The fallacy of equating "blindness" with fairness : ensuring trust in machine learning applications to consumer credit

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.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Institute for Data, Systems, and Society

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• Abuhamad, Grace M.(Grace Marie)

Advisor dc:contributor.advisor

• Daniel J. Weitzner.

• Institute for Data, Systems, and Society.
• Technology and Policy Program.

Statement 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/122094

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/122094