Public acceptance of facial recognition technology: Surveying attitudes, preferences, and concerns to inform policy development
type: CreativeWork
description: One application of artificial intelligence (AI) in public sector governance is facial recognition technology (FRT), which is used to identify or discover individuals by comparing an image of their face to a database of known faces for a match. Along with these applications, however, concerns surrounding FRT use by governments have emerged as critics raise issues not only about the technology itself, but also the implications for the expansion of the ‘surveillance society’ and specific concerns such as demonstrated racial biases in FRT. While FRT continues to be developed and used, and governments struggle to develop a legislative and regulatory response, the question of public acceptance of FRT emerges as timely and important for ongoing policy deliberations. This thesis reports on an empirical test of what the public deems acceptable in the context of FRT applications used in the public sector, focusing on citizens’ attitudes towards, preferences for, and concerns about public sector use of FRT. A survey of 266 residents of two comparative provincial jurisdictions in Canada — Saskatchewan and Ontario — gathered information on attitudes towards FRT used in a variety of settings using nine hypothetical scenario vignettes of FRT use. Descriptive statistics, correlation statistics, and regression analysis is used to identify which socio-economic and demographic factors predict support for public sector use of FRT. The findings from this research have implications for the adoption of FRT in the public sector and for the development of legislation and regulation in response to its use. Results indicate safety and security as a public priority and a general overall support for FRT use by public sector agencies regardless of sociodemographic characteristics, with the least favourable use being general public surveillance. Citizen service uses (particularly airport security) yielded the highest levels of support. The use of FRT by public sector agencies will require overt p…
abstract: One application of artificial intelligence (AI) in public sector governance is facial recognition technology (FRT), which is used to identify or discover individuals by comparing an image of their face to a database of known faces for a match. Along with these applications, however, concerns surrounding FRT use by governments have emerged as critics raise issues not only about the technology itself, but also the implications for the expansion of the ‘surveillance society’ and specific concerns such as demonstrated racial biases in FRT. While FRT continues to be developed and used, and governments struggle to develop a legislative and regulatory response, the question of public acceptance of FRT emerges as timely and important for ongoing policy deliberations. This thesis reports on an empirical test of what the public deems acceptable in the context of FRT applications used in the public sector, focusing on citizens’ attitudes towards, preferences for, and concerns about public sector use of FRT. A survey of 266 residents of two comparative provincial jurisdictions in Canada — Saskatchewan and Ontario — gathered information on attitudes towards FRT used in a variety of settings using nine hypothetical scenario vignettes of FRT use. Descriptive statistics, correlation statistics, and regression analysis is used to identify which socio-economic and demographic factors predict support for public sector use of FRT. The findings from this research have implications for the adoption of FRT in the public sector and for the development of legislation and regulation in response to its use. Results indicate safety and security as a public priority and a general overall support for FRT use by public sector agencies regardless of sociodemographic characteristics, with the least favourable use being general public surveillance. Citizen service uses (particularly airport security) yielded the highest levels of support. The use of FRT by public sector agencies will require overt p…
author: Sahlu, Kedist
language: en
date published: 2022-08
date modified: 2026-07-24T04:03:36Z
provider: University of Regina
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Faculty of Graduate Studies and Research, University of Regina

PropertyValue
property ID: Handle
value: https://hdl.handle.net/10294/15552

PropertyValue
property ID: DOI
value: 10.82465/4304