Workshop paper
Forough Poursabzi-Sangdeh, Samira Samadi, Jennifer Wortman Vaughan, Hanna Wallach
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APA
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Poursabzi-Sangdeh, F., Samadi, S., Vaughan, J. W., & Wallach, H. A Human in the Loop is Not Enough: The Need for Human-Subject Experiments in Facial Recognition.
Chicago/Turabian
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Poursabzi-Sangdeh, Forough, Samira Samadi, Jennifer Wortman Vaughan, and Hanna Wallach. A Human in the Loop Is Not Enough: The Need for Human-Subject Experiments in Facial Recognition, n.d.
MLA
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Poursabzi-Sangdeh, Forough, et al. A Human in the Loop Is Not Enough: The Need for Human-Subject Experiments in Facial Recognition.
BibTeX Click to copy
@techreport{forough-a,
title = {A Human in the Loop is Not Enough: The Need for Human-Subject Experiments in Facial Recognition},
author = {Poursabzi-Sangdeh, Forough and Samadi, Samira and Vaughan, Jennifer Wortman and Wallach, Hanna}
}
Abstract
The deployment of facial recognition technologies in high-stakes scenarios has sparked widespread concerns about privacy, reliability, and fairness. A common response to these concerns is the suggestion of adding a human in the loop to provide oversight and ensure fairness and accountability. However, the effectiveness of this approach is not often studied empirically, and the literature shows that humans have biases of their own. In this position paper, we argue for the necessity of empirical studies on human-in-the-loop facial recognition systems. We outline several technical and ethical challenges that arise in conducting such controlled studies and interpreting their results conclusively. Our goal is to initiate a discussion about the best path forward for AI and HCI researchers to work together towards empirical and human-centered approaches to the design and evaluation of human-in-the-loop facial recognition systems.
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The deployment of facial recognition technologies in high-stakes scenarios has sparked widespread concerns about privacy, reliability, and fairness. A common response to these concerns is the suggestion of adding a human in the loop to provide oversight and ensure fairness and accountability. However, the effectiveness of this approach is not often studied empirically, and the literature shows that humans have biases of their own. In this position paper, we argue for the necessity of empirical studies on human-in-the-loop facial recognition systems. We outline several technical and ethical challenges that arise in conducting such controlled studies and interpreting their results conclusively. Our goal is to initiate a discussion about the best path forward for AI and HCI researchers to work together towards empirical and human-centered approaches to the design and evaluation of human-in-the-loop facial recognition systems.
