Concern 402 · Working draft · editorial review in progress
Predictive-policing hotspots
Predictive-policing hotspots are places a model marks as more likely to experience certain recorded incidents.
Understanding the concern
A hotspot map may guide patrols or resource allocation. Its predictions depend on what incidents were recorded, how areas are divided and what period is analysed. A coloured patch on a map can look like an objective fact even when it reflects missing reports, past enforcement or uncertain estimates.
This differs from scoring an individual person. It still affects people living or working in the area, because a place-based label can change police presence and scrutiny. Research on feedback shows how new observations from patrols can reinforce earlier patterns. Useful evaluation asks whether the map reduces harm, not just whether it predicts recorded activity.
An example
A neighbourhood stays highlighted because increased patrols continue producing more records there.
Illustrative unless identified as a documented case.
Words in plain English
- Model
- The learned part of an AI system. It uses patterns found during training to produce an answer or prediction.
Evidence and its limits
The paper analyses feedback mechanisms. It does not establish the quality or legality of every place-based policing model.
Ensign, Friedler, Neville, Scheidegger and Venkatasubramanian · Research paper
Runaway Feedback Loops in Predictive Policing ↗ (opens in a new tab)Mathematical and empirical analysis of feedback from discovered crime data.
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 does the hotspot measure, and how do residents experience the response it triggers?
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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