Talking about facial recognition technology: How framing and context influence privacy concerns and support for prohibitive policy

Shore, Alexis

doi:10.1016/j.tele.2022.101815

Cited by 15 publications

(6 citation statements)

References 64 publications

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“…7 Additionally, empirical research has shown that FRT is bias against minority population, as it is less accurate for the faces of people between the ages of 18–30, particularly women and people of color (Klare et al, 2012 ). These findings are consistent with studies underpinning the argument that FRT disproportionately impacts minority groups and greatly impedes privacy (Buolamwini and Gebru, 2018 ; Grother et al, 2019 ; Shore, 2022 ). Within these concerns, empirical survey-based research has shown that individuals' acceptance to facial recognition varies given different political contexts and socio-demographic factors.…”

Section: Introductionsupporting

confidence: 86%

Facial recognition technology: regulations, rights and the rule of law

Qandeel

2024

Front. Big Data

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Despite their pronounced potential, unacceptable risk AI systems, such as facial recognition, have been used as tools for, inter alia, digital surveillance, and policing. This usage raises concerns in relation to the protection of basic freedoms and liberties and upholding the rule of law. This article contributes to the legal discussion by investigating how the law must intervene, control, and regulate the use of unacceptable risk AI systems that concern biometric data from a human-rights and rule of law perspective. In doing so, the article first examines the collection of biometric data and the use of facial recognition technology. Second, it describes the nature of the obligation or duty of states to regulate in relation to new technologies. The article, lastly, assesses the legal implications resulting from the failure of states to regulate new technologies and investigates possible legal remedies. The article uses some relevant EU regulations as an illustrative example.

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Section: Introductionsupporting

confidence: 86%

Facial recognition technology: regulations, rights and the rule of law

Qandeel

2024

Front. Big Data

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“…AIS operate within uncertain and unpredictable environments. Environmental dimensions include changing ethical expectations, like emerging public pressures relating to facial recognition [62,63], a developing regulatory environment exemplified by the recent release of the European Union AI Act [12], and an evolving cultural environment as the system is used in new countries around the world [64]. As a result, standard risk management approaches fall short.…”

Section: Extending Erm For Ai Ethical Risksmentioning

confidence: 99%

Managing Ethical Risks of Artificial Intelligence in Business Applications

McGrath,

Hevner,

Vreede

2024

Preprint

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The introduction of artificial intelligence (AI) capabilities in business applications provides significant benefits but requires organizations to manage critical risks of AI ethical consequences. We survey a range of large organizations on their use of enterprise risk management (ERM) processes and toolsets to predict and control the ethical risks of AI. Four serious gaps in current ERM systems are identified from analyses of the survey results: (1) AI ethical principles do not translate effectively to ethical practices; (2) Real-time monitoring of AI ethical risks is needed; (3) ERM systems emphasize economic not ethical risks; and (4) When ethical risks are identified, no solutions are readily at hand. To address these gaps, we propose a proactive approach to manage ethical risks by extending current ERM frameworks. An enhanced ERM (e-ERM) framework is designed and evaluated by subject matter expert focus groups. We conclude with observations and future research directions on the need for more aggressive pro-ethical management oversight as organizations move to ubiquitous use of AI-driven business applications.

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“…With respect to surveillance and privacy as key internet policy issues, the DPL framework suggests that commercial surveillance predicated on platform infrastructure requires regulatory frameworks informed by broader rights-based approaches to privacy (Smith, Shade, andShepherd 2017: 2795). These imperatives become particularly important in the context of facial recognition technologies that contribute to networked surveillance infrastructure that includes social media platforms, algorithmic sorting, and predictive policing (e.g., Kosta 2022;Shore 2022). Moreover, when dealing with the Canadian context as I do here, digital policy issues become further mired in the geopolitical dynamics of how large US tech firms come up against Canadian regulatory frameworks.…”

Section: Introductionmentioning

confidence: 97%

The Canadian Clearview AI Investigation as a Call for Digital Policy Literacy

Shepherd

2024

S&S

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In 2020, the Office of the Privacy Commissioner of Canada (OPCC) led a joint federal-provincial investigation into privacy violations stemming from the use of facial recognition technologies. The investigation was prompted specifically by the mobilization of Clearview AI’s facial recognition software in law enforcement, including by regional police services as well as the Royal Canadian Mounted Police. Clearview AI’s technology is based on scraping social media images, which, as the investigation found, constitutes a privacy law violation according to provincial and federal private sector legislation. In response to the investigation, Clearview AI claimed that consent for scraping social media images was not required from users because the information is already public. This common fallacy of social media privacy serves as a pivot point for the integration of digital policy literacy into the OPCC’s digital literacy materials in order to consider the regulatory environment around digital media, alongside their political-economic and infrastructural components. Digital policy literacy is a model that expands what is typically an individual- or organization-level responsibility for privacy protection by considering the wider socio-technical context in which a company like Clearview can emerge.

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Talking about facial recognition technology: How framing and context influence privacy concerns and support for prohibitive policy

Cited by 15 publications

References 64 publications

Facial recognition technology: regulations, rights and the rule of law

Facial recognition technology: regulations, rights and the rule of law

Managing Ethical Risks of Artificial Intelligence in Business Applications

The Canadian Clearview AI Investigation as a Call for Digital Policy Literacy

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