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Amazon Is Pushing Facial Recognition Tech That a Study Says Could Be Biased (nytimes.com)
An anonymous reader quotes a report from The New York Times: Over the last two years, Amazon has aggressively marketed its facial recognition technology to police departments and federal agencies as a service to help law enforcement identify suspects more quickly. Now a new study from researchers at the M.I.T. Media Lab has found that Amazon's system, Rekognition, had much more difficulty in telling the gender of female faces and of darker-skinned faces in photos than similar services from IBM and Microsoft. The results raise questions about potential bias that could hamper Amazon's drive to popularize the technology.
In the study, published Thursday, Rekognition made no errors in recognizing the gender of lighter-skinned men. But it misclassified women as men 19 percent of the time, the researchers said, and mistook darker-skinned women for men 31 percent of the time. Microsoft's technology mistook darker-skinned women for men just 1.5 percent of the time. For the latest study, [co-author of the study, Ms. Buolamwini, said] she sent a letter with some preliminary results to Amazon seven months ago. But she said that she hadn't heard back from Amazon, and that when she and a co-author retested the company's product a couple of months later, it had not improved. "It's not possible to draw a conclusion on the accuracy of facial recognition for any use case -- including law enforcement -- based on results obtained using facial analysis," Matt Wood, general manager of AI at Amazon Web Services, said. He added that the researchers had not tested the latest version of Rekognition, which was updated in November.
"Amazon said that in recent internal tests using an updated version of its service, the company found no difference in accuracy in classifying gender across all ethnicities," the NYT reports. The new study is scheduled to be presented Monday at an artificial intelligence and ethics conference in Honolulu.
In the study, published Thursday, Rekognition made no errors in recognizing the gender of lighter-skinned men. But it misclassified women as men 19 percent of the time, the researchers said, and mistook darker-skinned women for men 31 percent of the time. Microsoft's technology mistook darker-skinned women for men just 1.5 percent of the time. For the latest study, [co-author of the study, Ms. Buolamwini, said] she sent a letter with some preliminary results to Amazon seven months ago. But she said that she hadn't heard back from Amazon, and that when she and a co-author retested the company's product a couple of months later, it had not improved. "It's not possible to draw a conclusion on the accuracy of facial recognition for any use case -- including law enforcement -- based on results obtained using facial analysis," Matt Wood, general manager of AI at Amazon Web Services, said. He added that the researchers had not tested the latest version of Rekognition, which was updated in November.
"Amazon said that in recent internal tests using an updated version of its service, the company found no difference in accuracy in classifying gender across all ethnicities," the NYT reports. The new study is scheduled to be presented Monday at an artificial intelligence and ethics conference in Honolulu.
The word biased has a scientific connotation to it. Fake journalists use it to sound smarter. Plus it's been adopted by the offended community, which is the main audience of the Fake News Times. It has special meanings.
Machine learning doesn't work like that. You feed data into it and it works out the algorithms itself.
Confucius say, "Find worm in apple - bad. Find half a worm - worse."
So the message is that the software is much more likely to be successful at apprehending guilty white people? Sorry for using a racist tag ("white") in my comment.
It does sound like it's strongly biased against white people and should be scrutinized carefully.
...when the concern about this tech is not that it exists but that it might be "unfair" to some artificial identity politics minority.
Lol idiots (journalists)
Along with all the other morons who believe that local weather = global climate.
Where are we going and why are we in a handbasket?
the company found no difference in accuracy in classifying gender across all ethnicities
Maybe the spokesperson is clueless, but ethnicity is not race. Look at people in Cuba: some appear Black, some European, some Native American, many are mixed. But all are Hispanic ethnicity.
work by recording the light reflected from objects.
Darker-toned faces reflect less visible-wavelength light.
That's just physics, not racism.
So the amount of light, and ability to resolve contrasts, edges etc, would be less.
So the image classification task might be subject to more error.
Perhaps a different spectral range would work better?
Where are we going and why are we in a handbasket?
Algorithms can most certainly exhibit bias.
https://www.technologyreview.c...
https://en.wikipedia.org/wiki/...
https://towardsdatascience.com...
You are welcome on my lawn.
Machine learning doesn't work like that. You feed data into it and it works out the algorithms itself.
Data can be biased. If the training set is 90% photos of white people, then the NN is going to be better at identifying white people.
But it isn't clear why bias is a problem here. If it correctly identifies a white thief 90% of the time, and a black thief 80% of the time, is it really better to "fix it" so that the white identification rate is lowered to 80%, so that it is "fair"?
Why the hell would you WANT some fucked up bezos computer to identify you as you walk down the street?
I would say it is biased against bald beardless white men. Black women get misidentified at such a high rate the tech is worthless to identify them. That is a GOOD THING FOR BLACK WOMEN!!!
Iâ(TM)m not bald and now I am definitely keeping my beard. For once, a benefit to being a black woman: you dont get tracked and stored by bezos and his evil minions.
And any amazon engineers reading this who worked on the tech, SHAME ON YOU! You could have earned just as much working on tech that is not evil and not putting it straight into the hands of evil people and everyone with a dollar.
When a person recognises another person's face we usually mean to say that they've seen the person before and/or can possibly identify the person.
This slashdot article suggests that something else is meant here: gender and race recognition. Is that indeed the case? Are we asking law-enforcement systems to identify gender and race?
If so, to what end? To find people based that match often vague descriptions?
