[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-85572-en":3,"doc-seo-85572-105":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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},85572,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Fairness Constraints in High-Dimensional Generalized Linear Models","Machine learning models often inherit biases from historical data, creating urgent concerns about fairness and accountability. Many standard fairness methods require sensitive attributes (e.g., gender or race), yet privacy, ethics, and legal constraints frequently prevent access to them. This work proposes a framework that infers latent sensitive attributes from auxiliary features and embeds fairness constraints directly into generalized linear model training. Experiments show bias mitigation alongside preserved predictive accuracy, supporting practical, equitable decision-making.","arXiv :2604 . 16610v2 [ stat .ML] 11 Jul 2026  \nFairness Constraints in High-Dimensional Generalized  \nLinear Models  \nYixiao Lina,∗, James G. Bootha  \na Department of Statistics and Data Science, Cornell University  \nAbstract  \nMachine learning models often inherit biases from historical data, raising critical concerns about fairness and accountability. Conventional fairness interventions typically require access to sensitive attributes like gender or race, but privacy and legal restrictions frequently limit their use. To address this challenge, we propose a framework that infers sensitive attributes from auxiliary features and integrates fairness constraints into model training. Our approach mitigates bias while preserving predictive accuracy, offering a practical solution for fairness-aware learning. Empirical evaluations validate its effectiveness, contributing to the advancement of more equitable algorithmic decision-making.  \nKeywords: algorithmic fairness, latent sensitive attributes, finite mixture models, generalized linear models, variable selection  \n1. Introduction  \n1.1. Motivation  \nAlgorithmic decision-making pervades our lives, profoundly shaping outcomes in areas such as email filtering, image tagging, personalized news feeds, credit scoring, and job assessments. While these systems offer efficiency and convenience, they also raise significant concerns regarding transparency, accountability, and fairness. These concerns are especially pressing when the historical data used to train such systems reflect biases associated with sensitive attributes, such as gender, race, or religion. When unchecked, these biases can lead to systematically unfair treatment of certain groups and perpetuate existing societal inequities.  \n∗ Corresponding author.  \nEmail addresses: [yl2883@cornell.edu](yl2883@cornell.edu) (Yixiao Lin), [jb383@cornell.edu](jb383@cornell.edu) (James G. Booth)  \nA large body of work in fair machine learning seeks to mitigate these risks by constraining or modifying learning algorithms so that predictions satisfy group-based fairness notions, such as demographic parity or equalized odds. However, two practical challenges remain relatively underexplored. First, most methods assume that sensitive attributes are directly observed and available throughout the development and deployment of a system. In many real-world applications, this assumption fails due to privacy concerns, ethical considerations, or legal restrictions (Coston et al. , 2019) . Second, modern applications often involve high-dimensional feature spaces in which only a small subset of predictors is truly informative. Variable selection is therefore essential; yet, the interaction between feature selection, fairness constraints, and unobserved sensitive attributes is poorly understood.  \nIn this paper, we address these two challenges simultaneously. We consider settings in which sensitive attributes are multi-category, unobserved, and must be inferred from auxiliary predictors, while the main prediction task is modeled using generalized linear models (GLMs) in either low-or high-dimensional regimes. Our goal is to construct prediction rules that are both accurate and fair with respect to the latent sensitive attribute, while retaining the interpretability and sparsity benefits of modern variable selection methods.  \n1.2. Related work  \nExisting approaches to algorithmic fairness can be broadly categorized into three classes.  \n1. Preprocessing approaches. These methods modify the training data before model fitting in order to mitigate discriminatory patterns, for example, through reweighting, resampling, or data transformation to balance the representation of sensitive groups (Feldman et al. , 2015 ; Kamiran and Calders, 2012) .  \n2. In-processing approaches. These methods incorporate fairness constraints, regularization terms, or other fairness-aware objectives directly into the learning procedure so that model training jointly optimizes pred","cbCaichh6lpxzDga","https://ap.wps.com/l/cbCaichh6lpxzDga","pdf",2800959,4,1,44,"English","en",105,"# Introduction\n## Motivation\n## Related work","[{\"question\":\"Why are fairness constraints challenging in real-world machine learning systems?\",\"answer\":\"Because many systems train on historical data that may encode biases tied to sensitive attributes, and fairness-aware methods often require those sensitive attributes to be directly available.\"},{\"question\":\"How does the proposed framework handle unobserved sensitive attributes?\",\"answer\":\"It infers multi-category latent sensitive attributes from auxiliary predictors and then incorporates fairness constraints into model training.\"},{\"question\":\"What role does variable selection play in high-dimensional generalized linear models under fairness constraints?\",\"answer\":\"The setting assumes high-dimensional features with only a small subset being informative, so variable selection is essential; the paper targets the interaction between feature selection, fairness constraints, and latent sensitive attributes.\"}]",1784204679,111,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"fairness-constraints-in-high-dimensional-generalized-linear-models","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":21},"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":20},"https://docshare.wps.com/document/fairness-constraints-in-high-dimensional-generalized-linear-models/85572/",{"url":52,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-24","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why are fairness constraints challenging in real-world machine learning systems?","Question",{"text":75,"@type":76},"Because many systems train on historical data that may encode biases tied to sensitive attributes, and fairness-aware methods often require those sensitive attributes to be directly available.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed framework handle unobserved sensitive attributes?",{"text":80,"@type":76},"It infers multi-category latent sensitive attributes from auxiliary predictors and then incorporates fairness constraints into model training.",{"name":82,"@type":73,"acceptedAnswer":83},"What role does variable selection play in high-dimensional generalized linear models under fairness constraints?",{"text":84,"@type":76},"The setting assumes high-dimensional features with only a small subset being informative, so variable selection is essential; 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