The Growing Concerns Surrounding Metropolitan Police‘s Live Facial Recognition Deployments
The Metropolitan Police’s increasing reliance on Live Facial Recognition (LFR) technology is sparking significant debate and raising critical questions about its effectiveness, fairness, and impact on civil liberties. While the Met champions LFR as a vital tool for crime prevention, mounting evidence suggests a system prone to overreach, disproportionate impact, and questionable public support. This article delves into the specifics of these deployments, examining the data and concerns voiced by civil liberties groups, local councils, and residents.
A Surge in Scans, A Low Arrest Rate
The scale of LFR deployment in London is rapidly expanding. Recent data, including records from the Details Commissioner’s Office (LFR Deployment Grid), reveals a dramatic increase in facial scans – now routinely exceeding 15,000 faces per operation. However, this intensive scanning yields a remarkably low arrest rate.
Consider these key statistics:
Over two million people were scanned during the Met’s 2024 deployments.
Only 804 arrests were made, representing a mere 0.04% success rate. As of July 2024, over 1,000 arrests have been made since the start of the year, with 773 resulting in charges or cautions.
This disparity fuels concerns that LFR is functioning more as a “catch net” than a targeted policing strategy, as described by Silkie Carlo, director of Big Brother Watch, following a February 2022 deployment in Westminster. The sheer volume of scans, particularly with such a low yield, suggests a broad, untargeted approach.
Disproportionate Impact on Black Communities
Perhaps the most troubling aspect of these deployments is the evidence of disproportionate impact on Black communities. Analysis by Green Party London Assembly member Zoë garbett reveals a pattern:
Over half of the Met’s 180 LFR deployments in 2024 occured in areas with a higher-than-average proportion of Black residents.
Specifically,deployments were concentrated in Lewisham (34% Black population),Haringey (36% Black population),and Croydon (40.1% Black population).
This contrasts with London’s overall Black population of 13.5%.
This concentration raises serious questions about potential bias and the exacerbation of existing racial disparities in policing. The potential for LFR to “exacerbate racist outcomes in policing” was specifically highlighted by Newham Council when it unanimously voted to suspend LFR use within its borough in January 2023.
Questionable Public Support and Lack of Openness
The Met Police maintains that its use of LFR enjoys public support. However, this claim is increasingly challenged by evidence of limited consultation and outright opposition.
Here’s what the data shows:
In Lewisham, Computer Weekly revealed minimal direct consultation with residents regarding LFR deployments, despite the force’s claims of widespread support. Local councillors continued to voice concerns.
Green Lewisham councillor Hau-Yu Tam emphasized that public support is for “safer streets and improved equity,” not necessarily for LFR itself, particularly when information provided is incomplete or misleading.
Newham Council’s unanimous vote to suspend LFR use demonstrates a clear rejection of the technology by local representatives.
Despite local opposition, both the Met and the Home Office have indicated thier intention to continue LFR deployments, raising concerns about a disregard for local democratic processes and community concerns. The planned permanent LFR camera deployment in Croydon further underscores this trend.
The path Forward: Safeguards and Accountability
The current trajectory of LFR deployment in London demands a critical reassessment. To ensure responsible and equitable use of this powerful technology, several steps are crucial:
Enhanced Transparency: The Met must provide detailed, publicly accessible data on LFR deployments, including watchlists, scan numbers, arrest rates, and demographic breakdowns.
Self-reliant Oversight: Establish an independant body to oversee LFR deployments, ensuring adherence to legal and ethical standards.
Robust Safeguards: Implement stringent biometric and anti-discrimination safeguards to mitigate the risk of bias and inaccurate matches.
*Meaningful