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Accepted Paper:

Against automated attention tracking in the classroom  
Shannon Brick (Georgetown) Alicia Patterson (Oregon State University)

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Short abstract:

We explore the ethical challenges posed by facial recognition systems to track and measure student attention in classrooms. We should resist such tools as they stand to increase inequality, corporatization, and risk undermining the central goals of education.

Long abstract:

This paper undertakes a comprehensive evaluation of the epistemological and ethical challenges posed by computer vision technologies which use facial recognition to track student attention in classrooms. We argue that its use is not morally justified — educators should resist the adoption of such tools, and researchers should cease developing them. We center our analysis around two central questions. First, “Can we reasonably hope that this kind of technology will be able to augment or improve teachers’ capacity to serve their students?” Second, supposing that the answer to this question is “yes” (something we argue there are strong reasons to doubt): “Do the potential educational benefits of this technology outweigh the broader ethical costs associated with institutionalizing this kind of student surveillance?” The answer to this question is a resounding “no.” In a climate in which public distrust of teachers is growing, and increasing numbers of teachers are choosing to leave the profession, the argument of this paper is thus part of a broader effort to mobilize resistance against the development of tools that would not just increase surveillance of students, but entrench reliance on corporate partnerships in education, and increase inequality in our society more broadly.

Traditional Open Panel P394
Biometrics and their calculative logics
  Session 3 Wednesday 17 July, 2024, -