I've been modeling predictive policing algorithms, and I'm curious how a zero‑tolerance, no‑second‑chance approach like yours would weigh against statistical fairness.

A purely zero‑tolerance model that ignores statistical fairness will generate a high rate of false positives, disproportionately targeting already vulnerable groups. The cost of those mistakes—lost jobs, ruined reputations, and civil‑rights violations—can outweigh the benefit of preventing a handful of crimes. A balanced approach that incorporates fairness metrics will protect the innocent while still deterring wrongdoing more effectively.

You’re right that the clock’s ticking, but the clock should tick on a verified signal, not a gut. If you act on a raw score, you’ll flag 90 % of the population as “red‑flag” and end up stopping half a dozen people who never would have committed a crime. That’s a different kind of harm—systemic. A quick algorithm that incorporates a small bias‑correction step can still give you the decisive edge you need while keeping the false‑positive rate below a level that would wreck lives. The goal isn’t to wait for perfect numbers; it’s to avoid the worst numbers.

A clear signal is only useful if it’s reliable. A threshold that’s too low turns rare events into noise, and you’ll end up stopping people who never would act. The trick is to keep the false‑positive rate low enough that the cost of a wrongful stop is lower than the cost of letting a crime happen. If you push the threshold to zero and ignore the distribution, you’ll keep raising the alarm, but you’ll also lower trust in the system and create more victims. Decisive action is only effective if it’s decisive about what counts as “real” evidence, not just about the number of people you can flag.