Several problems with your points
- Data
For the SPAM example you provided, you used a Data Set available publicly with no consequences. But where will the training data for prediction of homicides will come from? Will it be accurate? Will it unfairly target minorities?
The predictor uses features in the data to predict the future. What features will the machine learning algorithm learn to predict homicide? location? age? gender? ethnicity? mannerism? income? I could see big problems with any features used to detect and potentially send law enforcement for "Investigations"
- Detectors
Capturing and sending users data to a warehouse is a privacy/security risk. But what is worse is installing a detector in their house, or in their computer.
What happens when a citizen removes the SPAM detector from their computer? Well, the next logical step is to pass a law requiring all citizens to run a specific process on their computer and are not allowed to reverse engineer it or else...
For me, this is even worse than having my privacy violated. It would mean users would not have root access to their own computer. It would mean, many applications will be illegal and developers cannot write certain applications that enable people to send emails. If this is not impossible, it would be the worst outcome for everyone.
- Globalization
The SPAM detector, the fire detector and the sniffer dog are great localized examples. But todays problems are globalized. Attacks might be planned and coordinated from different country with different set of rules. And not all countries are considerate when it comes to privacy. How will global security surveillance deployment work when we can't even agree on matters of climate change?