Predicting Violence in Advance With AI

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From the original article

The template for a typical ‘Can AI automate this?’ line of inquiry is to feed multiple examples of something whose characteristics you want to isolate, and see if a model trained on that data can pick out that characteristic on similar data that was not used in training:

Schema for a ‘Hail Mary’ AI project: labeled examples are divided into training and test sets, with the model learning from the first and then being challenged with unseen examples from the second. If it has genuinely learned the characteristic of interest rather than memorized the training data, it should recognize the same pattern in the test set.

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It’s a bit of a ‘Hail Mary’, since such experiments rest on the assumption that the characteristic will eventually stand out in the data if the AI keeps looking at the dataset, and that there is actually any kind of ‘underlying trend’ to elicit. If the characteristic was something we could easily recognize, we either wouldn’t need the AI model at all, or we would only be using it for routine automation.

This is not generally the case; most such research initiatives are ‘fishing expeditions’ for latent patterns that only superhuman repetition of attention could learn to recognize.

Often enough, the pattern turns out to be elusive, and may in fact be non-existent, because some events and traits may truly operate at pure chance; or else, the data is in some way inadequate, poorly curated, or poorly-trained.

In some cases, the prize is so desirable (cure cancer, nuclear fusion, overcome laws of thermodynamics, etc.) and so ‘politically attractive’ that the literature will return to it persistently, to see if better data or better techniques can yet elicit a characteristic pattern.

One such field is Automated Video Understanding, as it relates to public spaces and police-involved domains. For instance, this year a new study used real-world video footage of suicide attempts to identify ‘dwelling’ trends prior to a person’s suicide attempt, and to create a predictive system that could spot the characteristic way that they move around a public train platform prior to jumping:

From the 2026 paper ‘Suicide Risk Assessment from AI-powered Video Surveillance: An Interpretable Framework for Prevention in Metro Stations’, predictions from two frames, one showing a genuine rail suicide attempt and the other a non-incident case. Heatmaps beside each frame indicate areas of higher and lower risk on the platform, based on a person’s “dwell tendency” near the tunnel mouth and patterns observed in previous suicide attempts.

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In this case, the ‘dwell patterns’ (hard left and right in the image above) typical of a person conceiving of jumping in front of a train appeared to have a distinct signature that could be used as a pattern-recognition alert system in automated surveillance circuits.

Another popular strand of research in this line is violence detection/recognition, where unattended surveillance systems could be made capable of recognizing violent action, in order to bring the matter quickly to human attention – and even to trigger ancillary operations, such as flagging the footage for long-term preservation, and ensuring that recording is actually taking place, among other measures.

According to new research from the University of Alabama, the literature to date has concentrated on recognizing violence when it is already happening. For the new work, the researchers trained a model only on video footage prior to a violent event (or to no event, for statistical balance), to see if there might be recurrent and trainable ‘pre-violent’ characteristics – in this case, a combination of body motion, facial disposition, and audio, which together could provide a predictive signal for violent action, instead