Colorado drivers have a problem: many are receiving tickets for cars they don’t even own. A local news report thinks they know why. Recently, a woman wrote in after she received a speeding ticket but noticed that the attached photo radar ticket showed a completely different vehicle.
The license plates were similar, but Linnebur’s license plate had a “D” in the same spot where the plate in the image had an “O.” This has apparently been an ongoing issue — 9NEWS recalled a man disputing a parking ticket after the “O” on his license plate was confused with a zero in 2025.
This is due to automatic license plate readers (ALPR) in Colorado confusing D’s, zeroes, and O’s. Drivers in Pennsylvania reported a similar problem in March 2026. A new plate design added a diagonal slash through the zero specifically to improve legibility, but this caused toll cameras to confuse zeroes with eights. Turns out a license plate update is what confused Colorado cameras as well.
What license plate change caused Colorado ALPRs to fail?
Traveller70/Shutterstock
Colorado has changed its license plate configurations throughout the years — and the most recent one tripped up ALPRs. In the 1980s and 1990s, Colorado plates used a letter-first configuration (three letters followed by three numbers). In the year 2000, it was flip-flopped so the three numbers were first. But by 2018, Colorado decided to stop separating numbers and letters entirely. That’s when the issues started happening.
From 2018 onward, Colorado license plates used a four-letter, two-number format that allowed O’s and zeroes to appear on the same side of the plate, allowing for more configurations. Despite this, some ALPRs had never been calibrated to reflect this change, leaving the technology confused. Meanwhile, Colorado lawmakers have been trying to limit access to data collected by these ALPRs, a move other states are also pursuing. In some instances, this has led to court cases.
The DMV has been trying to change the font to make the two symbols more distinguishable. When 9NEWS asked about adding a strikethrough on the zeroes, Vehicle Services Director Benjamin Whittier said: “We have looked at that. We don’t do that. There was a decision made many years ago not to go that route, and it has its own misidentification characteristics we’ve seen them come back as an eight or a ‘Q’ if they’re dirty or obstructed.” He instructed drivers who received incorrect tickets to go to the DMV to obtain a new license plate.
Another day, another politically motivated attack in the United States.
This morning’s shooting at a Dallas ICE detention facility – where a sniper killed two detainees and wounded another before taking his own life prompted me to revisit a question that’s been troubling me: Is political violence actually increasing in America, or does it just feel that way?
To explore this, I’ve conducted what I’ll call a methodological experiment.
Rather than relying on traditional datasets, I’ve used ChatGPT and Claude to construct a synthetic index of political violence in the US since 1945. Let me be absolutely clear: this isn’t conventional data. It’s data generated through language models, with all the limitations that implies.
The Methodology (and Its Limitations)
Here’s what I did: I asked both ChatGPT and Claude to generate lists of politically motivated violent incidents since 1945, then had them score each incident’s severity on a scale where 50 represents a “normal” level.
The models assessed both casualties and symbolic significance, and I used them to cross-check each other’s work. I then quality-checked the output myself and categorised perpetrators by political affiliation where this was clearly established.
This approach is, admittedly, unorthodox. Language models are trained on existing texts and may reflect biases in their training data. They might overweight highly publicised events or recent incidents that featured prominently in their training corpus.
The “data” we’re looking at is essentially a structured synthesis of what these models have absorbed about American political violence.
Yet there’s something intriguing here. These models have processed vast amounts of information about political violence – news reports, academic studies, government documents. Their output might capture patterns that traditional datasets miss, though it might also amplify certain narratives or blind spots.
What the Synthetic Data Reveal
With those caveats firmly in mind, the patterns that emerge from this exercise are concerning. The model-generated index shows a clear upward trend in political violence over the past decade.
Looking at the breakdown by perpetrator ideology (where clearly established), the data suggest that right-wing extremist groups have been responsible for the majority of incidents in recent years, though we cannot draw conclusions about today’s attack whilst investigations are ongoing.
The synthetic data align with some empirical observations. Princeton’s Bridging Divides Initiative recorded over 600 incidents of threats and harassment against local officials in 2024 – a 74% increase from 2022. The University of Maryland found that in the first half of 2025, 35% of violent events targeted U.S. government personnel or facilities – more than twice the rate in 2024.
The Charlie Kirk Assassination and Recent Patterns
The September assassination of conservative activist Charlie Kirk marked a particularly dark moment.
The incident followed numerous recent acts of political violence, including the murder of Minnesota Democratic state Rep. Melissa Hortman and her husband, and two assassination attempts on President Trump in 2024.
What the synthetic data reveal is not just increased frequency but a shift in patterns. While overall levels of physical political violence remained low in 2024 compared to years prior, acts of vigilante violence grew as a proportion of all reported incidents.
We’re seeing less organised group violence and more lone-wolf attacks – a pattern that’s harder to predict and prevent.
The Epistemological Challenge
When we use language models to generate “data” about social phenomena, what exactly are we measuring? We’re essentially extracting structured information from the collective corpus of human writing about these events. It’s aggregating distributed information, but through an AI intermediary rather than traditional data collection methods.
This raises fascinating questions.
The models suggest that right-wing extremist violence has been responsible for a fairly large majority of U.S. domestic terrorism deaths since 2001. But how much of this reflects actual patterns versus the way these events are covered and discussed in the sources the models were trained on?
The synthetic data are, in a sense, a mirror of our collective discourse about political violence. They reflect not just what happened, but how we’ve talked about what happened. That’s both a limitation and, potentially, a feature – understanding the narrative landscape around political violence might be as important as counting incidents.
An Experimental Tool
I’ve built an interactive app (using the AI coding tool Lovable) based on this language model-generated violence index.
Users can explore the synthetic data, examine patterns across different time periods and perpetrator groups, and understand the methodology behind it. Think of it as an experiment in using AI to structure historical information rather than a definitive dataset.
The value isn’t in treating this as gospel truth, but in what it reveals about how these events are recorded, remembered, and synthesised in our collective digital memory.
When language models trained on our civilisation’s text output show rising political violence, it tells us something – even if that something is as much about narrative as about underlying reality.
This morning’s tragedy in Dallas reminds us that behind every data point – whether traditionally collected or AI-generated – there are real victims and real consequences. Understanding the patterns, however imperfectly, is the first step toward addressing them.
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