[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128168-en":3,"doc-seo-128168-105":31,"detail-sidebar-cat-0-en-105":93},{"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":28,"seo_description":14,"update_tm":29,"read_time":30},128168,549768702563,"Sage","https://ap-avatar.wpscdn.com/avatar/8000c4aa63b76e948b?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786536092046926083",8,"Research & Report","A study of Fairness in Machine Learning in the presence of Missing Values - Thesis","Fairness in machine learning algorithms has drawn increasing attention as algorithms shape everyday decisions. This study examines how bias can arise when data sources contain missing values, noting that missingness is often linked to socio-economic status or demographic characteristics rather than occurring completely at random. Research on how missing value mechanisms and handling methods affect algorithmic fairness remains limited. A systematic study evaluates how data missingness and different handling procedures influence fairness across multiple machine learning algorithms, focusing on simpler imputation approaches.","A study of Fairness in Machine Learning in the presence of Missing Values  \nAeysha Aziz Bhatti  \nThesis presented in the partial fulfilment  \nof the requirement for the degree of  \nMCom (Statistics)  \nat the University of Stellenbosch  \nSupervisor: Dr. T. Sandrock  \nDegree of confidentiality: A March 2023  \nPLAGIARISM DECLARATION  \n1. Plagiarism is the use of ideas, material and other intellectual property of another’s work and to present it as my own.  \n2. I agree that plagiarism is a punishable offence because it constitutes theft.  \n3. Accordingly, all quotations and contributions from any source whatsoever (including the internet) have been cited fully. I understand that the reproduction of text without quotation marks (even when the source is cited) is plagiarism.  \n4. I also understand that direct translations are plagiarism.  \n5. I declare that the work contained in this thesis, except otherwise stated, is my original work and that I have not previously (in its entirety or in part) submitted it for grading in this thesis or another thesis.  \n\n|  |  |\n| --- | --- |\n| Student number | Signature |\n| A. A Bhatti | 9 November 2022 |\n| Initials and surname | Date |\n\nCopyright © 2023 Stellenbosch University All rights reserved  \nACKNOWLEDGEMENTS  \nI would like to thank Stellenbosch University and the Department of Statistics and Actuarial Science for giving me this opportunity to complete an MCom in Statistics. Next, I would like to thank my supervisor Dr. Sandrock for providing guidance along the way at different stages of the thesis.  \nI would like to thank my dear husband for his unconditional love and support of my endeavours. I would also like to thank my mother for her support during the difficult times.  \nLast but not least, I want to thank Cathy O’Neil for giving me the inspiration to pursue the topic of fairness in machine learning.  \nABSTRACT  \nFairness of Machine Learning algorithms is a topic that is receiving increasing attention, as more and more algorithms permeate the day to day aspects of our lives. One way in which bias can manifest in a data source is through missing values. If data are missing, these data are often assumed to be missing completely randomly, but usually this is not the case. In reality, the propensity of data being missing is often tied to socio-economic status or demographic characteristics of individuals. There is very limited research into how missing values and missing value handling methods can impact the fairness of an algorithm. In this research, we conduct a systematic study starting from the foundational questions of how the data are missing, how the missing data are dealt with and how this impacts fairness, based on the outcome of a few different types of machine learning algorithms. Most researchers, when dealing with missing data, either apply listwise deletion or tend to use the simpler methods of imputation versus the more complex ones. We study the impact of these simpler methods on the fairness of algorithms. Our results show that the missing data mechanism and missing data handling procedure can impact the fairness of an algorithm, and that under certain conditions the simpler imputation methods can sometimes be beneficial in decreasing discrimination.  \nKeywords: fairness in machine learning, missing values, fairness metrics, imputation, algorithmic bias  \nOPSOMMING  \nDie regverdigheid van masjienleeralgoritmes is ’n onderwerp wat toenemend aandag geniet, soos al hoe meer algoritmes elke aspek van ons alledaagse lewens deurdring. Een manier waarop sydigheidin ’n databron kan manifesteer is deur ontbrekende waardes. Indien daar ontbrekende data is, word daar dikwels aanvaar dat die data op ’n algeheel ewekansige manier ontbrekend is, maar ditis gewoonlik nie die geval nie. In werklikheid is die geneigdheid vir die afwesigheid van data dikwels verwant aan sosio-ekonomiese status of demografiese eienskappe van individue. Daar is baie beperkte navorsing oor hoe ontbrekende waar","cbCaihiqD5ezXawe","https://ap.wps.com/l/cbCaihiqD5ezXawe","pdf",3877254,5,1,136,"English","en",105,"# 1 INTRODUCTION\n# 2 BACKGROUND: FAIRNESS IN MACHINE LEARNING\n## 2.1 Introduction\n## 2.2 Notation\n## 2.3 Fairness through unawareness\n## 2.4 Fairness metrics\n## 2.5 Bias mitigation algorithms\n## 2.6 Summary\n# 3 BACKGROUND: MISSING VALUES\n## 3.1 Introduction\n## 3.2 Missing data causes and patterns","[{\"question\":\"Why are missing values a fairness risk in machine learning?\",\"answer\":\"Missingness can be related to socio-economic status or demographic traits, creating bias in the data source that may translate into unfair algorithmic outcomes.\"},{\"question\":\"How does this thesis study the impact of missing value handling on fairness?\",\"answer\":\"It performs a systematic study on how the missing data mechanism and the selected missing data handling procedure affect fairness across multiple machine learning algorithms.\"},{\"question\":\"What do the results suggest about simpler imputation methods?\",\"answer\":\"Under certain conditions, simpler imputation methods can reduce discrimination, indicating that the missingness mechanism and handling choice jointly influence fairness.\"}]","A study of Fairness in Machine Learning in the presence of Missing Values - 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