The Growing Concerns Surrounding the Expansion of Live Facial Recognition Technology: The Need for Proper Scrutiny and Accountability

Live Facial Recognition (LFR) technology has become an increasingly valuable tool for police forces in their efforts to combat crime. However, there is a pressing concern that its use is being expanded without sufficient scrutiny and accountability. In order to address these concerns, it is crucial to examine the potential risks and limitations associated with LFR deployment, as well as the need for a clear legal framework and comprehensive regulation.

Acknowledgement of Updated Policies and Procedures

While it is commendable that police forces have taken steps to update their policies and procedures following the Court of Appeal judgment in Bridges in 2020, it is essential to highlight that this judgment was based on a narrow point on equality. We cannot simply wait for the legality of LFR deployment to be tested again in the courts; the government must take proactive measures to ensure that proper regulation is in place.

Legislative framework for LFR deployment

In order to ensure accountability and clarity, there must be a legislative framework authorized by Parliament to regulate the deployment of LFR technology. This framework would provide a solid legal foundation and help address the concerns raised about the discretionary powers currently vested in individual police officers. It is necessary to establish clear criteria for determining where and when LFR can be deployed.

Concerns raised by the Court of Appeal

In contrast to the evidence received from the police, the Court of Appeal in the Bridges judgment expressed serious concerns about the existing legal framework governing LFR. It highlighted “fundamental deficiencies” and questioned the lack of criteria for determining the deployment of LFR. These concerns emphasize the need for comprehensive regulation and scrutiny to safeguard against potential misuse of the technology.

Limitations of the Bridges case

It is important to note that the findings of the Bridges case were specific to that particular scenario and cannot be solely relied upon as a clear basis for the use of LFR. Regardless of the outcome in Bridges, the implementation of LFR should be firmly grounded in primary legislation. Relying solely on case law may leave room for ambiguity and inconsistency in its application.

Concerns about LFR implementation

One of the primary concerns surrounding the expansion of LFR is the discretion given to local officers in its implementation. This differs from one police force to another, which can lead to inconsistencies and potential misuse. To address this issue, a national compulsory LFR training program and standardized standards for England and Wales police forces should be adopted. This would ensure that officers are adequately trained and that the implementation of LFR aligns with uniform standards and principles.

Approval and Criteria for LFR “Watchlists”

Another area of concern is the creation of LFR “watchlists” containing suspected criminals. The process of approving individuals for inclusion on these watchlists should require proper scrutiny and accountability. There should be compulsory statutory criteria and standardized training for determining who is included in these lists, to ensure that they are not based on arbitrary or biased factors.

Regulation of Crowd-Sourcing Activity

Given the potential for extensive crowd screening using LFR technology, it is crucial to establish national regulations or guidelines governing its assessment. This would prevent the technology from being used disproportionately or indiscriminately. Such regulations should ensure that the use of LFR in crowd screening is proportionate, justified, and subject to appropriate oversight.

Embedding explainability in the LFR system

A crucial aspect of LFR technology is the need for explainability. It is imperative to consider how transparency and accountability can be embedded in the system. This would allow individuals subject to LFR to understand why they were flagged and ensure that decisions made by the technology are justifiable and non-discriminatory.

The expansion of Live Facial Recognition (LFR) technology has raised valid concerns regarding accountability, discretion, and potential misuse. It is essential to address these concerns through proper legislative measures. A clear and understood legal foundation, supported by a legislative framework authorized by parliament, is necessary to regulate the deployment of LFR technology. Alongside this, the adoption of national compulsory LFR training programs, standardized criteria for watchlists, national guidelines for crowd-screening activity, and the incorporation of explainability within the LFR system are essential steps towards ensuring that LFR technology is used responsibly, transparently, and only when necessary. By implementing these measures, we can strike a balance between effective law enforcement and safeguarding individual rights and privacy.

Explore more

Trend Analysis: Agentic AI in Data Engineering

The modern enterprise is drowning in a deluge of data yet simultaneously thirsting for actionable insights, a paradox born from the persistent bottleneck of manual and time-consuming data preparation. As organizations accumulate vast digital reserves, the human-led processes required to clean, structure, and ready this data for analysis have become a significant drag on innovation. Into this challenging landscape emerges

Why Does AI Unite Marketing and Data Engineering?

The organizational chart of a modern company often tells a story of separation, with clear lines dividing functions and responsibilities, but the customer’s journey tells a story of seamless unity, demanding a single, coherent conversation with the brand. For years, the gap between the teams that manage customer data and the teams that manage customer engagement has widened, creating friction

Trend Analysis: Intelligent Data Architecture

The paradox at the heart of modern healthcare is that while artificial intelligence can predict patient mortality with stunning accuracy, its life-saving potential is often neutralized by the very systems designed to manage patient data. While AI has already proven its ability to save lives and streamline clinical workflows, its progress is critically stalled. The true revolution in healthcare is

Can AI Fix a Broken Customer Experience by 2026?

The promise of an AI-driven revolution in customer service has echoed through boardrooms for years, yet the average consumer’s experience often remains a frustrating maze of automated dead ends and unresolved issues. We find ourselves in 2026 at a critical inflection point, where the immense hype surrounding artificial intelligence collides with the stubborn realities of tight budgets, deep-seated operational flaws,

Trend Analysis: AI-Driven Customer Experience

The once-distant promise of artificial intelligence creating truly seamless and intuitive customer interactions has now become the established benchmark for business success. From an experimental technology to a strategic imperative, Artificial Intelligence is fundamentally reshaping the customer experience (CX) landscape. As businesses move beyond the initial phase of basic automation, the focus is shifting decisively toward leveraging AI to build