[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121041-en":3,"doc-seo-121041-105":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},121041,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Procedural Fairness in Machine Learning","Procedural Fairness in Machine Learning examines fairness in ML beyond distributive (outcome) perspectives by defining procedural fairness for individual and group settings. Building on established ideas from philosophy and psychology, it formulates formal definitions and introduces a new evaluation metric, GPFFAE, which leverages feature attribution explanation to characterize the model’s decision process. Experiments on one synthetic and eight real-world datasets analyze links between procedural and distributive fairness. The study also proposes methods to pinpoint features causing procedural unfairness and improve fairness, with reduced impact on model performance.","arXiv :2404 .01877v2 [ cs .LG] 26 Feb 2026  \nProcedural Fairness in Machine Learning  \nZIMING WANG, Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation, Department of Computer Science and Engineering, Southern University of Science and Technology, China CHANGWU HUANG∗ , School of AI and Liberal Arts, Beijing Normal-Hong Kong Baptist University, China KE TANG∗ , Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation, Department of Computer Science and Engineering, Southern University of Science and Technology, China  \nXIN YAO, School of Data Science, Lingnan University, Hong Kong, China  \nFairness in machine learning (ML) has garnered significant attention. However, current research has mainly concentrated on the distributive fairness of ML models, with limited focus on another dimension of fairness, i.e., procedural fairness. In this paper, we first define the procedural fairness of ML models by drawing from the established understanding of procedural fairness in philosophy and psychology fields, and then give formal definitions of individual and group procedural fairness. Based on the proposed definition, we further propose a novel metric to evaluate the group procedural fairness of ML models, called GPFFAE, which utilizes a widely used explainable artificial intelligence technique, namely feature attribution explanation (FAE), to capture the decision process of ML models. We validate the effectiveness of GPFFAE on a synthetic dataset and eight real-world datasets. Our experimental studies have revealed the relationship between procedural and distributive fairness of ML models. After validating the proposed metric for assessing the procedural fairness of ML models, we then propose a method for identifying the features that lead to the procedural unfairness of the model and propose two methods to improve procedural fairness based on the identified unfair features. Our experimental results demonstrate that we can accurately identify the features that lead to procedural unfairness in the ML model, and both of our proposed methods can significantly improve procedural fairness while also improving distributive fairness, with a slight sacrifice on the model performance.  \nJAIR Track: Fairness and Bias in AI  \nJAIR Associate Editor: Roberta Calegari JAIR Reference Format:  \nZiming Wang, Changwu Huang, Ke Tang, and Xin Yao. 2026. Procedural Fairness in Machine Learning. Journal of Artificial Intelligence Research 85, Article 20 (February 2026), 30 pages. doi: 10.1613/jair.1.20498  \n1 Introduction  \nAs artificial intelligence (AI) is increasingly used in critical domains such as finance (Chen et al. 2016), hiring (L. Liet al. 2021), and criminal justice (Dressel and Farid 2018) to make consequential decisions affecting individuals, concerns about discrimination and fairness inevitably arise and have become the forefront of deliberations  \n∗ Corresponding authors  \ncn, Guangdong Provincial Key  \ncn, School of AI and Liberal Arts,  \ninspired Intelligent Computation, Department of Computer Science and Engineering, Southern University  \nAuthors’ Contact Information: Ziming Wang, orcid: 0000-0002-3118-8742, [wangzm2021@mail.sustech.edu](wangzm2021@mail.sustech.edu).  \nLaboratory of Brain-inspired Intelligent Computation, Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China; Changwu Huang, orcid: 0000-0003-3685-2822, [changwuhuang@bnbu.edu](changwuhuang@bnbu.edu).  \nBeijing Normal-Hong Kong Baptist University, Zhuhai, China; Ke Tang, orcid: 0000-0002-6236-2002, [tangk3@sustech.edu.cn](tangk3@sustech.edu.cn), Guangdong Provincial Key Laboratory of Brain  \nof Science and Technology, Shenzhen, China; Xin Yao, orcid: 0000-0001-8837-4442, [xinyao@ln.edu.hk](xinyao@ln.edu.hk), School of Data Science, Lingnan University, Hong Kong, China.  \nThis work is licensed under a Creative Commons Attribution International 4 .0 License.  \n© 2026 Copyrig","cbCaib3eSeyDrFnq","https://ap.wps.com/l/cbCaib3eSeyDrFnq","pdf",2328633,1,30,"English","en",105,"# Introduction\n# Fairness in ML: Distributive vs Procedural\n# Formal Definitions of Procedural Fairness\n# GPFFAE Metric for Group Procedural Fairness\n# Experimental Validation and Dataset Studies\n# Identifying Unfair Features and Improving Fairness","[{\"question\":\"What does procedural fairness mean in the context of machine learning?\",\"answer\":\"Procedural fairness refers to the fairness of the decision-making process rather than only the fairness of outcomes. The paper defines procedural fairness for both individual and group settings using established concepts from philosophy and psychology.\"},{\"question\":\"How is the GPFFAE metric constructed and what does it measure?\",\"answer\":\"GPFFAE measures group procedural fairness by using feature attribution explanation to capture how an ML model arrives at decisions. It evaluates procedural fairness based on the inferred decision process.\"},{\"question\":\"What relationship does the paper find between procedural and distributive fairness?\",\"answer\":\"The experimental studies reveal a relationship between procedural fairness and distributive fairness. The findings link improvements in process fairness with changes in outcome fairness.\"},{\"question\":\"How does the paper identify features that cause procedural unfairness and improve it?\",\"answer\":\"After validating the metric, the paper proposes a way to identify the features leading to procedural unfairness. It then introduces two improvement methods that significantly enhance procedural fairness and also improve distributive fairness, with only a slight sacrifice in model performance.\"}]","Procedural Fairness in Machine Learning | PDF",1785733450,76,{"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},"procedural-fairness-in-machine-learning","",{"@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/procedural-fairness-in-machine-learning/121041/",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","2026-08-03",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},"What does procedural fairness mean in the context of machine learning?","Question",{"text":75,"@type":76},"Procedural fairness refers to the fairness of the decision-making process rather than only the fairness of outcomes. The paper defines procedural fairness for both individual and group settings using established concepts from philosophy and psychology.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is the GPFFAE metric constructed and what does it measure?",{"text":80,"@type":76},"GPFFAE measures group procedural fairness by using feature attribution explanation to capture how an ML model arrives at decisions. It evaluates procedural fairness based on the inferred decision process.",{"name":82,"@type":73,"acceptedAnswer":83},"What relationship does the paper find between procedural and distributive fairness?",{"text":84,"@type":76},"The experimental studies reveal a relationship between procedural fairness and distributive fairness. The findings link improvements in process fairness with changes in outcome fairness.",{"name":86,"@type":73,"acceptedAnswer":87},"How does the paper identify features that cause procedural unfairness and improve it?",{"text":88,"@type":76},"After validating the metric, the paper proposes a way to identify the features leading to procedural unfairness. It then introduces two improvement methods that significantly enhance procedural fairness and also improve distributive fairness, with only a slight sacrifice in model performance.","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":21,"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"]