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Lin Li and Y. Karen Zheng. Zhu, Jessica H. Massachusetts Institute of Technology. Operations Research Center. Massachusetts Institute of Technology. Operations Research Center Sloan School of Management 2019-10-04T21:31:23Z 2019-10-04T21:31:23Z 2019 2019 https://hdl.handle.net/1721.1/122384 1120104049 This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Thesis: S.M., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2019 Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 75-80). Black box machine learning methods have allowed researchers to design accurate models using large amounts of data at the cost of interpretability. Model interpretability not only improves user buy-in, but in many cases provides users with important information. Especially in the case of the classification problems addressed in this thesis, the ideal model should not only provide accurate predictions, but should also inform users of how features affect the results. My research goal is to solve real-world problems and compare how different classification models affect the outcomes and interpretability. To this end, this thesis is divided into two parts: food safety risk analysis and human trafficking detection. The first half analyzes the characteristics of supermarket suppliers in China that indicate a high risk of food safety violations. Contrary to expectations, supply chain dispersion, internal inspections, and quality certification systems are not found to be predictive of food safety risk in our data. The second half focuses on identifying human trafficking, specifically sex trafficking, advertisements hidden amongst online classified escort service advertisements. We propose a novel but interpretable keyword detection and modeling pipeline that is more accurate and actionable than current neural network approaches. The algorithms and applications presented in this thesis succeed in providing users with not just classifications but also the characteristics that indicate food safety risk and human trafficking ads. by Jessica H. Zhu. S.M. S.M. Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center 80 pages eng Massachusetts Institute of Technology MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 Operations Research Center. Detecting food safety risks and human tracking using interpretable machine learning methods/ Thesis application/pdf 2019-10-04T21:31:22Z Publication Master Sloan OperRes unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="556bbe31-5162-47ae-95c6-000d11cc3ada"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Detecting food safety risks and human tracking using interpretable machine learning methods/</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2019</PublicationDate> <Authors> <Author> <DisplayName>Zhu, Jessica H.</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Operations Research Center.</Keyword> <Abstract>Black box machine learning methods have allowed researchers to design accurate models using large amounts of data at the cost of interpretability. Model interpretability not only improves user buy-in, but in many cases provides users with important information. Especially in the case of the classification problems addressed in this thesis, the ideal model should not only provide accurate predictions, but should also inform users of how features affect the results. My research goal is to solve real-world problems and compare how different classification models affect the outcomes and interpretability. To this end, this thesis is divided into two parts: food safety risk analysis and human trafficking detection. The first half analyzes the characteristics of supermarket suppliers in China that indicate a high risk of food safety violations. Contrary to expectations, supply chain dispersion, internal inspections, and quality certification systems are not found to be predictive of food safety risk in our data. The second half focuses on identifying human trafficking, specifically sex trafficking, advertisements hidden amongst online classified escort service advertisements. We propose a novel but interpretable keyword detection and modeling pipeline that is more accurate and actionable than current neural network approaches. The algorithms and applications presented in this thesis succeed in providing users with not just classifications but also the characteristics that indicate food safety risk and human trafficking ads.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>