I see your point but as OP mentioned: 1) Where is the data that shows each street is equally/proportionally scanned by parking attendant vehicles? There is pot

I see your point but as OP mentioned: 1) Where is the data that shows each street is equally/proportionally scanned by parking attendant vehicles? There is potential discriminatory policy there. 2) Where is the data that shows that scan-success rate (i.e., percentage of detected cars, for which a plate number could be made out) is distributed normally, rather than e.g. influenced by parking infrastructure which is correlated with socioeconomic factors? Again, potential discriminatory practice. Other possibilities exist, too. These may sound far fetched, and you may be right to say that, but the data would show it. It'd be nice to see the data. It cannot be said a priori that 'algorithm just checks the image, so there's no discrimination possible', if there's no word about ensuring that regardless of various factors (e.g. socioeconomic, racial etc), your non-compliance with parking rules has an equal chance of your plate being scanned and fed into the algorithm. It's the same issue why racial profiling is controversial. You can say 'this computer/algorithm is perfect at assessing non-compliance with a rule or regulation by individual X regardless of e.g. ethnicity', and you could still have discrimination because ethnic group A is fed into this system by way of racial profiling 10x more than ethnic group B. By the same token, we'd want to see where/how-often the scanning vehicles operate, and how their scans may have lower or higher success rates in various situations. In short, the OCR might be alright, but the entire process/model/architecture (from scanning to issuing a fine, in which OCR is just one of the middle-steps) may not be. That entire model including scanning (photographing) by a computer is described on the page, so saying its non-discriminatory implies they've assessed more than just the OCR step. And if that is the case, they cannot say it's a priori non-discriminatory quite yet without providing more info/data.