Dissertation/Thesis Abstract

Producing Faces and Seeing Machines
by Haeberle-Gosweiler, Nathaniel Robert, M.A., Villanova University, 2020, 44; 28031411
Abstract (Summary)

This research examines facial recognition as a relational capacity of networked human and machine agents. I argue that this approach is necessary to understand the complexities of current facial recognition technology in digital communication platforms. This work develops a descriptive architecture (Access, Attention, Action) that frames and examines recognition as a distributed production with ethical, material, and social ramifications. This framework highlights the remediation of biased logics and methods within networks of human and machine agents. In describing the basic capacities of facial recognition, I challenge human-centric notions of facial recognition. The resulting discussion of the AAA framework shows its ability to translate the socio-technical complexity of facial recognition into a perspective readily accessible to the communication discipline.

Indexing (document details)
Advisor: Oswald, Kathleen
Commitee: Coonfield, Gordon, Theiner, Georg
School: Villanova University
Department: Communication
School Location: United States -- Pennsylvania
Source: MAI 82/5(E), Masters Abstracts International
Subjects: Communication, Information Technology
Keywords: AI ethics, Algorithm, Faces, Facial recognition, Humachine, Surveillance
Publication Number: 28031411
ISBN: 9798691227486
Copyright © 2021 ProQuest LLC. All rights reserved. Terms and Conditions Privacy Policy Cookie Policy