{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/113921"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/113921","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Fair-doctor: Detecting and mitigating unfairness in neural networks","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-04-06 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-04-06 without embargo terms","abstract_has_math":false,"creators":["Adhikari, Rittika"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Koyejo, Oluwasanmi"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-04-29T21:35:50Z","date_published":"2022-04-29T21:35:50Z","updated_at":"2026-07-22T22:24:53Z","subjects":["Computer science"],"languages":["en","eng"],"rights":["Copyright 2021 Rittika Adhikari"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/113921","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Koyejo, Oluwasanmi"]},{"key":"dc:creator","label":"Author","values":["Adhikari, Rittika"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-04-29T21:35:50Z","2021-12","2021-12-09"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Computer science"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2021 Rittika Adhikari"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/113921"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-04-06 without embargo terms","The student, Rittika Adhikari, accepted the attached license on 2021-12-08 at 10:12.","The student, Rittika Adhikari, submitted this Thesis for approval on 2021-12-08 at 14:20.","This Thesis was approved for publication on 2021-12-09 at 08:57.","DSpace SAF Submission Ingestion Package generated from Vireo submission #17408 on 2022-04-06 at 17:11:08","Made available in DSpace on 2022-04-29T21:35:50Z (GMT). No. of bitstreams: 3 ADHIKARI-THESIS-2021.pdf: 4421594 bytes, checksum: 44f4855328660ac2641bef0e7defa17d (MD5) Masters' Thesis.zip: 4172154 bytes, checksum: 47ec26d8f69477468797fa1b6ad3a647 (MD5) LICENSE.txt: 4213 bytes, checksum: 5a9fb55ead610f3ffff271f2dc0b4e0a (MD5) Previous issue date: 2021-12-09","\"Important decisions are increasingly based directly on predictions from classifiers; for example, machine learning models are now being used to facilitate autonomous vehicles, predict stock market trends, diagnose illnesses, and so much more. However, users fundamentally understand very little about how these black box classifiers come to make decisions, and whether these predictions are unbiased. With the more prevalent adoption of these systems, it is crucial that we must be able to both explain and understand what concepts our models utilize to make predictions to ensure that we are building unbiased, interpretable models. 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To facilitate this, we propose Fair-Doctor, a pipeline which diagnoses unfairness, treats it, and follows up to ensure that the algorithmic bias has been mitigated. We utilize TCAV (Testing With Concept Activation Vectors), a state-of-the-art interpretability tool, to diagnose unfairness. We also introduce a novel adversarial fairness loss function, which works to remove the specified unfairness in the model. We evaluate this architecture on a simple CNN trained on CelebA to predict how \"\"young\"\" a person looks. Our results demonstrate that we are able to successfully reduce the bias in this model.\""],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/113921"],"dc:language":["en","eng"],"dc:rights":["Copyright 2021 Rittika Adhikari"],"dc:subject":["Computer science"],"dc:title":["Fair-doctor: Detecting and mitigating unfairness in neural networks"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:53Z"}