At least this is relatively innocuous. Until recently if you did a Google Image Search for "person" or "people", it only showed white men.
Is it surprising that very few of the result surprises me?
I did use images.google.se in order to tell google which country I wanted my bias from since that is the culture and demographics I am most familiar with. I also only looked at photos of a person and ignored emojis.
I have also seen here on HN links to websites that have captured screen shots of word association from google images and published them so you could click a word see the screen shot. They tend to follow the same line as above, but with some subtle differences, and I suspect that is the country culture being just a bit different to mine.
I just submitted all your searches to google.com from Australia, and the results were nothing like what you described; all the results were very diverse.
This is to be expected, as Google has been criticised for years for reinforcing stereotypes in image search results, and has gone to great effort to adjust the algorithms to reduce this effect.
But here, not that I think it will help: https://www.recompile.se/~belorn/happyvscriminal.png
First is happy person. Out of 20 we have 14 women, 4 guys, 2 children.
Second is criminal person. The contrast to the first image should be obvious enough that I don't need to type it.
If I type in "person" only I get the following persons in the first row in following order: Pierre Person (male) Greta Thunberg (female) Greta Thunberg (female) Unnamed man (male) Unnamed woman (female) Mark zuckerberg (male) Keanu Reeves (male) Greta Thunberg (female) Trump (male) Read Terry (male) Unnamed man (male) Greta Thunberg (female) Greta Thunberg (female) Unnamed woman (female) Unnamed woman (female)
Resulting in 8 pictures of females, 8 males, which I must say is very balanced (I don't care to take a screenshot, format and upload, so if you don't trust the result then don't).
Typing in doctor as someone suggested in a other thread I get in order (f=female, m=male): fffmffmmmmfmmfffmfmfmmmff
and Nurse: fffmffmfmmffmffmfffmffmffff
Interestingly the first 5 images have the same order of gender and are both primarily female, through doctor tend to equalize a bit more later while nurse tend to remain a bit more female dominated.
Your initial comment said "Happy person", women of color.
But your screenshot showed several white people, several men, and a diversity of ages. Yes, more women, which is probably reflective of the frequency of photos with that search term/description in stock photo libraries and articles/blog posts featuring them. No big deal.
You also said "Criminal person", Hispanic men
But the screenshot contains more photos of India's prime minister than it does of Hispanic men. In fact I can't see any obviously-Hispanic men, and the biggest category in that set seems to be white men (though some are ambiguous).
The doctor and nurse searches suggest Google is making some effort to de-bias the results against the stereotype.
To me the biggest takeaway is that image search results still aren't very good at all, for generic searches like this.
Indeed it's likely that they can't be, as it's so hard to discern the user's true intent (for something as broad as "happy person"), compared to something more specific like "roger federer" or "eiffel tower".
"Kind person" - pictures of men women, children, of all ages and colors.
"good person" - Mostly pictures of two hands holding. No clear bias towards women at all. If anything, more of the hands look "male".
"Bad person" - Nearly 100% cartoon characters
Absolutely ridiculous that you would take the time to write up such fake nonsense.
Following the stereotype content model theory I would likely get a pretty decent prediction of what kind of culture and group perspective produced the data. You could also rerun the experiment in different locations to see if it differ.
The net result of that Google search, combined with the "Shirt Without Stripes" repo, leaves me even more unimpressed with the capabilities of our AI overlords.
The next few images contained Donald Trump, Terry Crews, Bill Gates and a French politician named Pierre Person.
After that it was actually quite a varied mix of men/women and color/white people.
I am still not very impressed with Google's search engine in this aspect, but it is not biased in the way you suggest.
At least it is not biased that way for me. As far as I am aware, and I might be completely wrong here, Google, in part, bases its search results on your prior search history and other stored profile information. It is entirely possible that your search results say more about your online profile than about Google engine :)
Well, she was the 2019 Time Person of the Year.
Likewise, Trump was the 2016 choice, and Crews and Gates have been featured as part of a group Person of the Year (“The Silence Breakers” and “The Good Samaritans” respectively).
- If I entered "person" I'd see a mix of images substantially similar to what I saw using google.co.uk up to and including Terry Crews, which was frankly a little weird, and otherwise mostly white
- If I entered "人", which Google Translate reliably informs me is Japanese for "person", I'd see a few white faces, but a substantial majority of Japanese people
So it seems possible that Google's trying to be smart in showing me images that reflect the ethnic makeup I might expect based on my language and location. I mean, it's doing a pretty imperfect job of it (men are overrepresented, for one) but viewed charitably it's possible that's what's going on.
Is the case for woke outrage against Google Image Search overstated? Possibly; possibly not. After these experiments I honestly don't feel like I have enough data to come to a conclusion either way, although it does seem like they may at least be trying to do a half decent job.
