From ‘wild west’ to ‘responsible’ AI testing ‘in-the-wild’: lessons from live facial recognition testing by law enforcement authorities in Europe

Abstract

Although ‘in-the-wild’ technology testing provides an important opportunity to collect evidence about the performance of new technologies in real world deployment environments, such tests may themselves cause harm and wrongfully interfere with the rights of others. This paper critically examines real-world AI testing, focusing on live facial recognition technology (FRT) trials by European law enforcement agencies (in London, Wales, Berlin, and Nice) undertaken between 2016 and 2020, which serve as a set of comparative case studies. We argue that there is an urgent need for a clear framework of principles to govern real-world AI testing, which is currently a largely ungoverned ‘wild west’ without adequate safeguards or oversight. We propose a principled framework to ensure that these tests are undertaken in an epistemically, ethically, and legally responsible manner, thereby helping to ensure that such tests generate sound, reliable evidence while safeguarding the human rights and other vital interests of others. Although the case studies of FRT testing were undertaken prior to the passage of the EU’s AI Act, we suggest that these three kinds of responsibility should provide the foundational anchor points to inform the design and conduct of real-world testing of high-risk AI systems pursuant to Article 60 of the AI Act.

Publication DOI: https://doi.org/10.1017/dap.2025.10019
Divisions: College of Business and Social Sciences
College of Business and Social Sciences > Aston Law School
Additional Information: Copyright ©The Author(s), 2025. Published by Cambridge University Press. This is an OpenAccess article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0), which permits non-commercial re-use, distribution, and reproduction in any medium, provided that no alterations are made and the original article is properly cited. The written permission of Cambridge University Press must be obtained prior to any commercial use and/or adaptation of the article.
Last Modified: 29 Sep 2025 07:42
Date Deposited: 24 Sep 2025 11:05
Full Text Link:
Related URLs: https://www.cam ... C195CE403AE3BDF (Publisher URL)
PURE Output Type: Article
Published Date: 2025-09-19
Accepted Date: 2025-06-26
Authors: Yeung, Karen
Li, Wenlong (ORCID Profile 0000-0002-2574-1847)

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