Artificial Minority: Combating Unconscious Biases Embedded in Facial Recognition Technology

Emily Tse
Exhibition and Experience Design

About this Item

Title
Artificial Minority: Combating Unconscious Biases Embedded in Facial Recognition Technology
Contributor Names
Tse, Emily (Author)
Lyons, Christina (Thesis advisor)
Ferwerda, Christina (Thesis advisor)
Fashion Institute of Technology, State University of New York. Exhibition and Experience Design (Degree granting institution)
Date
2023
Degree Information
M.A. Fashion Institute of Technology, State University of New York 2023
Department: Exhibition and Experience Design
Source: Masters Abstracts International, Volume: 85-08
Advisors: Christina Lyons; Christina Ferwerda
Committee members: Brenda Cowan; Brooke Carlson
Abstract
This Master's thesis qualifying paper is a compilation of the thesis research and design application to spread public awareness of algorithmic injustice in facial recognition technology.Part one is the thesis research exploring facial recognition technology and reasons why the technology is biased. The inequality impedes civil rights, such as the right to be free from discrimination and the right to privacy. This section also discusses three case studies of facial recognition audits and how participatory museum techniques are used to encourage dialogue and promote empathy. Additionally, primary research was conducted in which expert interviews contributed to the understanding of technology as a system of power and ways to orchestrate workshop programming. Two iterations of prototype testing and a survey collected opinions on facial recognition technology.Part two of the thesis paper contains the concept and design development of the exhibition project. The exhibition used the participatory museum techniques researched in part one to encourage dialogue on algorithmic justice. The client, site, and audience were chosen based on the research in part one. The design development contains details of the spatial design and how the graphic system was applied to the environmental graphics. A study model of the exhibition space was constructed.
Keywords: Algorithmic justice, Experience design, Facial recognition, Participatory museum, Responsible AI
Subject
Design
Ethics
Artificial intelligence
Interactive art
Rights
In Copyright
The copyright for this work is held by its author/creator(s). Usage of this material beyond what is permitted by copyright law must first be cleared with the rights-holder(s). This work has been made available online by the Fashion Institute of Technology Gladys Marcus Library strictly for research and educational purposes. If you are the copyright holder for this work and have any objections to this work being made available online, please notify us immediately at [email protected].
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Identifier
FIT Repository ID: etd_000954
pqdiss: 30819297
URN/ISBN: 9798381446401
Related Materials
Also available from ProQuest
Type
Text
Thesis
Language
English
Publisher
Ann Arbor : ProQuest Dissertations & Theses,

Citation

Tse, E. (2023). Artificial Minority: Combating Unconscious Biases Embedded in Facial Recognition Technology [Master's thesis, Fashion Institute of Technology, State University of New York]. FIT Institutional Repository. https://institutionalrepository.fitnyc.edu/item/155919
Tse, Emily. Artificial Minority: Combating Unconscious Biases Embedded in Facial Recognition Technology. 2023. Fashion Institute of Technology, State University of New York, Master's thesis. FIT Digital Repository, https://institutionalrepository.fitnyc.edu/item/155919
Tse, Emily. "Artificial Minority: Combating Unconscious Biases Embedded in Facial Recognition Technology." Master's thesis, Fashion Institute of Technology, State University of New York, 2023. https://institutionalrepository.fitnyc.edu/item/155919