The TL;DR of it is that google crawls the internet for photos, associates those photos with text content pulled from the caption or from the surrounding page, and gives them a popularity score based on the popularity of the page/image. There are some cleverer bits trying to label objects in the images, but it's primarily a reflection of how frequently that image is accessed and how well the text content on the page matches your query. There's some additional localization, anti-spam, and freshness rating that influences the results too.
The majority of pages with "人" and a photo on it that has a machine labeled person image would be a photo of a japanese/chinese person, and if you're being localized to japan with a vpn, that would be even more true.
Google doesn't "know" what you're trying to search. It's a giant pattern matching game that slices and dices and rearranges text to find the closest match.
I'm not disputing that, and it certainly explains why it's "good enough" for somes search queries whilst being totally gimpy for others.
My understanding was that Google does prioritise what it's classified as local search results though, on the basis that they're likely to be more relevant.
"Person without stripes" shows several zebras, tigers, a horse painted like a zebra, and a bunch of people with stripes.
Interestingly, duckduckgo shows me, as second result, an albino tiger with, you guessed it, no stripes. The page title has "[...] with NO stripes [...]" in it, so I assume that helped the algo a bit.
EDIT: I also got the painted horse (it looks spray-painted, if you ask me) and I must admit it's quite funny to look at
Unless things have really changed, [doctor] will be mostly white men and [nurse] will be mostly white and Filipino women.
But don't blame the AI. The AI has no morality. It simply reflects and amplifies the morality of the data it was given.
And in this case the data is the entirety of human knowledge that Google knows about.
So really you can't blame anyone but society for having such deeply engrained biases.
The question to ask is does the programmer of the AI have a moral obligation to change the answer, and if so, guided by whose morality?
Key point right there. Unless Google is deliberately injecting racial and/or gender bias into their code, which seems extremely far fetched (to put it kindly), the real fault lies with us humans and what we choose to publish on the web.
I would contend that society is biased. There is no evidence that says men are better doctors than women, and in fact what little this has been studied says that women make better doctors than men (and is reflected in the more recent med school graduation classes which are majority women).
So it's a question of what you are asking for when you search for [doctor]. Are you asking for a statistical sampling or are you asking for a set of exemplars?
> So statistically, it would be correct to return mostly male doctors in an image search.
And that's exactly it. The AI has no morality. It's doing exactly what it should, and is amplifying our existing biases.
IMO, wrapping it in a concept like "morality" because the pictures have people in them just serves to excuse the problem and obscure its (otherwise obvious) solution.
Any sort of image search is going to tend to be biased toward stock photos, because those images are well labeled, and often created to match things people search for.
Nurses it's 34 women to 5 men. Proportions of skin tones are what I'd expect to see in a city in my country.
You can blame statistics for that. Beyond that, you can blame genetics for slightly skewing the gender ratios of certain fields and human social behavior to amplify this gap to an extreme degree.
(That's how I would do it if I wanted more accurate rather than more general results.)
There's not much diversity, assuming Terry Crews is from USA, then all the first viewport full of images are Western people; except Ms Thunberg they're all from USA AFAICT [I'm in UK].
The first non-Western person would be a Polish dude called Andrzej Person (the second Person called Person in my list after a USA dancer/actress), then Xi Jinping a few lines down. The population in my UK city is such that about 5/30 of my kids primary and secondary school, respectively, classmates have recent Asian (Indian/Pakistani) heritage. So, relative to our population, there are more black people, far fewer Indian-subcontinent no obviously local people.
Interesting for me is there are no boys. I see girls, men and women of various ages but no boys. 7 viewports down there's an anonymous boy in an image for "national short person day". The only other boys in the top 10 pages are [sexual and violent] crime victims.
The adjectives with thumbnails across the top are interesting too - beautiful, fake, anime, attractive, kawaii are women; short, skinny, obese, big [a hugely obese person on a scooter], cute, business are men.
Most of the very top results seem to be of trump and greta thunberg.
If you were unfamiliar with them and searched "widgets" to find out more and got widgets of a single colour and form, it would not be an unreasonable assumption that widgets are mostly (if not entirely) that shape and colour, especially if there was nothing to indicate that this was a subset of potential widgets.
It's not so much "demand for diversity" as it is "more accurate and correct representation".
I never figured out what kind of mistake could have led to that.
Relatedly, one time I picked up a prescription for a cat. The cat's name was listed as CatFirstName MyLastName. They had another (human) client with that same first and name. It turned out that on my previous visit they had "corrected" that client's record to indicate that he was a cat.
If I search for 'person' it's a mixed-race woman, then a white woman (Greta Thurnberg), then a white man.
Many interpreted this along tribal lines, but likely it is that there is constant tuning and lots of complex constraints.
[1] not to say that you implied the reason was racism, but often it is attributed to something along those lines
Something of a corollary to Brooksian egg-manning: with an infinite number of possible searches, you can find at least one whose results do not exactly match the current demographics of the state from which you place the search.
The google image search you did -- did not provide incorrect answers, unlike the OP's