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It outlines core concepts such as data and algorithmic bias, protected attributes, discrimination forms, and commonly used fairness metrics including demographic parity, equalized odds, calibration, and trade-offs. It further illustrates unfairness through hiring, criminal-justice risk assessment, facial recognition, and lending scenarios, and frames interventions like mitigation techniques and fairness-aware design within an African philosophical foundation of justice, equity, and moral reasoning.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/the-philosophical-foundation-of-fairness-in-machine-learning-an-african-perspective/290499/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/the-philosophical-foundation-of-fairness-in-machine-learning-an-african-perspective/290499.png","ImageObject",300,407,{"name":92,"@type":93},"Noah","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-29","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",17,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What does fairness in machine learning mean in the document?","Question",{"text":113,"@type":114},"Fairness in machine learning is defined as the principle that an algorithm should make decisions in a just and unbiased way, avoiding discrimination against individuals or groups based on sensitive characteristics such as race or gender.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"Which fairness metrics are highlighted?",{"text":118,"@type":114},"The document highlights demographic parity, equalized odds, fairness through awareness, and calibration, explaining how they measure fairness by equal outcomes, error rates, contextual fairness, or matched predicted probabilities across groups.",{"name":120,"@type":111,"acceptedAnswer":121},"What causes unfairness in machine learning according to the examples?",{"text":122,"@type":114},"Unfairness is linked to biased or incomplete training data reflecting societal inequities, as well as unintended consequences of optimization objectives, proxies, or assumptions in model design.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},290499,1789641848,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":34,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":130,"read_time":144,"preview_page_count":4},8796095462418,"https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780","1  \nEmedolu & Okeke  \nTHE PHILOSOPHICAL FOUNDATION OF FAIRNESS IN MACHINE LEARNING:  \nAN AFRICAN PERSPECTIVE  \nCyriacus O. Emedolu (Rev. Fr) | Madonna University, Nigeria Okija Campus |  \n[frcyriacus@madonnauniversity.edu.ng](frcyriacus@madonnauniversity.edu.ng)  \nRemi Chukwudi Okeke, Ph. D | Department of Public Administration Madonna University, Nigeria Okija Campus |  \n[okekerc@madonnauniversity.edu.ng](okekerc@madonnauniversity.edu.ng)  \nCONCEPTUALIZING FAIRNESS IN MACHINE LEARNING  \nFairness in machine learning (ML) refers to the principle that an algorithm should make decisions in a way that is just and unbiased, avoiding discrimination against individuals or groups based on characteristics such as race, gender, age, or other sensitive attributes (Barocas et al., 2023; Pessach & Shmueli, 2022; Mehrabi et al., 2021) . Fairness is therefore crucial in ensuring ethical and equitable use of ML systems, particularly in applications like hiring, lending, healthcare, and criminal justice. Key Concepts in Fairness in Machine Learning include the following:  \n• Bias: Bias in ML can occur at different stages, from data collection to model deployment. On one hand, data bias is about when training data reflects historical inequalities or societal prejudices. On the other hand, algorithmic bias refers to when the design of the algorithm inadvertently favors one group over another.  \n• Protected Attributes: Characteristics such as race, gender, age, or disability that should not influence the outcomes ofan ML model.  \n• Discrimination: Unfair treatment of individuals or groups in the decisions made by ML systems. This can be direct (explicit bias) or indirect (disparate impact) .  \n• Fairness Metrics: Several metrics are used to measure fairness in ML models, including:  \n• Demographic Parity: Ensures equal outcomes across groups, regardless of sensitive attributes.  \n• Equalized Odds: Ensures equal error rates (false positive and false negative) across groups.  \n• Fairness through Awareness: Considers contextual knowledge about fairness in the specific application.  \nCENTER FOR CYBER-SOCIAL DYNAMICS WORKSHOP PROCEEDINGS VOLUME 1, 2026  \n2  \nEmedolu & Okeke  \n• Calibration: Ensures the likelihood of an outcome matches the predicted probability across groups.  \n• Trade-offs: Achieving fairness often involves trade-offs with other objectives, such as accuracy or utility. For example, optimizing for equalized odds might slightly reduce overall accuracy.  \n• Mitigation Techniques: To address fairness concerns, several techniques can be used:  \n• Pre-processing: Adjusting the data before training to remove biases.  \n• In-processing: Modifying the learning algorithm to enforce fairness constraints.  \n• Post-processing: Adjusting the model’s predictions to ensure fairness after training.  \n• Fairness-Aware Design: Incorporating fairness considerations throughout the ML lifecycle, from data collection and preprocessing to model evaluation and deployment (Barocas et al., 2023; Chouldechova & Roth, 2020; Pessach & Shmueli, 2022; Makhlouf et al., 2021; Mehrabi et al., 2021; Mhasawade et al., 2021) .  \nEXAMPLES OF UNFAIRNESS IN MACHINE LEARNING  \nExamples of unfairness in machine learning arise in various domains where biased data, algorithmic design, or unintended consequences lead to discriminatory outcomes. For example, an ML-based recruitment tool was found to favor male candidates over female candidates for technical roles. This was because the training data reflected historical hiring practices, where men were more likely to be hired for technical jobs, leading the model to associate higher suitability with male candidates. An ML tool used in the U.S. for risk assessment in criminal justice was found to predict higher recidivism rates for Black defendants than for white defendants. This was because of bias in training data, which may have reflected systemic racial disparities in policing and sentencing (Chen, 2023; Kawamleh, 2024) .  \nFurth","cbCainOBcP8KALzD","https://ap.wps.com/l/cbCainOBcP8KALzD","pdf",308797,"English","# Conceptualizing Fairness in Machine Learning\n## Key Concepts in Fairness in Machine Learning\n## Examples of Unfairness in Machine Learning\n# Situating Fairness in Machine Learning Within a Philosophical Foundation","[{\"question\":\"What does fairness in machine learning mean in the document?\",\"answer\":\"Fairness in machine learning is defined as the principle that an algorithm should make decisions in a just and unbiased way, avoiding discrimination against individuals or groups based on sensitive characteristics such as race or gender.\"},{\"question\":\"Which fairness metrics are highlighted?\",\"answer\":\"The document highlights demographic parity, equalized odds, fairness through awareness, and calibration, explaining how they measure fairness by equal outcomes, error rates, contextual fairness, or matched predicted probabilities across groups.\"},{\"question\":\"What causes unfairness in machine learning according to the examples?\",\"answer\":\"Unfairness is linked to biased or incomplete training data reflecting societal inequities, as well as unintended consequences of optimization objectives, proxies, or assumptions in model design.\"}]","THE PHILOSOPHICAL FOUNDATION OF FAIRNESS IN MACHINE LEARNING: AN AFRICAN PERSPECTIVE | PDF",18]