A Framework for Assessing Proportionate Intervention with Face Recognition Systems in Real-Life Scenarios
CoRR(2024)
摘要
Face recognition (FR) has reached a high technical maturity. However, its use
needs to be carefully assessed from an ethical perspective, especially in
sensitive scenarios. This is precisely the focus of this paper: the use of FR
for the identification of specific subjects in moderately to densely crowded
spaces (e.g. public spaces, sports stadiums, train stations) and law
enforcement scenarios. In particular, there is a need to consider the trade-off
between the need to protect privacy and fundamental rights of citizens as well
as their safety. Recent Artificial Intelligence (AI) policies, notably the
European AI Act, propose that such FR interventions should be proportionate and
deployed only when strictly necessary. Nevertheless, concrete guidelines on how
to address the concept of proportional FR intervention are lacking to date.
This paper proposes a framework to contribute to assessing whether an FR
intervention is proportionate or not for a given context of use in the above
mentioned scenarios. It also identifies the main quantitative and qualitative
variables relevant to the FR intervention decision (e.g. number of people in
the scene, level of harm that the person(s) in search could perpetrate,
consequences to individual rights and freedoms) and propose a 2D graphical
model making it possible to balance these variables in terms of ethical cost vs
security gain. Finally, different FR scenarios inspired by real-world
deployments validate the proposed model. The framework is conceived as a simple
support tool for decision makers when confronted with the deployment of an FR
system.
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