoring the need for fairness-aware selection and transparent reporting of debiasing impacts.","https://schema.org",{"og:url":52,"og:type":91,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":93,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":96},[97,101,105,109,114,119,124,126,131,134,138],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},"Exam",70,"exam",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},5,"Comic",60,"comic",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},6,"Technology",50,"technology",{"id":120,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":122,"slug":123},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":125},"research-report",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":129,"slug":130},9,"Religion & Spirituality",20,"religion-spirituality",{"id":129,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":129,"slug":133},"World Cup","world-cup",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":135,"slug":137},10,"Lifestyle","lifestyle",{"id":139,"doc_module":4,"doc_module_name":46,"category_name":140,"show_sort_weight":110,"slug":141},19,"General","general"]