Cloud Vision API will not return gendered labels such as 'man' and 'woman'
Hello Google Cloud Vision API customer,
We are writing to let you know that starting February 19, 2020, the Cloud Vision API will no longer return gendered labels such as 'man' and 'woman' that describe persons in an image when using the ‘LABEL_DETECTION’ feature.
What do I need to know?
As you know, the Cloud Vision API can perform feature detection on a local image file for the purpose of identifying persons by sending the contents of the image file through ‘LABEL_DETECTION’.
Currently, when you request the API to annotate an image with labels, if you use this feature on images with people, it may return labels describing them in an image with gendered terms, like ‘man’ or 'woman’.
Given that a person’s gender cannot be inferred by appearance, we have decided to remove these labels in order to align with the Artificial Intelligence Principles at Google, specifically Principle #2: Avoid creating or reinforcing unfair bias.
https://ai.google/principles/
40 comments
[ 1.1 ms ] story [ 1302 ms ] threadThe fact is gender can be determined by appearance in 99.5% of circumstances or more, which is probably higher than the overall accuracy of their ML labelling system.
The idea of "avoiding unfair bias" is highly vague and problematic since, theoretically nearly any statement or view beyond "particle X is at Y" type statements has a somewhat "unfair" bias reflecting the lens of the human experience, which has developed over millions of years of biology and thousands of years of culture.
Finally, seeing as the fact is that 99% of people would not be negatively affected by this feature remaining whatsoever, and the very slight negative impact on the other (intersex type) 1% or so is minimal at best, while the total impact of removing the feature is mildly annoying for many users, this seems a lot like irrational, hollow, virtue signalling.
The biggest group likely to be impacted is trans people, not intersex people.
Trans people have been around forever, and aren't going away just to make image classification less complex. It's not a "trend", treatment options just happen to be more available and the internet makes communities more visible now.
So the solution would be to not identify _anyone_ as people? As a ginger I doubt that.
https://adssettings.google.com/u/0/authenticated
Here Google can infer my gender and my age and personal interests just from my search history. I am sure this is not perfect either, it is still immensely useful for advertising.
Humans don't have much trouble, except against adversarial/outlier examples.
Now, 0.6% of Americans identify as transgender [http://williamsinstitute.law.ucla.edu/wp-content/uploads/How...]. That means using sex prediction alone, we can achieve a theoretical maximum of 99.4% accuracy here. This ignores the fact that transgenders often signal their gender via behavior or clothing, or attempt to obtain the physical characteristics of their assumed genders as well, both of which could further improve performance.
Loads of research suggests 7 year olds are about 70% accurate at predicting gender based off of pictures. It takes a couple of decades to get that into ranges you’ve cited. That doesn’t sound easy. If it is, I’d love to read about it.
Why be toxic?
Isn't this just outright wrong? A person's gender can be inferred by appearance, and with extremely high accuracy at that.
If organizations are creating systems using that API, most will just roll with man/woman labeling. If those systems are important, a lot of trans and non-binary people will end up fucked over.
At the end of the day, if the deprecated functionality is still needed, just set a threshold on masculine vs feminine. It's a far more elegant approach, even if more work.
Humans have trouble with this too, e.g. many crushed on Taylor Hanson.
All it's saying is that it won't make gender classifications based on perceived masculinity/femininity.
Just because you can automatically classify certain features to some degree of accuracy doesn't mean you should. A company like Google also has an ethical obligation to think about how the technology will be used.
How does it do that? All they are doing is no longer using perceived masculinity as a metric for determining whether you might be male or female.
>I'll not bother making a fuss
And yet here we are
A two sentence post on HN is "making a fuss"???
I imagine the high level business use cases for these machine learning APIs are similar. For example, analyze shopping mall camera footage to figure out demographics shopping there and coordinate the (socially accepted and segregated) men/women department stores accordingly.
This is such an profitable use case I cynically can't see why they would cripple their API like this, regardless of their ethical stance.
I wish the adbuyers would flex that money muscle a bit and put google in line again.
Put it another way, how impressed would your boss be if you were to say "Yo, I've nailed this. I've got a kick ass algorithm, check this out. Oh, yeah, it fucks up 30% of the time."
Bias in AI is bad, but what makes it much much worse is arrogant attitudes claiming it doesn't exist.
And you know what, that speaks to the idiocy around AI more than anything. All these debates on AGI are moot, because people are already so keen to believe they got it all figured out it doesn't even matter if the actual tech is any good at all.
[0] https://www.wired.com/story/photo-algorithms-id-white-men-fi...
As is pronouncing the effects of bias and prejudice. You can almost guarantee the person is white without using any AI.
I was or still am pretty critical of AI since the quantification of every property of people will probably not be very enjoyable for anyone. What changed my mind a bit is that there are people wanting to employ it to improve reality. That is far worse and I hadn't had that on my radar before.