Pub Date : 2025-05-01Epub Date: 2025-03-11DOI: 10.1109/mis.2025.3549484
Sitao Min, Hafiz Asif, Jaideep Vaidya
Today, data-driven models and artificial intelligence / machine learning underlie decision making in almost all aspects of society. However, significant concerns have been raised over the fairness of such models. While various aspects of algorithmic fairness have been studied, the effect of missing data on fairness remains understudied. This is a significant problem since data in real-world settings is almost never complete, and may often suffer from systemic missingness. This article systematically evaluates how missing data, particularly when correlated with protected classes and outcome variables, affects the fairness of classifiers. Utilizing a comprehensive framework covering various missing data patterns, rates, and mitigation methods, we analyze 150 experimental dataset variants derived from real-world scenarios, and find that missing data correlated with sensitive attributes and outcomes can exacerbate disparities, even for little missingness, making it crucial to address missingness in fairness evaluations.
{"title":"Exploring the inequitable impact of data missingness on fairness in machine learning.","authors":"Sitao Min, Hafiz Asif, Jaideep Vaidya","doi":"10.1109/mis.2025.3549484","DOIUrl":"10.1109/mis.2025.3549484","url":null,"abstract":"<p><p>Today, data-driven models and artificial intelligence / machine learning underlie decision making in almost all aspects of society. However, significant concerns have been raised over the fairness of such models. While various aspects of algorithmic fairness have been studied, the effect of missing data on fairness remains understudied. This is a significant problem since data in real-world settings is almost never complete, and may often suffer from systemic missingness. This article systematically evaluates how missing data, particularly when correlated with protected classes and outcome variables, affects the fairness of classifiers. Utilizing a comprehensive framework covering various missing data patterns, rates, and mitigation methods, we analyze 150 experimental dataset variants derived from real-world scenarios, and find that missing data correlated with sensitive attributes and outcomes can exacerbate disparities, even for little missingness, making it crucial to address missingness in fairness evaluations.</p>","PeriodicalId":13160,"journal":{"name":"IEEE Intelligent Systems","volume":"40 3","pages":"28-38"},"PeriodicalIF":5.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12204593/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144527668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-13DOI: 10.1109/mis.2024.3439535
Arun Narayanan, Evangelos Pournaras, Pedro H. J. Nardelli
{"title":"Large-scale Package Deliveries with Unmanned Aerial Vehicles using Collective Learning","authors":"Arun Narayanan, Evangelos Pournaras, Pedro H. J. Nardelli","doi":"10.1109/mis.2024.3439535","DOIUrl":"https://doi.org/10.1109/mis.2024.3439535","url":null,"abstract":"","PeriodicalId":13160,"journal":{"name":"IEEE Intelligent Systems","volume":"40 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142222363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-30DOI: 10.1109/mis.2024.3415208
Daniel E. O’Leary
{"title":"Do Large Language Models Bias Human Evaluations?","authors":"Daniel E. O’Leary","doi":"10.1109/mis.2024.3415208","DOIUrl":"https://doi.org/10.1109/mis.2024.3415208","url":null,"abstract":"","PeriodicalId":13160,"journal":{"name":"IEEE Intelligent Systems","volume":"10 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141872567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-30DOI: 10.1109/mis.2024.3426011
{"title":"IEEE Computer Society Has You Covered!","authors":"","doi":"10.1109/mis.2024.3426011","DOIUrl":"https://doi.org/10.1109/mis.2024.3426011","url":null,"abstract":"","PeriodicalId":13160,"journal":{"name":"IEEE Intelligent Systems","volume":"65 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141872535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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