Policing Facial Recognition — Between Risks, Misconceptions, and the Need for a More Honest Debate
Asress Adimi Gikay (PhD) Senior Lecturer in AI, Disruptive Innovation and Law, Brunel University of London
Photo credit: Abyssus, via Wikimedia commons
Live facial recognition on the rise
Live facial recognition (LFR), is quickly gaining ground across Europe, with countries like Germany having used it to target serious criminal offences. The technology scans people’s faces in real time and matches them against police watchlists (e.g., people suspected of committing serious crimes). The EU’s Artificial Intelligence(AI) Act, allows police in member states to use LFR for serious crimes such as terrorism. However, the implementation of the EU AI Act in member states will likely face challenges as technical issues such as accuracy and legal boundaries are yet to be adequately tested.
Meanwhile, the UK Metropolitan Police have gained an extensive experience in managing the risk posed by the technology, arresting more than 1,000 people between January 2024 and August 2025. In August 2025, despite opposition from 11 civil liberty groups, the Metropolitan deployed LFR at the Europe’s largest street festival celebrating African-Caribbean culture, Notting Hill Carnival, making 61 arrests.
The Metropolitan Police have taken the most step to address one of the biggest challenges in the use of the technology, i.e., ethnic bias. However, a controversy remains as to whether ethnic bias has been adequately tackled with data being interpreted differently to support the specific narrative being advanced. Misconception or misframing of critical notions in the field surveillance also shape public perception and could potentially inform policy and regulatory choices that are not necessarily evidence based. I believe the prevailing positions adopted by academics and civil society groups also partly reflect such a state of affairs— selective use of data, unwarranted anxiety about surveillance and misconceptions around core legal concepts.
The view predominantly advanced today by academics and civil liberty groups is a proposal for banning or imposing moratorium on the use of LFR on the ground that it is inaccurate, ethnically biased, susceptible to racially discriminatory use and enables mass surveillance. Whilst these are valid concerns, the Metropolitan Police’s experience over the past decade and the debate it sparked illustrates that the debate over governing the technology often doesn’t fairly weigh human rights and public safety concerns. Based on the experiences from the use of LFR technology in UK policing, in this post, I cover issues that often don’t surface wider-public discourse, some of these issues being crucial in providing insights into how LFR technology can deployed in the EU under the AI Act as well as other jurisdictions.
From backlash to acceptance
Critics often describe policing facial recognition as Orwellian surveillance tool. Yet history shows facial recognition is not the first or only technology to raise such a fear.
When Transport for London released a poster in 2002 announcing CCTV on buses, the design featured a double-decker bus gliding under a sky, with floating eyes. Its slogan read— “Secure Beneath the Watchful Eyes.” Simon Davies, the then head of Privacy International described it as “acutely disturbing.” Two decades later, CCTV is widely accepted as an essential tool for solving crimes.
Big Brother Watch, initially opposed airport facial recognition e-gates, warning that the system creates privacy intrusive massive database of personal information and is prone to risk of error. Today, automated border control in Europe is considered a privilege, allowing faster passport control, available primarily to European passport holders. ‘Other travellers’ undergo more intrusive security control, including through fingerprints.
New technologies usually caused alarm, until their public benefits become clearer and they gain legitimacy. I don’t believe policing facial recognition is any different.
Measuring the impact of ethnic bias is tricky
Concerns about bias in facial recognition stem from early studies of commercial gender-classification algorithms and Metropolitan Police’s initial deployments that showed poorer accuracy especially for black women.
However, a 2023 audit by the National Physical Laboratory (NPL), commissioned by the Metropolitan police found that when the system is optimally set, it works without significant ethnic disparities.
A crucial factor is the ‘recognition confidence threshold,’ or ‘face match threshold’ which determines how accurately the software matches faces. It ranges between 0-1. Higher settings reduce errors but yield fewer face matches while lower settings give more matches with less accuracy. The Metropolitan Police currently uses 0.64, a level recommended by the NPL to reduce ethnic bias significant enough to treat is as not concerning(statistically insignificant).
The NPL’s test involved 400 volunteers embedded in an estimated crowd of 130,000. The test showed that at a 0.64 setting or higher, there was no ethnic disparity in accuracy. At thresholds of 0.62 and 0.60, ethnic bias was statistically insignificant, while at 0.58 and 0.56, the system struggled to identify black faces.
Pete Fussey, a recognised expert in this field, contends the sample was too small to support such a conclusion and notes that “false matches were not actually assessed at the settings where ethnic bias was non-existent”. This essentially rests on the fact that for a technology that scans millions of faces, testing it on faces of 400 volunteers is less likely to generate a sufficient evidence base. In their book, Facial Recognition Surveillance: Policing in the Age of Artificial Intelligence (p, 58), Pete Fussey and Daragh Murray argue:
“Also of note are claims that no demographic bias is discernible above the 0.64 threshold. This is because no false positives occurred at this level. Put another way, no bias was observed because the system was not adequately tested in this range. Notable here is that such arguments rest less on how FRT operates and more on how statistics work A suitable analogy would be the claim that 90 per cent of car accidents occur within a quarter-mile of home. This is less because such locales are inherently hazardous and more because almost all car journeys happen within a quarter-mile of home. Fewer journeys occur 600 miles away so accidents in that category are rarer. ”
However, a counter-argument to above is that the test in question did show steady decline in ethnic disparities with higher face match thresholds— at 0.56, 22 vs. 3 (Black vs. White); at 0.58, 11 vs. 0; at 0.60, 4 vs. 0; at 0.64, 0 vs. 0. Despite the sample being smaller, the consistent decline implies that face match threshold clearly determines accuracy. The insistence on testing the technology until bias is completely removed is also unrealistic. So, if no inaccuracy was recorded at 0.64 and ethnic bias declined gradually up to that point, it would not be unreasonable to conclude that the technology works optimally at the given setting.