I'm probably being a moron for not realising this until now. All this time I thought they were just looking to match people on the street with photos of people that are "wanted".
Why is this a good think for Black women? It seems like they'd be falsely arrested at a higher rate than other people, assuming the tech is seeking matches for wanted criminals or terrorists.
Facebook would *never* promote a technology without thoroughly thinking through the implications. They are the pinnacle of corporate social responsibilty...
Come to think of it, that last part may actually be true.
Post may contain irony: discontinue use if experiencing mood swings, nausea or elevated blood pressure.
Is there one single Tolerant Liberal out there who isn't a raging violent profane lunatic?
Obviously this technology needs to be banned from use until it misidentifies men as women as often as it misidentifies women as men. We can't allow anything that yields unequal results from ever being used.
But it isn't clear why bias is a problem here. If it correctly identifies a white thief 90% of the time, and a black thief 80% of the time, is it really better to "fix it" so that the white identification rate is lowered to 80%, so that it is "fair"?
Depending on the application, it could be, yes. The difference in false positive rate between 90% and 80% is double.
If the recognition frequently leads to police action that can be harmful or disturbing for innocents, having a system that falsely identifies one group twice as much as another might cause tension. In that case, lowering the accuracy until it's equal across the board might be prudent, so black innocents aren't twice as likely[*] to be falsely targetted as white innocents are.
Catching more thieves might not offset that injustice.
[*]: Or even more, if sequential targeting occurs, trying the second on the list if the first fails, then the third. The total error rate accumulates faster the higher the uncertainty.
There was a story last year about a woman who had endless trouble with telephone banking because the system was convinced her voice sounded male.
Do you have a citation? I am curious why a bank would treat one gender differently than another, and give "endless trouble" only to males.
I have a Vanguard account, and they use voice recognition as an optional extra security feature, but they treat males and females exactly the same. The VR identifies each customer as an individual. Categorizing voices by gender would be pointless and unnecessary.
It doesn't end, and shouldn't. Every circumstance is different, and new problems can and will present themselves. This is why we have a legislature and government, instead of relying on black-and-white totalitarian laws and regulation, set in stone and not allowing adjustments to reality.
One of the functions of a modern government is to protect the minorities from a tyranny of the majority, and make sure that justice is kept blind, even if it means we sometimes have to deliberately blindfold her.
https://www.bbc.co.uk/news/uk-...
Their fraud system sees that the account belongs to a woman and flags it up when it things a man is calling.
const int one = 65536; (Silvermoon, Texture.cs)
SJW, n: "Someone I don't like, and by the way I'm a fuckwit" - AC
They probably trained this on their full-time development staff.
They should have included warehouse staff, and then a double measure of cleaning/maintenance staff.
There. Fixed that.
Do you know that for certain?
A lot of this stuff is more smoke and mirrors than you might think. They may well feel that a rough classification is better than nothing.
Female voice and they know the account holder is male? Reject!
British accent, and they know (from voice analysis) the account holder has a Valley Girl accent? Reject, fershure!
A very reasonable approach, really. Don't assume that this stuff is doing sophisticated voice prints.
The fix for that is to invest in better quality equipment that works all over the USA.
Its not a math, design, computer problem. Its a global data set problem.
Keep working on the design until it works as expected on all average passengers, drivers around the USA.
The demographics of a city should be easy to understand. Find nations with the same average demographics and see what their best CCTV detection rate is?
Other advanced nations have the same count of people to track with CCTV and passports/national ID cards everyday.
Bring all the working CCTV code back to the USA and every advanced nations police detection math can be added to US math.
Slowly the detection rate will go way up and less police will be needed to respond to data sets that did not match.
Jobs, math, cooperation, more police work and more police over time, criminals and illegal migrants detected.
City crime rates go down and investment returns. Illegal migrants get found.
Nations that gave the US their demographic math/code get a huge return as the USA exports back their great new police CCTV products.
Faster computer networks, better detection of every face.
Export jobs in the USA with new advanced CCTV systems for global use.
More winning.
Domestic spying is now "Benign Information Gathering"
Some people are under the mistaken impression that something wildly inaccurate and highly biased towards false positives wouldn't be sufficient to void Constitutional rights. The Supreme Court put that notion to rest when they ruled a cop merely needs to claim a dog trained to please him gave permission, and the 4th Amendment is gone. People who think inaccuracy is a good thing will be sorely disappointed. Well, they won't until the day comes when they're on the receiving end of police bullshit, but then they will.
I'd be curious to see how it classifies "Kaitlyn" Jenner, or "Chelsea" Manning.
Remember when Google's image algorithm classified multiple black people like gorillas and monkeys? YouTube's video suggestion algorithm did the same thing: After a report on a crime committed by black people, viewers were recommended a video of a baby gorilla born at a zoo.
This pattern recognition was completely unbiased. Without human political correctness, computers do think black people look like gorillas, and that's a harsh pill for many to swallow.
Am I surprised that black people are again on the raw end of computer logic? No. Computers think that black women look like black men a significant percentage of the time. Programmers now need to try and figure out how not to misgender black women (a really hard problem to solve in technology). I suggest that young non-black children might even have a similar struggle and error rate in black gender identification by facial photo.
This must be a new meaning of the word "biased" - adj, giving a result I don't agree with.
Confucius say, "Find worm in apple - bad. Find half a worm - worse."
You read those citations and THAT'S your takeaway? I'm not sure I believe you're that stupid, but I guess I could be wrong.
You are welcome on my lawn.