Web Desk
Once the stuff of sci-fi, the idea that AI could predict future crimes is now being taken seriously.
This isn’t about fortune-telling—it’s about probabilities, data, and complex algorithms. But just because something is possible doesn’t mean it’s practical or ethical.
The Human Need to Predict
Humans have always wanted to know the future. From crab-reading diviners in Cameroon to online fortune tellers in Bangkok, prediction is an old game.
Today, AI feeds this desire, offering powerful forecasting tools. In industries like energy, predictive AI is already helping allocate resources efficiently by analyzing historical data.
But forecasting a storm isn’t the same as predicting a crime. Crime is deeply personal and often depends on unpredictable variables—emotions, social context, mental state, environment.
Like sports, crime involves too many live factors for any machine to guarantee outcomes.
Is Past Behavior a Reliable Guide?
Predictive policing tools are often built on the assumption that past behavior forecasts future action. There’s some truth here—occupational psychologists use the same logic in hiring decisions.
But the law is a moving target. For instance, in 2017, the UK Supreme Court changed how dishonesty is judged.
A behavior that once was illegal could become legal—and vice versa.
So even if someone has a dishonest history, can AI really predict if they’ll break tomorrow’s laws, not just today’s? Plus, what about crimes that don’t even exist yet—those that will only become possible with new tech?
What Data Should Be Used?
AI needs data. But what kind? Prior arrests? Acquittals? Social circles? Facial features?
Predicting crime based on appearance echoes 19th-century pseudoscience and introduces serious risks of racial or social bias.
Take this example: A teenager kicks a ball, and it hits a car. If the car is damaged, it’s a crime. If not, no crime. Same action, different outcomes.
Later, the car owner performs a citizen’s arrest, but the case gets dropped. Suddenly, the teen wasn’t the offender—the car owner was. Could AI have foreseen all that?
The Risk of False Predictions
Let’s say AI does flag someone as likely to offend. Then what? Do we monitor them forever? Fine them before they act? What legal standard applies—“beyond reasonable doubt”? Policing cannot afford to become a game of Minority Report.
If someone does offend, the system might say, “See? We were right.” But if they don’t, was the AI wrong—or did early intervention work? It’s an impossible loop to untangle. And without clear legal guidelines, enforcement becomes a murky moral minefield.
Where AI Can Help Policing
Predictive AI does have solid uses in law enforcement. For example:
Identifying vulnerable individuals before harm happens.
Resource planning to protect critical infrastructure.
Traffic flow management and smarter shift scheduling.
Instead of predicting who will commit a crime, it’s more valuable to use AI to respond better to crime that has occurred—or to prevent it through community-level forecasting, not individual targeting.
Policing Needs Confidence, Not Prejudice
As Malcolm Gladwell put it, “A prediction in a field where no prediction is possible is just prejudice.” Even if AI adds numbers and graphs, it can still embed old biases in new code.
Public trust in policing will grow not through futuristic predictions, but through clear, just, and smart use of technology to solve real problems today.
