[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117287-en":3,"doc-seo-117287-105":29,"detail-sidebar-cat-0-en-105":90},{"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":13,"seo_description":14,"update_tm":27,"read_time":28},117287,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Racial Bias in Machine Learning Algorithms in Secondary Mathematics Education - Master Thesis","This thesis examines racial bias and discrimination in machine learning algorithms using America’s longitudinal high school students dataset. Results show that models may appear to produce similar accuracy for White and Asian students and for Black and Hispanic students, while still generating systematically different error patterns. Specifically, the algorithms yield a higher false positive rate for White/Asian groups and underestimate Black/Hispanic students’ 12th grade mathematics performance. The study analyzes and compares seven commonly used algorithms’ biased outcomes across these groups.","UCLA  \nUCLA Electronic Theses and Dissertations  \nTitle  \nRacial Bias in Machine Learning Algorithms in Secondary Mathematics Education  \nPermalink  \n[https://escholarship.org/uc/item/2nk8q8fv](https://escholarship.org/uc/item/2nk8q8fv)  \nAuthor  \nHwang, Suyeon Betty  \nPublication Date  \n2024  \nPeer reviewed|Thesis/dissertation  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nUNIVERSITY OF CALIFORNIA Los Angeles  \nRacial Bias in Machine Learning Algorithms in Secondary Mathematics Education  \nA thesis submitted in partial satisfaction of the requirements for the degree Master of Applied Statistics and Data Science  \nby  \nSuyeon Hwang  \n2024  \n© Copyright by Suyeon Hwang 2024  \nABSTRACT OF THE THESIS  \nRacial Bias in  \nMachine Learning Algorithms  \nin Secondary Mathematics Education  \nby  \nSuyeon Hwang  \nMaster of Applied Statistics and Data Science  \nUniversity of California, Los Angeles, 2024  \nProfessor Ying Nian Wu, Chair  \nThis paper examines racial bias and discriminations in machine learning algorithms using America’s longitudinal high school students dataset. This study reveals machine learning algorithms may present a seemingly fair accuracy for both White and Asian student group and Black and Hispanic student group, but underneath the surface, the machine learning algorithms consistently produce a higher false positive rate for the White/Asian student groups while it consistently underestimates Black/Hispanic student group’s 12th grade math performance. This paper provides a comprehensive analysis and comparison of seven commonly used machine learning algorithms’ performances in terms of biased results towards the White and Asian student groups versus Black and Hispanic student groups.  \nThe thesis of Suyeon Hwang is approved.  \nGeorge Michailidis Nicolas Christou Ying Nian Wu, Committee Chair  \nUniversity of California, Los Angeles 2024  \nTo my former boyfriend and now fianc´e,   who—supported me from the beginning of this academic journey when it only was a dream—fervently encouraged and cheered me on throughout this program.  \niv  \nTABLE OF CONTENTS  \n1 Introduction ...................................... 1  \n2 Data .......................................... 3  \n2.1 Background of the Data ............................. 3  \n2.2 Data Cleaning ................................... 4  \n3 EDA .......................................... 6  \n4 Methodology ..................................... 12  \n4.1 K-Nearest Neighbor ................................ 12  \n4.2 Logistic Regression ................................ 14  \n4.3 Support Vector Machine ............................. 15  \n4.4 Random Forest .................................. 16  \n4.5 Ensemble Method ................................. 16  \n4.6 Neural Network .................................. 18  \n4.7 XGBoost ...................................... 20  \n5 Results ......................................... 22  \n5.1 KNN ........................................ 22  \n5.2 Logistic Regression ................................ 24  \n5.3 Support Vector Machine ............................. 28  \n5.4 Random Forest .................................. 31  \n5.5 Ensemble Method ................................. 36  \n5.6 Neural Network .................................. 38  \n5.7 XGBoost ...................................... 43  \n5.8 Overall Results .................................. 47  \n6 Discussion ....................................... 53  \n6.1 Limitations .................................... 53  \n6.2 Further Work ................................... 53  \n7 Conclusion ....................................... 56  \n8 References ....................................... 58  \nLIST OF FIGURES  \n3.1 Piechart of the Racial Distribution ......................... 6  \n3.2 Distribution of the WA and the BH Group ..................... 7  \n3.3 Distribution of 9th Grade Math Performance by Race............... 8  \n3.4 Correlation Coefficients of ","cbCaikKWLr4fLr4c","https://ap.wps.com/l/cbCaikKWLr4fLr4c","pdf",4713652,1,71,"English","en",105,"# Introduction\n# Data\n## Background of the Data\n## Data Cleaning\n# EDA\n# Methodology\n## K-Nearest Neighbor\n## Logistic Regression\n## Support Vector Machine\n## Random Forest\n## Ensemble Method\n## Neural Network\n## XGBoost\n# Results\n## KNN\n## Logistic Regression\n## Support Vector Machine\n## Random Forest\n## Ensemble Method\n## Neural Network\n## XGBoost\n## Overall Results\n# Discussion\n## Limitations\n## Further Work\n# Conclusion\n# References","[{\"question\":\"What dataset and scope does the thesis use to study bias?\",\"answer\":\"The study uses America’s longitudinal high school students dataset and focuses on racial differences in machine learning predictions related to secondary mathematics education outcomes.\"},{\"question\":\"How do the algorithms differ across racial groups in error patterns?\",\"answer\":\"The algorithms show seemingly fair accuracy across groups but produce a higher false positive rate for White/Asian groups and underestimate Black/Hispanic students’ 12th grade math performance.\"},{\"question\":\"Which machine learning algorithms are compared in the analysis?\",\"answer\":\"The thesis compares seven commonly used algorithms: K-Nearest Neighbor, Logistic Regression, Support Vector Machine, Random Forest, an Ensemble Method, a Neural Network, and XGBoost.\"}]",1785675011,179,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"racial-bias-in-machine-learning-algorithms-in-secondary-mathematics-education-master-thesis","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/racial-bias-in-machine-learning-algorithms-in-secondary-mathematics-education-master-thesis/117287/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What 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