{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/122094"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/122094","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"The fallacy of equating \"blindness\" with fairness : ensuring trust in machine learning applications to consumer credit","abstract":"Fifty years ago, the United States Congress coalesced around a vision for fair consumer credit: equally accessible by all consumers, and developed on accurate and relevant information, with controls for consumer privacy. In two foundational pieces of legislation, the Fair Credit Reporting Act (FCRA) and the Equal Credit Opportunity Act (ECOA), legislators described mechanisms by which these goals would be met, including, most notably, prohibiting certain information, such as a consumer's race, as the basis for credit decisions, under the assumption that being \"blind\" to this information would prevent wrongful discrimination. While the policy goals for fair credit are still valid today, the mechanisms designed to achieve them are no longer effective.","abstract_html":"Fifty years ago, the United States Congress coalesced around a vision for fair consumer credit: equally accessible by all consumers, and developed on accurate and relevant information, with controls for consumer privacy. In two foundational pieces of legislation, the Fair Credit Reporting Act (FCRA) and the Equal Credit Opportunity Act (ECOA), legislators described mechanisms by which these goals would be met, including, most notably, prohibiting certain information, such as a consumer's race, as the basis for credit decisions, under the assumption that being "blind" to this information would prevent wrongful discrimination. While the policy goals for fair credit are still valid today, the mechanisms designed to achieve them are no longer effective.","abstract_has_math":false,"creators":["Abuhamad, Grace M.(Grace Marie)"],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Institute for Data, Systems, and Society","school":null,"contributors":[],"advisors":["Daniel J. Weitzner."],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019","date_published":"2019","updated_at":"2026-07-22T22:21:24Z","subjects":["Institute for Data, Systems, and Society.","Technology and Policy Program."],"languages":["eng"],"rights":["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."],"rights_urls":["http://dspace.mit.edu/handle/1721.1/7582"],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1721.1/122094","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Daniel J. Weitzner."]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. Institute for Data, Systems, and Society","ESD","Technology and Policy Program","IDSS"]},{"key":"dc:contributor.other","label":"Dc Contributor Other","values":["Massachusetts Institute of Technology. Institute for Data, Systems, and Society.","Technology and Policy Program."]},{"key":"dc:creator","label":"Author","values":["Abuhamad, Grace M.(Grace Marie)"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2019-09-16T18:17:15Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2019-09-16T18:17:15Z"]},{"key":"dc:date.issued","label":"Date","values":["2019"]},{"key":"dc:publisher","label":"Institution","values":["Massachusetts Institute of Technology"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Institute for Data, Systems, and Society.","Technology and Policy Program."]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["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."]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://dspace.mit.edu/handle/1721.1/7582"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/1721.1/122094"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.","Thesis: S.M. in Technology and Policy, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, 2019","Cataloged from student-submitted PDF version of thesis.","Includes bibliographical references (pages 75-82)."]},{"key":"dc:description.abstract","label":"Abstract","values":["Fifty years ago, the United States Congress coalesced around a vision for fair consumer credit: equally accessible by all consumers, and developed on accurate and relevant information, with controls for consumer privacy. In two foundational pieces of legislation, the Fair Credit Reporting Act (FCRA) and the Equal Credit Opportunity Act (ECOA), legislators described mechanisms by which these goals would be met, including, most notably, prohibiting certain information, such as a consumer's race, as the basis for credit decisions, under the assumption that being \"blind\" to this information would prevent wrongful discrimination. While the policy goals for fair credit are still valid today, the mechanisms designed to achieve them are no longer effective.","The consumer credit industry is increasingly interested in using new data and machine learning modeling techniques to determine consumer creditworthiness, and with these technological advances come new risks not mitigated by existing mechanisms. This thesis evaluates how these \"alternative\" credit processes pose challenges to the mechanisms established in the FCRA and the ECOA and their vision for fairness. \"Alternative\" data and models facilitate inference or prediction of consumer information, which make them non-compliant. In particular, this thesis investigates the idea that \"blindness\" to certain attributes hinders consumer fairness more than it helps since it limits the ability to determine whether wrongful discrimination has occurred and to build better performing models for populations that have been historically underscored.","This thesis concludes with four recommendations to modernize fairness mechanisms and ensure trust in the consumer credit system by: 1) expanding the definition of consumer report under the FCRA; 2) encouraging model explanations and transparency; 3) requiring self-testing using prohibited information; and 4) permitting the use of prohibited information to allow for more comprehensive models."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["S.M. in Technology and Policy"]},{"key":"dc:title","label":"Title","values":["The fallacy of equating \"blindness\" with fairness : ensuring trust in machine learning applications to consumer credit"]}]}],"canonical_facts":{"dc:contributor.advisor":["Daniel J. Weitzner."],"dc:contributor.department":["Massachusetts Institute of Technology. Institute for Data, Systems, and Society","ESD","Technology and Policy Program","IDSS"],"dc:contributor.other":["Massachusetts Institute of Technology. Institute for Data, Systems, and Society.","Technology and Policy Program."],"dc:creator":["Abuhamad, Grace M.(Grace Marie)"],"dc:date.accessioned":["2019-09-16T18:17:15Z"],"dc:date.available":["2019-09-16T18:17:15Z"],"dc:date.issued":["2019"],"dc:description":["This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.","Thesis: S.M. in Technology and Policy, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, 2019","Cataloged from student-submitted PDF version of thesis.","Includes bibliographical references (pages 75-82)."],"dc:description.abstract":["Fifty years ago, the United States Congress coalesced around a vision for fair consumer credit: equally accessible by all consumers, and developed on accurate and relevant information, with controls for consumer privacy. In two foundational pieces of legislation, the Fair Credit Reporting Act (FCRA) and the Equal Credit Opportunity Act (ECOA), legislators described mechanisms by which these goals would be met, including, most notably, prohibiting certain information, such as a consumer's race, as the basis for credit decisions, under the assumption that being \"blind\" to this information would prevent wrongful discrimination. While the policy goals for fair credit are still valid today, the mechanisms designed to achieve them are no longer effective.","The consumer credit industry is increasingly interested in using new data and machine learning modeling techniques to determine consumer creditworthiness, and with these technological advances come new risks not mitigated by existing mechanisms. This thesis evaluates how these \"alternative\" credit processes pose challenges to the mechanisms established in the FCRA and the ECOA and their vision for fairness. \"Alternative\" data and models facilitate inference or prediction of consumer information, which make them non-compliant. In particular, this thesis investigates the idea that \"blindness\" to certain attributes hinders consumer fairness more than it helps since it limits the ability to determine whether wrongful discrimination has occurred and to build better performing models for populations that have been historically underscored.","This thesis concludes with four recommendations to modernize fairness mechanisms and ensure trust in the consumer credit system by: 1) expanding the definition of consumer report under the FCRA; 2) encouraging model explanations and transparency; 3) requiring self-testing using prohibited information; and 4) permitting the use of prohibited information to allow for more comprehensive models."],"dc:description.degree":["S.M. in Technology and Policy"],"dc:identifier.uri":["https://hdl.handle.net/1721.1/122094"],"dc:language.iso":["eng"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["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."],"dc:rights.uri":["http://dspace.mit.edu/handle/1721.1/7582"],"dc:subject":["Institute for Data, Systems, and Society.","Technology and Policy Program."],"dc:title":["The fallacy of equating \"blindness\" with fairness : ensuring trust in machine learning applications to consumer credit"],"dc:type":["Thesis"],"thesis:degree_name":["Master"]},"updated_at":"2026-07-22T22:21:24Z"}