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Amazon’s Face Recognition Falsely Matched 28 Members of Congress with Mugshots

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Re: Amazon’s Face Recognition Falsely Matched 28 Members of Congress with Mugshots

#71

Post by Kalium »

dragonwriter wrote: post #65
notyourwork wrote: post #60

Earlier quoted context omitted.

Threshold is a configuration property no different than a dynamo read capacity. Misconfigured properties don’t work the way you expect. I don’t see how documentation can be expected to give black and white guidance to what this threshold should be set to without having more understanding of your use case.

> I don’t see how documentation can be expected to give black and white guidance to what this threshold should be set to without having more understanding of your use case. It can say exactly what the Amazon response to the Verge dismissing the ACLU test should, and, in fact, it should say that if Amazon really does have such critical, specific domain-specific recommended settings, rather than it being something made…

You're unquestionably correct. Amazon should spell out in clear, simple, easily accessible language what level of certainty is appropriate for what type of application, giving concrete examples at every point.

With that said, it's possible that no amount of clear guidance would have mattered here. The ACLU isn't exactly asking for better docs.

Re: Amazon’s Face Recognition Falsely Matched 28 Members of Congress with Mugshots

#72

Post by zo1 »

sudhirj wrote: post #58
joemaller1 wrote: post #10

Things I want to know: - Show us the side-by-side images of the false-positives. Are the matches plausible? - What is the demographic distribution of the mugshot database? If the data is disproportionately biased, then that bias would be reflected in the false-positives. A casual skimming of some mughot websites shows a potentially significant racial bias.

Whether the matches are plausible is completely besides the point. This isn't an evaluation of Amazon's recognition quality, it's a demonstration that the system is fallible and notes on the consequences. Same thing re data setup. Not the point again. Even if law enforcement hires the world's best computer vision experts (fat chance, it'll go to cheapest contractor) this is a note that there can be mistakes that get…

I don't see how this is a problem, unless the police are feeding it blurry surveillance camera footage then claiming "success, it matched person X", which I doubt, as it would fall apart at the smallest of questioning.

For the most part, this sort of article makes things seem like there will be some sort of racial-based, dystopian police future where automated algorithms will target people and have them put into jail. I don't see that happening in the near future at all. The police departments' personnel currently using this probably LOOK at the matches and only then make a further call. Just like I would assume AFIS fingerprint hit results are double-checked by skilled operators after matching to reduce false-positives.

Re: Amazon’s Face Recognition Falsely Matched 28 Members of Congress with Mugshots

#73

Post by zo1 »

sudhirj wrote: post #62
petermcneeley wrote: post #59

Earlier quoted context omitted.

What are the consequences?? How is this any different than the suspect sketches? This is a far worse situation and still quite minor https://www.cbc.ca/news/canada/british-columbia/bc-mother-ch...

What does the link have to do with sketches? It's about name similarity on the no fly list. Regarding sketches, they're actually interesting because they force a human to exercise judgement, being ambiguous by nature. This is fine - any law enforcement officer stopping someone who looks like a sketch is going to give them the benefit of the doubt. On the other hand, if a computer tells an officer a person ahead is an…

"[..]conversation will start with guns drawn, and that will greatly increase the likelyhood of things going south."

That is where the two sides of this argument diverge. First: That things "will start with guns drawn". And Second: That things are more likely to "go south" for an innocent individual incorrectly blamed by this.

Re: Amazon’s Face Recognition Falsely Matched 28 Members of Congress with Mugshots

#74

Post by dang »

hanselot wrote: post #12
joemaller1 wrote: post #10

Things I want to know: - Show us the side-by-side images of the false-positives. Are the matches plausible? - What is the demographic distribution of the mugshot database? If the data is disproportionately biased, then that bias would be reflected in the false-positives. A casual skimming of some mughot websites shows a potentially significant racial bias.

How dare you question our great overlords the ACLU. They would never use statistics to find results favourable to their hypothesis.

Maybe not, but please don't post unsubstantive comments here.

Re: Amazon’s Face Recognition Falsely Matched 28 Members of Congress with Mugshots

#75

Post by zo1 »

otakucode wrote: post #32
joemaller1 wrote: post #10

Things I want to know: - Show us the side-by-side images of the false-positives. Are the matches plausible? - What is the demographic distribution of the mugshot database? If the data is disproportionately biased, then that bias would be reflected in the false-positives. A casual skimming of some mughot websites shows a potentially significant racial bias.

When facial recognition fails, it usually looks pretty weird. Like it will identify faces in the knots and grain patterns of a wooden wall behind somebody. It's really a very ineffective technology and it's disturbing that it gets sold as something you could use to justify locking someone in a cage over. Machines with pareidolia are not good tools.

When people speak of "facial recognition", they speak of faces being compared to other faces. What you're mentioning is face-detection. Are you honestly saying that police departments will have a "criminal database" filled with wooden walls and furniture?

Re: Amazon’s Face Recognition Falsely Matched 28 Members of Congress with Mugshots

#76

Post by andrewguenther »

> People of color were disproportionately falsely matched in our test.

They are trying to make this racially charged without giving enough information to verify their claims. If you use a dataset of mugshots, that's statistically going to have more data on people of color. If you have more data on people of color, it is more likely to match people of color. Claiming the algorithm is racist because your data is racist is inflammatory bullshit.

Re: Amazon’s Face Recognition Falsely Matched 28 Members of Congress with Mugshots

#77

Post by spamizbad »

andrewguenther wrote: post #76

> People of color were disproportionately falsely matched in our test. They are trying to make this racially charged without giving enough information to verify their claims. If you use a dataset of mugshots, that's statistically going to have more data on people of color. If you have more data on people of color, it is more likely to match people of color. Claiming the algorithm is racist because your data is racist…

How is stating a fact- it disproportionately matched people of color- somehow the same as claiming racism?

Re: Amazon’s Face Recognition Falsely Matched 28 Members of Congress with Mugshots

#78

Post by [deleted] »

zo1 wrote: post #73
sudhirj wrote: post #62

Earlier quoted context omitted.

What does the link have to do with sketches? It's about name similarity on the no fly list. Regarding sketches, they're actually interesting because they force a human to exercise judgement, being ambiguous by nature. This is fine - any law enforcement officer stopping someone who looks like a sketch is going to give them the benefit of the doubt. On the other hand, if a computer tells an officer a person ahead is an…

" [..]conversation will start with guns drawn, and that will greatly increase the likelyhood of things going south. " That is where the two sides of this argument diverge. First: That things "will start with guns drawn". And Second: That things are more likely to "go south" for an innocent individual incorrectly blamed by this.

[deleted]

Re: Amazon’s Face Recognition Falsely Matched 28 Members of Congress with Mugshots

#80

Post by rrego »

andrewguenther wrote: post #76

> People of color were disproportionately falsely matched in our test. They are trying to make this racially charged without giving enough information to verify their claims. If you use a dataset of mugshots, that's statistically going to have more data on people of color. If you have more data on people of color, it is more likely to match people of color. Claiming the algorithm is racist because your data is racist…

Pretty lousy argument. Firstly, the majority of inmates in the US are white (58%). I'd assume mug shot stats are similar, or at the very least not mostly black people.

Moreover, if a algorithm enforces bias to the detriment of inoccents, it's a bad algorithm

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