[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122373-en":3,"doc-seo-122373-105":29,"detail-sidebar-cat-0-en-105":89},{"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":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":20,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},122373,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Measuring the Immeasurable-Perspectives on Best Practices When Operationalizing Machine Learning Fairness for Recommender Systems","This document is a Thesis Approval Form for Jessie Smith, a Doctoral candidate in Information Science at the University of Colorado Boulder. The thesis, titled \"Measuring the Immeasurable: Perspectives on Best Practices When Operationalizing Machine Learning Fairness for Recommender Systems,\" focuses on critical research into the operationalization of fairness in machine learning, specifically within recommender systems. The form indicates that the research involved human subjects, requiring Institutional Review Board (IRB) approval, with protocol number 24-0142. It also confirms no use of live animals or animal tissue, negating the need for Institutional Animal Care and Use Committee (IACUC) approval. The document serves as a formal record of committee review and approval, certifying that both the content and presentation of the thesis meet scholarly standards. Signatories include Committee Chair Casey Fiesler and Committee Member Robin Burke, with signatures and dates affixed, confirming their examination and acceptance of the final thesis submission for scholarly work in the field of Information Science.","Docusign Envelope ID: 19B0F9B2-FE34-4718-AC3A-42FDC332DECE  \nStudent Name  Jessie Smith  Student ID *********  Doctoral  Degree in  Information Science   \nThesis Title:  \nMeasuring the Immeasurable: Perspectives on Best Practices When Operationalizing Machine Learning Fairness for Recommender Systems  \nHave you performed research involving human subjects which requires approval from the Institutional Review Board (IRB)?  X  Yes   No  \nIRB Protocol Number 24-0142   \nHave you used live animals, animal tissue, or observational animal work which requires approval from the Institutional Animal Care and Use Committee (IACUC)?   Yes  X  No  \nIACUC Protocol Number    \nAttach the final copy of thesis/dissertation for committee review. While formatting changes maybe requested by the Graduate School, the content of the attached document should be final.  \nApprovals:  \nCommittee Chair Name  Casey Fies_ler _________________________________________Signature _____________________________________ Date Signed  3/25/2025   \nCommittee Member Name _Rob_in Burke___________________________________________Signature _____________________________________ Date Signed   \nThe final copy of this thesis has been examined by the signatories, and we find that both the content and the form meet acceptable presentation standards of scholarly work in the abovementioned discipline.","cbCaivOfAGqrOUD1","https://ap.wps.com/l/cbCaivOfAGqrOUD1","pdf",241141,1,"English","en",105,"# Thesis Approval Form\n## Student Information\n## Thesis Title\n## IRB/IACUC Approval\n## Committee Approvals","[{\"question\":\"What is the title of Jessie Smith's thesis?\",\"answer\":\"Jessie Smith's thesis is titled \\\"Measuring the Immeasurable: Perspectives on Best Practices When Operationalizing Machine Learning Fairness for Recommender Systems.\\\"\"},{\"question\":\"Did Jessie Smith's research involve human subjects?\",\"answer\":\"Yes, Jessie Smith's research involved human subjects and required approval from the Institutional Review Board (IRB) with protocol number 24-0142.\"},{\"question\":\"What is the purpose of this document?\",\"answer\":\"This document is a Thesis Approval Form, signifying that the committee has examined the thesis and found its content and form to meet acceptable presentation standards for scholarly work.\"}]","Measuring the Immeasurable-Perspectives on Best Practices When Operationalizing Machine Learning Fairness for Recommender Systems | 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