| dc.description.abstract | This dissertation develops a digital cultural rhetorics critique of predictive algorithms that reproduce and reinforce existing inequalities, ultimately calling on academics, activists, and the public to organize for increased ethics, transparency, and accountability. Using a case study framework, I investigate how algorithms manipulate, impede, and commodify identification among individuals and groups to highlight the harm—particularly for underrepresented and marginalized people—caused by increased algorithmic influence that decreases individual agency. The first case study examines the Disability March, a virtual march that accompanied the 2017 Women’s March on Washington, as a meta-cyberprotest that allowed for collective identification in the absence of predictive algorithms. The second case study documents a small-scale experiment that I conducted using automated bots to understand how the exchange of identification between Twitter’s “Who to Follow” recommendation algorithm and individual users contributes to information echo chambers, increasing inequality and promoting division. The final case study interrogates the “glitches,” or points of ethical tension, within and across interviews I conducted with predictive marketers to understand how targeted advertising uses algorithm-driven identification to surveil, sort, and profile individuals for commercial gain. By drawing on work from feminist theory, critical media studies, and critical code studies, I develop a transdisciplinary interrogation of the impact of algorithmic identification in public, corporate, and governmental spheres and offer potential pedagogical and practical interventions to evade and expose the network of digital surveillance that surrounds the web. | |
