| Those are the šš¼šæšš possible uses for this. To illustrate lets imagine a really really good system that had a 0% false negative rate¹ , and a 0.000001%² false positive rate. If we were to sample the entire country looking the the FBI's most wanted we would end up with ~3290 matches, 3280 of which are going to be other people³. Considering high value of the individual the chances of harassment or wrongful arrest (or worst) is pretty high. 1: In reality the false negative and false positive rate are going to be directly inversely related. The more you decrease the false positive rate the higher your false negative rate is. 2: That is 1 in 1 million 3: In this ideal situation it is assuming there is an even distribution. At this point computer vision is significantly worst for false positives for people of color. |
How big is the false positive rate with tips received by phone? Law enforcement probably has to deal with much more wrong clues on a daily basis than to double check an image that was flagged by an automated system. And if even a human inspection can't tell the difference between the missing person and an image of the missing person then it's definitely worth checking out.