Concern 351 · Working draft · editorial review in progress
Predictive policing
Predictive-policing feedback is a cycle in which past police activity helps direct future patrols, which then generate more records from the same places.
Understanding the concern
A model may use recorded incidents to suggest where officers should go. But some incidents are recorded because officers are already present. Sending more officers to an area can produce more observations there, feeding the next prediction. The data may partly describe where police looked rather than only where harm occurred.
Think of checking one cupboard every day and another once a month, then concluding that nearly all problems happen in the first. Research has modelled how this kind of feedback can distort policing systems. Reported crimes and independently measured conditions can provide different information, but no single adjustment removes every social concern.
An example
An area receives more patrols because it has more recorded incidents, then records still more incidents because patrols increase.
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 demonstrates a feedback mechanism under stated modelling assumptions, not a claim about every police department.
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.
A question to discuss
Does the model distinguish underlying harm from the effects of where officers have already been sent?
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.
Justice
Justice means fair treatment, fair procedures, and attention to who receives benefits and burdens. It asks whether people with less power can be heard and seek repair.
Look at outcomes across different groups and individual cases. Provide a usable way to challenge decisions and correct harm. Equal wording in a policy does not guarantee equal treatment in practice.
Ask together: Who benefits, who bears the cost, and who can challenge the decision?
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?
Listening
Listening means trying to understand someone's experience before preparing an answer. People who use a service, work with it, or are judged by it may notice problems its designers miss.
Invite people to describe what happened in their own words. Repeat back what you understood and let them correct it. A quiet person or a complaint that is hard to measure may still reveal something important.
Ask together: Whose experience have we not heard yet?
Editorial ratings
These are editorial judgments from the register, not measured probabilities or predictions about your situation.
About the register and scoring →More ways to explore
Search for more writing
For more specific or recent material, open a search below or ask for a web summary. AI-generated summaries can contain mistakes; open the original articles to check them.