Concern 399 · Working draft · editorial review in progress
Live facial recognition in public spaces
Live facial recognition compares faces seen by cameras with stored identities while people are moving through a place.
Understanding the concern
Unlike reviewing a recording later, live recognition can prompt action while someone is still present. People may be scanned without actively presenting themselves for an identity check. That creates questions about whose faces are searched, which watchlist is used and how a possible match affects someone in public.
It is like checking everyone passing through a square against a list, rather than checking a ticket at a clearly marked entrance. Accuracy is only one issue; the reason and scope of surveillance matter too. The EU AI Act restricts particular real-time law-enforcement uses in public spaces, with limited exceptions. That is not a worldwide ban on every facial-recognition use.
An example
A person passing a camera is compared with a watchlist despite having no connection to an investigation.
Illustrative unless identified as a documented case.
Words in plain English
- AI
- Software that learns patterns from examples or uses rules to perform tasks such as recognizing images or generating text. It can make mistakes and does not understand every situation as a person would.
- Prompt
- The instructions or information given to an AI to guide its response.
- EU
- The European Union, a group of European countries with shared institutions and some shared laws.
Evidence and its limits
The EU rule is scoped by use and exceptions. Local law, authorisation and the actual system must be checked for a specific deployment.
European Commission AI Act Service Desk · Regulatory explainer
What Systems Are Prohibited under Article 5 of the AI Act? ↗ (opens in a new tab)Scoped prohibitions and exceptions, not a blanket worldwide ban on all recognition, scoring or monitoring.
National Institute of Standards and Technology · Research explainer
NIST Study Evaluates Effects of Race, Age, Sex on Face Recognition Software ↗ (opens in a new tab)2019 testing found different error patterns by algorithm, application, image quality and demographic group.
Broader frameworks and background
These resources provide context. They do not establish every claim in this concern.
- OECD AI Principles (opens in a new tab)
framework
- NIST AI RMF (opens in a new tab)
framework
A question to discuss
What specific purpose justifies scanning everyone here, and what prevents an uncertain match from causing harm?
Keep track of different views, what needs checking, and any next step in your own discussion notes.
Open discussion notes →Principles for the conversation
Before you finish · reflect together
Virtues and principles to consider
Virtues are qualities we practice in how we treat people and make choices. They are not scores for judging someone. Use these ideas to explore what a thoughtful response could look like.
Dignity
Dignity means recognizing each person's worth regardless of usefulness, status, or a system's score. People deserve respect and a voice in matters that affect them.
Explain important decisions, avoid humiliating treatment, and keep room for a person's story. Do not treat a prediction as a complete account of who someone is.
Ask together: Does this use treat each person as someone whose experience matters?
Trustworthiness
Trustworthiness means giving people good reasons to rely on you. It grows when promises, actions, and evidence line up. With AI, a friendly voice or a famous brand is not enough: people need to know what was checked and what could still go wrong.
In practice, keep promises small enough to test, record mistakes, and tell people when the tool's limits affect them. If a service promises human review, make sure a person really has time and authority to review it.
Ask together: What would give the people affected a sound reason to trust this use?
Stewardship
Stewardship means caring for resources and systems that others depend on, including people who will use them later. It looks beyond the benefit of a quick launch.
Plan for maintenance, energy use, data protection, and eventual shutdown. Ask who will look after the tool when the original team moves on and how future users can avoid inheriting hidden costs.
Ask together: What are we responsible for caring for over time?
Editorial ratings
These are editorial judgments from the register, not measured probabilities or predictions about your situation.
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