{"id":{"repo_id":"buffalo","oai_identifier":"oai:ubir.buffalo.edu:10477/84101"},"canonical_url":"https://search.dev.ndltd.org/etd/buffalo/oai:ubir.buffalo.edu:10477/84101","repository":{"repo_id":"buffalo","name":"Buffalo","base_url":"https://ubir.buffalo.edu/oai/request"},"display":{"title":"Towards Better Interpretability of Machine Learning-Based Decision Support Systems","abstract":"Ph.D.","abstract_html":"Ph.D.","abstract_has_math":false,"creators":["Wang, Xiaomei; 0000-0002-3432-0227"],"institution":"State University of New York at Buffalo","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Bisantz, Ann","Industrial and Systems Engineering"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-06-21T15:47:56Z","date_published":"2022-06-21T15:47:56Z","updated_at":"2026-07-27T19:05:30Z","subjects":["industrial engineering"],"languages":["eng"],"rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/10477/84101","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Bisantz, Ann","Industrial and Systems Engineering"]},{"key":"dc:creator","label":"Author","values":["Wang, Xiaomei; 0000-0002-3432-0227"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-06-21T15:47:56Z","2020"]},{"key":"dc:publisher","label":"Institution","values":["State University of New York at Buffalo"]},{"key":"dc:type","label":"Dc Type","values":["Text","Dissertation"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["industrial engineering"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/10477/84101"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Ph.D.","Decision support systems are systems designed to support decision making processes. In recent years, researchers have applied machine learning for the purpose of supporting decision making in a wide range of domains. Yet, how to communicate the system recommendations to human decision makers, and how human decision makers would use and trust the machine learning recommendations remained understudied. To solve this problem, we first conducted work analysis to understand how machine learning could be incorporated into a real world decision making task. Then, we developed machine learning models, and integrated the machine learning recommendations to an experimental test bed to support a human-in-the-loop study of how human make decisions with machine learning recommendations. Finally, we did experiment with the test bed to understand how decision makers would use a decision support system with embedded machine learning recommendations, and how that impacts their decision making strategies.","**To request an accessible version of the file(s) associated with this item, contact library@buffalo.edu. Please include the item's persistent URL [http://hdl.handle.net/. . .] in your request.**"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Towards Better Interpretability of Machine Learning-Based Decision Support Systems"]}]}],"canonical_facts":{"dc:contributor":["Bisantz, Ann","Industrial and Systems Engineering"],"dc:creator":["Wang, Xiaomei; 0000-0002-3432-0227"],"dc:date":["2022-06-21T15:47:56Z","2020"],"dc:description":["Ph.D.","Decision support systems are systems designed to support decision making processes. In recent years, researchers have applied machine learning for the purpose of supporting decision making in a wide range of domains. Yet, how to communicate the system recommendations to human decision makers, and how human decision makers would use and trust the machine learning recommendations remained understudied. To solve this problem, we first conducted work analysis to understand how machine learning could be incorporated into a real world decision making task. Then, we developed machine learning models, and integrated the machine learning recommendations to an experimental test bed to support a human-in-the-loop study of how human make decisions with machine learning recommendations. Finally, we did experiment with the test bed to understand how decision makers would use a decision support system with embedded machine learning recommendations, and how that impacts their decision making strategies.","**To request an accessible version of the file(s) associated with this item, contact library@buffalo.edu. Please include the item's persistent URL [http://hdl.handle.net/. . .] in your request.**"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/10477/84101"],"dc:language":["eng"],"dc:publisher":["State University of New York at Buffalo"],"dc:rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"dc:subject":["industrial engineering"],"dc:title":["Towards Better Interpretability of Machine Learning-Based Decision Support Systems"],"dc:type":["Text","Dissertation"]},"updated_at":"2026-07-27T19:05:30Z"}