The ethics of facial recognition technologies, surveillance, and accountability in an age of artificial intelligence: a comparative analysis of US, EU, and UK regulatory frameworks
Open accessAbstract
The rapid development of facial recognition technologies (FRT) has led to complex ethical choices in terms of balancing individual privacy rights versus delivering societal safety. Within this space, increasingly commonplace use of these technologies by law enforcement agencies has presented a particular lens for probing this complex landscape, its application, and the acceptable extent of citizen surveillance. This analysis focuses on the regulatory contexts and recent case law in the United States (USA), United Kingdom (UK), and European Union (EU) in terms of the use and misuse of FRT by law enforcement agencies. In the case of the USA, it is one of the main global regions in which the technology is being rapidly evolved, and yet, it has a patchwork of legislation with less emphasis on data protection and privacy. Within the context of the EU and the UK, there has been a critical focus on the development of accountability requirements particularly when considered in the context of the EU’s General Data Protection Regulation (GDPR) and the legal focus on Privacy by Design (PbD). However, globally, there is no standardised human rights framework and regulatory requirements that can be easily applied to FRT rollout. This article contains a discursive discussion considering the complexity of the ethical and regulatory dimensions at play in these spaces including considering data protection and human rights frameworks. It concludes that data protection impact assessments (DPIA) and human rights impact assessments together with greater transparency, regulation, audit and explanation of FRT use, and application in individual contexts would improve FRT deployments. In addition, it sets out ten critical questions which it suggests need to be answered for the successful development and deployment of FRT and AI more broadly. It is suggested that these should be answered by lawmakers, policy makers, AI developers, and adopters.
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- Authors
- Almeida, D., Shmarko, K., & Lomas, E.
- Affiliations
- University College London (GB)
- Corresponding author
- Almeida, Denise
- Venue
- AI and Ethics
- Published
- 2021-07-29
- Volume
- 2
- Issue
- 3
- Pages
- 377-387
- ISSN
- 2730-5953 (ISSN-L)
- 2730-5961
- PMC
- PMC8320316
- OpenAlex
- W3186337419
- Cited by
- 203
- References
- 13
- Citations by year
- 2026: 41 · 2025: 53 · 2024: 56 · 2023: 34 · 2022: 16 · 2021: 3
- FWCI
- 177.67
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- AI and Ethics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- University College London
- Topics
- Social Sciences › Law › Digitalization, Law, and Regulation
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Law, AI, and Intellectual Property
- Keywords
- facial recognition technology, surveillance, law enforcement, right to privacy, data protection, General Data Protection Regulation, privacy by design, accountability, AI regulation, data protection impact assessment, human rights impact assessment, comparative regulation, United States, European Union, United Kingdom, transparency, algorithmic auditing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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What counts as a claim ›How we measure confidence ›How we check this ›
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