So this is a good application of the birthday paradox actually and the fundamental misunderstanding people have of this is already a real problem in law enforcement.

Take DNA testing. We all know that given two samples the odds of a false positive are incredibly low. So DNA testing is a great too for eliminating people who might otherwise be suspects or further evidence against the guilty.

The problem is that given this DNA database, someone decided "let's find our suspect by looking for a match". With 1 in a billion chance of a random match and 100M samples, your chances of getting a false positive are really high.

The problem is that there are instances where a DNA match alone is used to prosecute or even convict people.

In recent years this problem has gotten worse due to the rise of familial DNA matching. Given two samples, we used to only have the ability to say if they were a match or not. Now we can say how much of a match they are. How much of a partial match is enough? What's more, you may be implicated by the stored DNA is relatives.

Facial recognition is far more imprecise than DNA. So yeah I fully expect this to get abused by prosecutors and law enforcement.

It's something like the "rare disease" paradox in medical testing and sensitivity and specificity.

If I increase the population I test to include any warm body, eventually I'll end up with more false positives than real positives.

If you are looking for one male suspect and comparing them to all the male faces (let's assume it gets gender correct) in the us, with a 99.9999% (six 9s) accurate algorithm you would get something like:

1 true positive 182,499,818 true negatives 182 false positives

Broad scale facial recognition is just an outright stupid thing to do as a sole measure of identification without the use of other information.

Yes, I think in the modern world with these methods and massive data sets being used in law enforcement, medical sector etc, the people working there should undergo a lot of training about how basic Bayesian inference works etc. We are otherwise in a world of hurt. Healthy people being told they have some really serious rare diseases, maybe even starting treatment, innocent people being prosecuted or killed during raids etc.

Yeah... training almost assuredly won't happen sadly. Especially for cops.

I teach this stuff (biomedical engineering statistics) to undergrads.

The hardest thing for them with probability is false positives. conceptually it does not seem to align well with how the human brain works. I've had students literally get so frustrated the cry because they know what the math says but can't accept that you can test positive for a disease and not have it. They know they are wrong but it's just this weird sticky misconception.

I think you nailed it on the head with, it is 'just not a useful thing to do'.

This seems like a clear example for why facial recognition is a technology that is just not 'solved' yet. The appearance of people's faces, especially from similar ethnic backgrounds, is just too similar for a ML model to parse out with any confidence.

I have noticed this in real life. As I get older, I notice it more and more. I'm sure many of you all have too. There are very distinct patterns, or 'buckets', that human faces tend to fall in. I think our brains tend to naturally categorize them accordingly.

It is probably subconscious. I might not be able to articulate it, or put a definite 'name' on a group. But I know I am constantly seeing patterns of faces in public. People I don't know, and have never met, but they remind me of other random people I have seen in public. Or maybe they remind me of a popular celebrity that everyone knows.

Either way, something goes off in my head. I can't help but think to myself, they must have some sort of similar lineage, or genetic background. I subconsciously categorize them into a bucket with others I've seen.

I imagine this is similar to how Rekognition, and other models work. I thought the blog post from the parent commment @bko, is a fantastic example of this. It is actually amazing, when you think about it, that the ML model can match these faces up as well as it does.

To the naked eye, it is clearly not the same person. Rightly so, considering all the images were in the range of 70-80% confidence. But many are remarkably close. I think this illustrates, the concept I am trying to describe. You can notice it, even with the naked eye.

All of this rambling is to say, I agree with Amazon's moratorium on Rekognition.

As impressive as the technology is, it should probably not be used to try pin-point specific individuals yet, or whatever else folks might be erroneously trying to use it for. If we are to trust facial recognition to identify specific individuals, it should probaly be approaching near 100% confidence, and I imagine that level of confidence is a long ways off.

I sorta think about it as "what's the likelihood of someone winning the lotto twice in a year?" Certainly the probability that any specific person wins twice (assuming here these are independent events...) is so ridiculously small. And yet probability at least 1 person somewhere is that lucky is incredibly high!

Now ask if my neighbor looks like any random person on the street, sure, it's small. But that chance we find two people somewhere that look very similar is incredibly high.

I think the interesting question is what is the chance of doppelgangers given population size and genetic similarity of the population.

Sounds like the prosecutor's fallacy to me unless great care is taken to also consider lots of supplementary information when performing a match. (https://en.wikipedia.org/wiki/Prosecutor's_fallacy)

Recommend reading the part about Sally Clark. The last two sentences are an incredibly short and understated horror story.

"Sally Clark was a practising solicitor before the conviction. After her three-year imprisonment she developed a number of serious psychiatric problems including serious alcohol dependency and died in 2007 from acute alcohol poisoning"

It's only not a useful thing to do if you care about innocent people being wrongfully convicted, which is as you look at the US justice system[0] is debatable. From an outside perspective the US justice system seems to care more about the false negative than the false positive rate of conviction.

There is already a whole slew of dubious methods[1] (bite mark analysis, blood splatter analysis, fibre comparison) being used to put people in prison/to death, these things are known to be bullshit, but they are still being used. I really think we as technology-literate people should fight as hard as we can against the introduction of facial recognition in the justice system because once it becomes common place it will be really hard to undo it.

[0]: https://www.innocenceproject.org [1]: https://www.latimes.com/opinion/op-ed/la-oe-humes-forensic-e...

Denmark has a fairly homogeneous population (discounting the much needed immigration) I swear there are basically 5-8 Danish feature sets that sometimes I amuse myself seeing how they are put together.

This of course means that there are doppelgangers or near doppelgangers all over the place. Even funnier, when i first moved back here from the U.S I was staying at a place in which there was this guy who looked exactly like one of my friends from the U.S, even down to facial expressions and mannerisms (maybe that was because he was always extremely stoned).

Yeah but I am not sure it is suggested to be used in any other context than where a human would be comparing faces too. Ie searching for someone in a crowd or cctv, or checking a passport picture against someone at the gate. None of that is burden of proof level of accuracy. But it just allows to better cover a crowd or process more people at the airport.

If you had accuracy similar to a human, and assuming this is an existing procedure humans do use, then of course it's useful -- you're doing the same task cheaper/faster.

Presumably you'd also use the same techniques as used by humans today to narrow down further, like taking into account location, time, etc of match

Doing the same thing but faster and cheaper doesn't necessarily mean it's better. If there's a high cost to a procedure, then the user would need to show the value of checking a candidate in some other way for it to be worth the effort. Making it easier and faster means you're more likely to check against random innocent individuals.

Fast, cheap, good. You can only ever choose two.

Often, you are lucky to get one.

Seems like the equivalent of p-hacking to me. Sure they could apply complex queries to narrow down the suspects in a fair way, but I don't trust them to do that.

> you're doing the same task cheaper/faster

Move fast and break lives.