Date of Award
University Scholars Director
Dr. Christine Chaney
First Advisor/Committee Member
Second Advisor/Committee Member
Dr. Carlos Arias
Artificial Intelligence, bias, error rates, ethics, distributed responsibility
Computer Vision Machine Learning (CVML) in the application of facial recognition is currently being researched, developed, and deployed across the world. It is of interest to governments, technology companies, and consumers. However, fundamental issues remain related to human rights, error rates, and bias. These issues have the potential to create societal backlash towards the technology which could limit its benefits as well as harm people in the process. To develop facial recognition technology that will be beneficial to society in and beyond the next decade, society must put ethics at the forefront. Drawing on AI4People’s adaption of bioethics for AI, Luciano Floridi’s distributed morality framework, Kate Crawford’s definition of harms of representation, and Microsoft’s leadership in facial recognition ethics within the industry, this paper explores stakeholder responsibility within CVML to create the best integration of CVML for society. The paper attempts to connect ethics with praxis in making decisions related to CVML.
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Blank, Abagayle Lee, "Computer Vision Machine Learning and Future-Oriented Ethics" (2019). Honors Projects. 107.