One thing more people need to be aware of is the False Positive Paradox.

If you have an app that can identify people with 97.6% accuracy, that’s pretty good right?

No! When looking for a small sample in a large enough population, it’s really bad.

I ran the numbers for a facial recognition app with a 97.6% accuracy rate, looking for a group of people that make up approx 3% of the US population. Of the ~8 million matches it will find, with a 97.6% accuracy rate, around 96% of the matches will be false positives.

When the thing you are looking for is rare, you need a margin of error that is significantly lower than the rarity of the thing you are searching for. Otherwise you get some unintuitive (and potentially alarming) results.

When you deploy those tests at scale, your results become garbage.

That 97.6% accuracy rate is from facial recognition is for algorithms being tested in ideal conditions. Not when the subject is a terrified, trembling person being filmed in a snowstorm. In those conditions, I can guarantee that the accuracy is significantly worse.

Anybody who claims an app can provide a ‘definitive’ identification in the field is lying to you.

Anybody who claims such an app should supersede provided documentation is malicious.

Anybody who buys into those claims, I happen to have a bridge for sale (and I don’t feel bad about selling you it).