This is an education failure on the part of the purchasers. They should know what you can and cannot do with data. Think about the dataset you'd need to make th

This is an education failure on the part of the purchasers. They should know what you can and cannot do with data. Think about the dataset you'd need to make the burglary prediction. Cops would have some priors about where/when it might happen, but is the data actually collected? You'd need to know a lot of things about a large number of homes in order to see how the characteristics are distributed. Has the house been left empty for a while? Has someone posted about their holiday on FB? Is there something of value in the house? What security measures are there? Are known house-burglary gangs in action in the area? Those are the kind of things you'd think cops might have priors on. I'm sure one can chime in on this. But they're also the kinds of things that you might have problems collecting data for. And even if you had it, your false positive rate needs to be pushed pretty low for it to be useful. Plus your false negative (place is burgled but system didn't say) needs to be really low, in a country where there isn't that much crime to begin with. For the second system, as the article says, you can just say everyone is a risk. It's like saying everyone has Covid, you'll correctly find all the carriers. This is an obvious issue with ML systems that a purchaser needs to understand. The last system, with the recidivism, is maybe the most concerning. It seems to suggest that people's paroles are decided based on this system? Then you'd better have good evidence that the system works, in the sensitivity/specificity sense. https://en.wikipedia.org/wiki/Sensitivity_and_specificity https://en.wikipedia.org/wiki/Sensitivity_and_specificity