I tried to recreate this using Rekognition. I used a public mugshot database. Here are my results [0] if you're curious about what these false matches look like.
[0] https://medium.com/ml-everything/how-facial-recognition-work...
[0] https://medium.com/ml-everything/how-facial-recognition-work...
> However, just from a cursory review of the mugshots, people of color were disproportionately represented in the mugshot database. So it’s not entirely fair to criticize the facial recognition technology for matching more people of color.
No, the point is the disproportionate representation, and it is a fair criticism, because it is a fundamental limiting factor in the use of the technology. You seem to be distinguishing the "technology" from the data source, but that is not possible. The quality of the technology depends on the quality of the data provided to it.
> I believe a curated list of mugshots with certain characteristics would result in a similar representation of mismatches. Nothing I have read about the technology suggests that there are inherit [sic, is inherent] bias.
This bias is in the data, not the algorithm per se. This is quite accepted in many, even most, criticisms of machine learning applications in the social sphere. It comes up a lot for example in NLP models, for example with assumptions on gender for professions.
It is a technology problem because it demands a technological solution, because removing any and all bias by hand-curating datasets is just not a scalable approach.
The algorithms reflect and amplify biases in the data. They also go into feedback loops once they affect the reality being represented in data.
Imagine that an algorithm selects individuals at an airport for search. Contraband smugglers are caught. The dataset now includes them, evolving the bias. We have already seen this at play on social media, recommendation engines, fraud detection, etc.
The future is worrying. There are ratchets on technologies such as these. Unless there are extraordinary counter-reactions, they will be "affecting their own datasets" en masse very soon.
The difference is with technology these biases can at least try to be addressed directly and create an objective more fair system.
Technology can address biases but we need to make sure that they are addressed sufficiently, so that we do not end up causing more problems than we solve.
This is a really interesting point that I'll keep in the back of my mind, thanks for that! I hadn't even thought it through in such a concrete manner but I think this really hits the nail on the head.
I actually think it’s probably better than most people’s naked eye recognition in a crowd.
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.
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.
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.
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.
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.
"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"
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...
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).
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
Move fast and break lives.
For comparison, I did another test where I took one of those youtube videos that shows you the same person's face over ten years. I used the original 12 year old boy as the image I'm using to match. Over 1,300+ images, it had > 70% confidence in all but 4 images (2 had big sunglasses, one the guy was in green-face, and the other one was actually his wife). And this is from a single picture that's ten years old.
The first part I looked at an open-source facial recognition model that did considerably worse.
https://medium.com/ml-everything/how-facial-recognition-work...
> > Only one in a thousand abusive husbands eventually murder their wives
> The more pertinent question is what percentage of murdered women were murdered by their abusive ex-husband?
Shouldn't this be "what percentage of murdered women with an abusive ex-husband were murdered by said ex-husband"?
Yay, we have an algorithm judging people, but is it fair to Canadians? Completely off...
I know how ML works but I believe we should demand better from these technologies than the limits of our own biases.
Right now, this machine learning algorithm is apparently about as smart as a bigot arguing "yea but percentages show that crime is in fact higher among blacks!". It mainly shows how systemic the racism is, that a dumb ML algo picks up on it.
This is not solved by showing the bigot less statistics about black crime, but by showing them how to pull their head out of their ass.
We should expect no less from our ML technologies, otherwise you'll keep running behind the facts, always fixing errors after they have been learned and made.
Yeah that is hard and we have no idea how to approach it. But the alternative appears to be writing computer programs with the reasoning skills of a racist cop.
You're using the strawman and whataboutism logical fallacies, but you're not actually making a point.