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Interactive Mitigation of Biases in Machine Learning Models
Interactive Mitigation of Biases in Machine Learning Models
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211153108
- ISBN
- 9798346500643
- DDC
- 378.1
- 서명/저자
- Interactive Mitigation of Biases in Machine Learning Models
- 발행사항
- [Sl] : Purdue University, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 101 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-05, Section: B.
- 주기사항
- Advisor: Fang, Shiaofen.
- 학위논문주기
- Thesis (Ph.D.)--Purdue University, 2024.
- 초록/해제
- 요약Bias and fairness issues in artificial intelligence algorithms are major concerns as people do not want to use AI software they cannot trust. This work uses college admissions data as a case study to develop methodology to define and detect bias, and then introduces a new method for interactive bias mitigation.Admissions data spanning six years was used to create machine learning-based predictive models to determine whether a given student would be directly admitted into the School of Science under various scenarios at a large urban research university. During this time, submission of standardized test scores as part of a student's application became optional which led to interesting questions about the impact of standardized test scores on admission decisions. We developed and analyzed predictive models to understand which variables are important in admissions decisions, and how the decision to exclude test scores affects the demographics of the students who are admitted.Then, using a variety of bias and fairness metrics, we analyzed these predictive models to detect biases the models may carry with respect to three variables chosen to represent sensitive populations: gender, race, and whether a student was the first in his/her family to attend college. We found that high accuracy rates can mask underlying algorithmic bias towards these sensitive groups.Finally, we describe our method for bias mitigation which uses a combination of machine learning and user interaction. Because bias is intrinsically a subjective and context-dependent matter, it requires human input and feedback. Our approach allows the user to iteratively and incrementally adjust bias and fairness metrics to change the training dataset for an AI model to make the model more fair. This interactive bias mitigation approach was then used to successfully decrease the biases in three AI models in the context of undergraduate student admissions.
- 일반주제명
- Admissions policies
- 일반주제명
- College admissions
- 일반주제명
- COVID-19
- 기타저자
- Purdue University.
- 기본자료저록
- Dissertations Abstracts International. 86-05B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520250211153108
■006m o d
■007cr#unu||||||||
■020 ▼a9798346500643
■035 ▼a(MiAaPQ)AAI31682036
■035 ▼a(MiAaPQ)Purdue26076964
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a378.1
■1001 ▼aBusum, Kelly Van.
■24510▼aInteractive Mitigation of Biases in Machine Learning Models
■260 ▼a[Sl]▼bPurdue University▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a101 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-05, Section: B.
■500 ▼aAdvisor: Fang, Shiaofen.
■5021 ▼aThesis (Ph.D.)--Purdue University, 2024.
■520 ▼aBias and fairness issues in artificial intelligence algorithms are major concerns as people do not want to use AI software they cannot trust. This work uses college admissions data as a case study to develop methodology to define and detect bias, and then introduces a new method for interactive bias mitigation.Admissions data spanning six years was used to create machine learning-based predictive models to determine whether a given student would be directly admitted into the School of Science under various scenarios at a large urban research university. During this time, submission of standardized test scores as part of a student's application became optional which led to interesting questions about the impact of standardized test scores on admission decisions. We developed and analyzed predictive models to understand which variables are important in admissions decisions, and how the decision to exclude test scores affects the demographics of the students who are admitted.Then, using a variety of bias and fairness metrics, we analyzed these predictive models to detect biases the models may carry with respect to three variables chosen to represent sensitive populations: gender, race, and whether a student was the first in his/her family to attend college. We found that high accuracy rates can mask underlying algorithmic bias towards these sensitive groups.Finally, we describe our method for bias mitigation which uses a combination of machine learning and user interaction. Because bias is intrinsically a subjective and context-dependent matter, it requires human input and feedback. Our approach allows the user to iteratively and incrementally adjust bias and fairness metrics to change the training dataset for an AI model to make the model more fair. This interactive bias mitigation approach was then used to successfully decrease the biases in three AI models in the context of undergraduate student admissions.
■590 ▼aSchool code: 0183.
■650 4▼aAdmissions policies
■650 4▼aCollege admissions
■650 4▼aCOVID-19
■690 ▼a0800
■71020▼aPurdue University.
■7730 ▼tDissertations Abstracts International▼g86-05B.
■790 ▼a0183
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17164962▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
