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Title: Facial Recognition: Understanding Concerns and Privacy Attitudes Across Increasingly Diverse Deployment Scenarios
The rapid growth of facial recognition technology across ever more diverse contexts calls for a better understanding of how people feel about these deployments — whether they see value in them or are concerned about their privacy, and to what extent they have generally grown accustomed to them. We present a qualitative analysis of data gathered as part of a 10-day experience sampling study with 123 participants who were presented with realistic deployment scenarios of facial recognition as they went about their daily lives. Responses capturing their attitudes towards these deployments were collected both in situ and through daily evening surveys, in which participants were asked to reflect on their experiences and reactions. Ten follow-up interviews were conducted to further triangulate the data from the study. Our results highlight both the perceived benefits and concerns people express when faced with different facial recognition deployment scenarios. Participants reported concerns about the accuracy of the technology, including possible bias in its analysis, privacy concerns about the type of information being collected or inferred, and more generally, the dragnet effect resulting from the widespread deployment. Based on our findings, we discuss strategies and guidelines for informing the deployment of facial recognition, particularly focusing on ensuring that people are given adequate levels of transparency and control.
Authors:
; ;
Award ID(s):
1801316
Publication Date:
NSF-PAR ID:
10289280
Journal Name:
USENIX Symposium on Usable Privacy and Security
Page Range or eLocation-ID:
246-262
Sponsoring Org:
National Science Foundation
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