Sure. "Falsely" :-)
Amazon’s Face Recognition Falsely Matched 28 Members of Congress with Mugshots
91–100 of 114 posts
Re: Amazon’s Face Recognition Falsely Matched 28 Members of Congress with Mugshots
#92Earlier 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.
I don't really understand the sides and arguments you are implying here. Could you spell it out please? (I'm not from the US, and have no relevant experience with cops, guns.)
Re: Amazon’s Face Recognition Falsely Matched 28 Members of Congress with Mugshots
#93Some commenters here mentioned they may have setup the test incorrectly and that may be, but I think the problem this highlights most is that technology used improperly, especially by law enforcement can have major ramifications and consequences. Is the contractor that makes software for your local department going to follow best practices and have the algorithm audited by experts? Will they release the code? Those a…
I think a fun thought exercise is finding the fine line between tech and guns/alcohol/cars. You cannot sue a gun/car/alcohol manufacturer if their product is used to injure someone because it functioned as designed but was used maliciously. How does that legal precedent work when extrapolated to tech and something like facial recognition? If it worked exactly as designed and we know it has a margin of error (or can b…
If firearms manufacturers produced guns that fired straight over ninety percent of the time, but no guarantee of bullet direction beyond that, they'd probably have a lot of lawsuits on their hands.
Re: Amazon’s Face Recognition Falsely Matched 28 Members of Congress with Mugshots
#94Earlier 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...
One major difference is that non-technical people tend to trust what a computer says far more than they should. People are familiar with sketches and can make judgments on how clearly they match a face. They have no similar frame of reference for computer face recognition.
But people are familiar with photographs.
There is no way the software wouldn't show the picture that's been matched before an action (arrest...) is taken
Re: Amazon’s Face Recognition Falsely Matched 28 Members of Congress with Mugshots
#95Some commenters here mentioned they may have setup the test incorrectly and that may be, but I think the problem this highlights most is that technology used improperly, especially by law enforcement can have major ramifications and consequences. Is the contractor that makes software for your local department going to follow best practices and have the algorithm audited by experts? Will they release the code? Those a…
I think a fun thought exercise is finding the fine line between tech and guns/alcohol/cars. You cannot sue a gun/car/alcohol manufacturer if their product is used to injure someone because it functioned as designed but was used maliciously. How does that legal precedent work when extrapolated to tech and something like facial recognition? If it worked exactly as designed and we know it has a margin of error (or can b…
I think a better analogy would be a bridge or building- they are regulated by a rigorous code that will exonerate the engineers behind the implementation only if it was followed to the letter.
Will that slow things down and make them more expensive? Absolutely, but that’s the cost of safety.
In this case though what is probably most lacking is public knowledge about the shortcomings of such systems. The public needs to understand that 90% accuracy, while it sounds high, mean it’s wrong a lot, and the chances of it being wrong if it was continuously run all the time are actually quite high.
Re: Amazon’s Face Recognition Falsely Matched 28 Members of Congress with Mugshots
#96Earlier 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.
Regarding point two, I can't really see any argument that an encounter with a police officer is not more likely to go south if the officer is under the misapprehension that one is a potentially dangerous criminal; Police shootings are already not infrequent in the US, and adding the tension of greater perceived danger makes an already volatile situation worse - a sudden movement that might otherwise pass unnoticed is much more threatening from someone who has been misidentified as an armed gunman, for example, and is far more likely to elicit lethal response.
Re: Amazon’s Face Recognition Falsely Matched 28 Members of Congress with Mugshots
#97https://www.theverge.com/2018/7/26/17615634/amazon-rekogniti... >Reached by The Verge, an Amazon spokesperson attributed the results to poor calibration. The ACLU’s tests were performed using Rekognition’s default confidence threshold of 80 percent — but Amazon says it recommends at least a 95 percent threshold for law enforcement applications where a false ID might have more significant consequences. Presented witho…
You think Police Departments won't knock down the threshold just for justification to knock down some perp's door? They've done far worse to get the results they want.
Re: Amazon’s Face Recognition Falsely Matched 28 Members of Congress with Mugshots
#98> 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
> 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.
I actually did not know this. Thank you. I had conflated incarceration rate with inmate population. If anyone is curious for a source, see here: https://www.bop.gov/about/statistics/statistics_inmate_race....
> Moreover, if a algorithm enforces bias to the detriment of inoccents, it's a bad algorithm
I agree with this 100%. But they haven't provided enough information about their dataset to make this conclusion. If they provided enough information for someone to independently analyze the data and reproduce the experiment, I would flip immediately.
Re: Amazon’s Face Recognition Falsely Matched 28 Members of Congress with Mugshots
#99> 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…
And the data is "racist" as well because...? This whole topic is racially charged because incarceration rates are a race issue and you are being disingenuous with your comment pretending the problem with facial recognition can somehow exist and be fixed in isolation.
If your data is disproportionate then you're more likely to get matches in the larger portion. The ACLU is claiming that Rekognition is racially biased, but their experiment could be setting them to get the result they want because their dataset is already biased.
I'm not saying the topic isn't a race issue, I'm saying the ACLU isn't providing the data for someone to verify their results and is possibly making false claims that no one can verify.
Re: Amazon’s Face Recognition Falsely Matched 28 Members of Congress with Mugshots
#100Some commenters here mentioned they may have setup the test incorrectly and that may be, but I think the problem this highlights most is that technology used improperly, especially by law enforcement can have major ramifications and consequences. Is the contractor that makes software for your local department going to follow best practices and have the algorithm audited by experts? Will they release the code? Those a…
I think a fun thought exercise is finding the fine line between tech and guns/alcohol/cars. You cannot sue a gun/car/alcohol manufacturer if their product is used to injure someone because it functioned as designed but was used maliciously. How does that legal precedent work when extrapolated to tech and something like facial recognition? If it worked exactly as designed and we know it has a margin of error (or can b…
Would a camera manufacturer be liable if the camera's image processor happened to cause one person's photograph to look like another, which then caused a human looking at that photograph to misidentify someone?