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Thomas W. Malone. Wang, Sibo,M. Eng.Massachusetts Institute of Technology. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2019-12-05T18:03:18Z 2019-12-05T18:03:18Z 2019 2019 https://hdl.handle.net/1721.1/123114 1128184807 This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019 Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 53-54). How can we evaluate peoples moral character? Judging someones moral character can be a difficult task, especially through only short interactions such as an interview. In this thesis, I examined the possibility of using machine learning techniques to predict peoples propensity to commit certain unethical behavior based on analyzing their responses to interview questions aimed at testing their moral character. I experimented with a number of machine learning algorithms and text analysis techniques and created models for predicting unethical behavior based on the interview response texts. The model results are then compared to 1. human judge ratings of the interviewees moral character and 2. human judge predictions of the interviewees tendency to cheat based on reading the same interview responses. Overall, we showed that machine learning models can explain parts of the variance in unethical behavior that were not explained by human judge ratings. by Sibo Wang. M.Eng. M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science 54 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 Electrical Engineering and Computer Science. Predicting unethical behavior from interview responses : machine learning models versus human judges Thesis application/pdf 2019-12-05T18:03:16Z Publication Master EECS unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="f1267c93-0788-404e-b6da-31059c8ae785"> <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>Predicting unethical behavior from interview responses : machine learning models versus human judges</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2019</PublicationDate> <Authors> <Author> <DisplayName>Wang, Sibo,M. Eng.Massachusetts Institute of Technology.</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>Electrical Engineering and Computer Science.</Keyword> <Abstract>How can we evaluate peoples moral character? Judging someones moral character can be a difficult task, especially through only short interactions such as an interview. In this thesis, I examined the possibility of using machine learning techniques to predict peoples propensity to commit certain unethical behavior based on analyzing their responses to interview questions aimed at testing their moral character. I experimented with a number of machine learning algorithms and text analysis techniques and created models for predicting unethical behavior based on the interview response texts. The model results are then compared to 1. human judge ratings of the interviewees moral character and 2. human judge predictions of the interviewees tendency to cheat based on reading the same interview responses. Overall, we showed that machine learning models can explain parts of the variance in unethical behavior that were not explained by human judge ratings.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>