The NPL’s test is consistent with the risk management system in the EU AI Act, which sets strict standards for high-risk AI systems. In its provisions requiring risk management for high-risk AI systems, in particular article 9(3), the AI Act requires that
“The risk management measures referred to in paragraph 2, point (d), shall be such that the relevant residual risk associated with each hazard, as well as the overall residual risk of the high-risk AI systems is judged to be acceptable.”
It means that the expectation in terms of risks including risk of ethic bias is not a complete elimination rather it is mitigation to the extent that some acceptable(tolerable) level of risks could still exist. By these standard, NPL’s testing is likely considered robust, since at 0.64 ethnic bias would reasonably be seen as low enough to be acceptable in view of the technology’s benefits
Subsequent Metropolitan Police’s deployment data is also indicative of this. Between January and August 2025, the Metropolitan Police have misidentified only eight people using LFR, leading to no arrests. While ethnic breakdown for these false matches is not studied, the small number makes any ethnic disparity likely negligible.
Currently, there is one pending legal action brought against the Metropolitan Police by Big Brother Watch concerning prolonged police engagement with a mistakenly identified individual. This was not officially documented as false arrest, and therefore the official record in the UK is that there has not been a single false arrest following misidentification by LFR in the UK.
The above highlights that statistics alone doesn’t capture the complex ways LFR really affect people. Human oversight, responsible police judgment, and procedural safeguards play a crucial role; and the current debate discounts these components.
Policing by consent isn’t policing by of everyone’s consent
A common misconception is that overt(transparent) LFR surveillance undermines policing by consent, as people don’t meaningfully consent to being surveilled.
Peter Fussey and Daragh Murray argue that, for instances, signages placed by the Metropolitan Police at deployment spots to inform the public of LFR operations were insufficient to obtain informed consent, as they contained inadequate information, lacked visibility and offered no opportunity for refusing consent.
Echoing this, former director of Big Brother Watch, Silkie Carlo stated in an interview, “there’s no meaningful consent process whatsoever. You certainly can’t withdraw consent.”
I think this view misrepresents both the law and the idea of policing by consent. The relevant UK Surveillance Camera Code of practice requires overt surveillance to be based on consent, specifically clarifying that consent in this context should be regarded as “analogous to policing by consent”.
Policing by consent is traced to the 9 point principles of Robert Peel, UK’s Home Secretary set out in the general instructions issued to new officers in 1829. Essentially, it requires public consent for police to serve the community where the legitimacy of policing power drives from public support. It does not require individual member of the public to consent to specific policing operations.
Similarly, surveillance by consent requires the community broadly to agree to visible camera systems as a legitimate tool for public safety, not whether everyone agrees to the surveillance. Besides facilitating legitimacy, transparent police surveillance ensures that those aggrieved by potentially unlawful surveillance can take legal actions. The Surveillance Camera Code of Practice itself which is the basis for transparency in overt surveillance confirms this point by not only specifying that consent in this context is equivalent to policing by consent but also indicating the reason why consent is required. Section 3.3.2. states that “Surveillance by consent is dependent upon transparency and accountability on the part of a system operator. The provision of information is the first step in transparency and is also a key mechanism of accountability.” Nowhere in the code or any other legislation is it stated that surveillance by consent entitles individuals to consent to or withdraw consent to specific operations on individual level. Despite quoting the SCC including the relevant reference to policing by consent in their recent book, Peter Fussey and Daragh Murry don’t engage with the notion of policing by consent when they discuss consent in the context of overt surveillance, instead engaging with data protection law notion of consent. If consent of everyone who could be captured by LFR camera or even a normal CCTV came is to be secured, most public facing CCTV cameras would have to be removed.
It is therefore legally and conceptually unfounded to claim that overt LFR surveillance requires the consent of everyone who walks by the LFR camera. Neither can this be realistically achieved in practice.
Surveillance harms, but context matters
Opponents often alert that surveillance in public space, can deter people from speaking freely, attending protests, or joining public events, a phenomenon called the ‘chilling effect.’
In the context of LFR, Daragh Murray asserted that it might discourage attendance at the 2025 Notting Hill Carnival, citing uncertainty about how the technology is used and historical allegations of institutional racism against the Metropolitan Police.
The 2024 Carnival experienced two murders, multiple assaults, and stabbings, and yet an estimated two million people attended the Carnival this year, undeterred by the potential violence. Suggesting that surveillance would deter participation in such a cultural event is clearly implausible. At the very least, there is no evidence to back this claim.
The chilling effect of surveillance is a concern in the context of political protests, where authorities may target opposition groups and threaten civil liberties. It can also be argued that excessive policing of minority communities may create a chilling effect to some extent, though this is highly context dependent. For example, the 2025 Carnival had 7,000 police officers with supporting technologies, and their presence was requested by the organisers and generally welcomed by the public. To suggest that adding LFR to this setting would have altered the behaviour of potential attendees is hardly credible. The blanket claim that surveillance suppresses civil rights and alters behaviours in all contexts is not supported by evidence.
The bottom-line
Facial recognition will inevitably become routine policing tool. Rather than pushing unrealistic proposals of bans or moratoriums, regulatory debate should properly weigh the trade-offs between human rights and public safety in ensuring the proportionate use of the technology. Questions about when LFR should be used and considered proportionate and other issues such as oversight should be debated carefully. However, the UK police’s use LFR, and the ongoing debate highlights that policy and regulatory proposals could be based on shaky interpretation of data and understanding of essential legal concepts.
This piece was first published in EU Law Analysis: EU Law Analysis: Policing Facial Recognition — Between Risks, Misconceptions, and the Need for a More Honest Debate
