[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123595-en":3,"doc-seo-123595-105":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},123595,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","Globalizing Fairness Attributes in Machine Learning - A Case Study on Health in Africa","Machine learning is increasingly used in healthcare, but fairness concerns are critical because models can amplify historical and current inequities, creating harmful outcomes for vulnerable groups. In the context of global health in Africa, where power imbalances between the Global North and South persist, the paper develops fairness attributes tailored to African conditions. It maps where these attributes arise across machine-learning-enabled medical modalities and identifies real-world points in the pipeline to incorporate them. The paper positions the work as a foundation and a call for further research on fair ML in Africa.","arXiv :2304 .02 190v 1 [ cs .LG] 5 Apr 2023  \nGlobalizing Fairness Attributes in Machine Learning: A Case  \nStudy on Health in Africa  \nMercy Nyamewaa Asiedu∗ Google Research Awa Dieng∗  \nGoogle Research  \nAbigail Oppong  \nGhana NLP  \nMaria Nagawa  \nDuke University  \nSanmi Koyejo  \nStanford University, Google Research  \nKatherine Heller  \nGoogle Research  \n[masiedu@google.com](masiedu@google.com)[awadieng@google.com](awadieng@google.com)[abigoppong@gmail.com](abigoppong@gmail.com)[ ](abigoppong@gmail.com)[maria.nagawa@duke.edu](maria.nagawa@duke.edu)[ ](maria.nagawa@duke.edu)[sanmik@google.com](sanmik@google.com)  \n[kheller@google.com](kheller@google.com)  \nAbstract  \nWith growing machine learning (ML) applications in healthcare, there have been calls for fairness in ML to understand and mitigate ethical concerns these systems may pose. Fairness has implications for global health in Africa, which already has inequitable power imbalances between the Global North and South. This paper seeks to explore fairness for global health, with Africa as a case study. We propose fairness attributes for consideration in the African context and delineate where they may come into play in di􀀋erent ML-enabled medical modalities. This work serves as a basis and call for action for furthering research into fairness in global health.  \n1. Introduction  \nMachine learning (ML) models have the potential forfar reaching impact in health. However they also have the potential to propagate biases that re􀀍ect realworld historical and current inequities and could lead to unintended, harmful outcomes, particularly for vulnerable populations (Huang et al. , 2022; Haytham and Wang, 2022; Chen et al. , 2021; Char et al. , 2018; Gianfrancesco et al. , 2018; Obermeyer et al. , 2019) . Sources of biases include missing data, non-  \n∗ These authors contributed equally  \nrepresentative sample size, imbalanced group distributions, and misclassi􀀌cation or measurement error (Gianfrancesco et al. , 2018) . In health, this can affect vulnerable populations-patients with fractured care, low literacy levels, underrepresented subgroups, and/or patients who access clinical facilities with limited devices for measurements or data input methods (Gianfrancesco et al. , 2018; Obermeyer et al. , 2019) . A prominent example is an algorithm used in the United States that deprioritized sicker black patients for care, based on a faulty cost variable, which perpetuated structural socio-economic disparities (Obermeyer et al. , 2019) . This has led to the institution of the algorithmic fairness 􀀌eld, various attempts to correct machine learning biases, through evaluating models and understanding attributes that may cause ML models to make unfair or biased decisions.  \nThere are multiple de􀀌nitions for global health but we use the de􀀌nition proposed by Beaglehole (2010): \"collaborative trans-national research and action for promoting health for all\". Fairness is especially important for global health, which despite positive outcomes such as decreased has been plagued with inequitable power imbalances between high-income countries (HICs) and low-and middle-income countries (LMICs) (Holst, 2020) . The recent decolonizing global health movement shedslight on \\how knowledge generated from HICs de􀀌ne practices and informs thinking to the detriment  \n© M.N. Asiedu, A. Dieng, A. Oppong, M. Nagawa, S. Koyejo & K. Heller.  \nFairness in Global Health  \nof knowledge systems in LMICs\"(Eichbaum et al. , 2021) . Eichbaum et al. (2021) explore intersections between colonialism, medicine and global health, and how colonialism continues to impact global health programs and partnerships. They make the point that even the use of the term \\Global health\"to describe health in LMICs is problematic. Given that most machine learning models are developed with problem formulation, personnel, resources and data from HICs, and may be imported with little regulation to LMICs, there is a risk for algorithmic colonia","cbCaiqRl9uyAI2AR","https://ap.wps.com/l/cbCaiqRl9uyAI2AR","pdf",502392,1,14,"English","en",105,"# Introduction\n## Fairness and bias in healthcare ML\n## Global health and power imbalances\n## Related work on contextual fairness\n# Proposed fairness attributes for Africa\n## Axes of disparities between Africa and the West\n## Global axes of disparities in Africa\n## Medical ML modalities where to apply them\n# Discussion and future research directions","[{\"question\":\"Why is fairness emphasized for machine learning in healthcare?\",\"answer\":\"Because ML systems can propagate biases tied to real-world historical and current inequities, leading to unintended harmful outcomes for vulnerable populations.\"},{\"question\":\"How does the paper frame global health and Africa as a case study?\",\"answer\":\"It adopts a definition of global health centered on trans-national research and action, then examines how fairness matters under inequitable power imbalances between regions.\"},{\"question\":\"What does the paper propose for fairness in the African context?\",\"answer\":\"It proposes fairness attributes for African conditions, including axes of disparities between Africa and the West and global axes of disparities within Africa.\"}]","Globalizing Fairness Attributes in Machine Learning - 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