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Comment by jedberg | Hacker News Reader

Image cropping algorithms are hard. When we made our first one for reddit, it used this algorithm:

Find the larger dimension of the image. Remove either the first or last row/column of pixels, based on which had less entropy. Keep repeating until the image was a square.

The most notable "bias" of this algorithm was the male gaze problem identified in the article. Women's breasts tended to have more entropy than their face, so the algorithm focused on that since it was optimized for entropy. To solve the problem, we added software that allowed the user to choose their thumbnail, but not a lot of users used it or even realized they could.

I assume they've since upgraded it to use more AI with actual face detection and so on, but at the time, doing face detection on every image was computational infeasible.

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Breasts shouldn't have more entropy than face. Perhaps the reason is due to the breasts being in the middle of the picture, so the face gets being compared to bottom rows more frequently?

Why not? Shirts might have flashy patterns, differently colored fabrics, alternating skin and shirt. On a row-per-row basis I can see the chest area being more entropic than a face with an even skin tone.

edit: I googled "woman" and selected random pictures which showed the whole upper body, entropy summed over each row to the right: https://imgur.com/a/oVB57gu

Seconded. I'm picturing my wife's choices in clothing--I would say often the highest entropy will be her top. And depending on the resolution and how good the entropy measurement is I can easily see her pants coming in second. (She tends to favor repeating patterns.)

(She has no work dress code she needs to conform to. Rarely does she wear a top as plain as any of your examples.)

Reddit's image cropping algorithm is hilariously bad. As is their video player, and their ads, and their ranking, and their messaging tools...

And even just trying to access a post/thread on mobile. I already clicked the link, then I have to click it once more to say “yes I want to do the browser thing I explicitly did”, then another time still to actually show more than half a screen of content.

And their search, and their new design. Pretty much everything about it sucks. They got lucky Digg screwed themselves over.

How is entropy related to "male gaze". This approach seems to be unsupervised, I don't see the problem.

How is entropy defined in this context?

Clearly there is human-derived input in the system (otherwise... What's the point just crop randomly)

Randomness in pixel values

Here is the code:

https://github.com/reddit-archive/reddit/blob/753b17407e9a9d...

But in short, it's a histogram of the values of the pixels.

"Entropy" in this context also left me wondering. Perhaps "variance" or "deviation from the mean"?

Thanks for the insights!

Entropy usually boils down to "sum p * log(p) for all p (and possibly normalize)", assuming you have discrete probabilities p. It is not related to variance or mean.

Which is not really image entropy at all, as it totally neglects spatial structure. You could sort an image and get the same entropy using the histogram approach.

I don't think the claim is that the behaviour is caused by "male gaze", but rather that the outcome of always focusing the cropping around any visible cleavage is functionally identical.

Whether or not it's unsupervised, whether or not it's sexist, it seems that a thumbnail focusing on a person's face rather than their breasts is typically going to be more desirable. Depending on context, of course.

> This approach seems to be unsupervised, I don't see the problem.

Someone wrote and tested this algorithm, and either:

a) didn't test it on pictures of women, or,

b) didn't notice that it cropped breasts rather than faces, or,

c) didn't think that was a problem.

If they had noticed and cared, this wouldn't be the approach in use.

It sounds like it was blasted out in an afternoon.

Maybe they weren't thinking about boobies a whole lot, tried like 5 random test images and shipped it?

Presumably there are a ton of failure modes for that algorithm, why get so moralistic and high-horse about just one?

> why get so moralistic and high-horse about just one?

Not doing so; just observing the facts.

If a proposed 'unsupervised' algorithm of this simplicity highlighted women's faces perfectly, but zoomed in on men's receding hairlines, it wouldn't have made it past the drawing board. Indeed, it's reasonable to believe that nobody would have noticed that it consistently worked for women. We certainly wouldn't know that this algorithm existed or be talking about it here.

We observe a bias in what is considered important to check before shipping.

That's an AWFUL lot of assumptions, there. You've constructed an entire complex narrative around something where all we know is that it's very simplistic.

I don't think this is a complex narrative. It's the reality of development:

The simplest case is just to pick the most central square, and then you could probably improve that by picking a standard square according the rule of thirds. Those are the naive algorithms - this choice of alternative algorithm is deliberate and isn't as naive or simplistic as you're claiming.

The algorithm is only considered useful because it appears to do better than that, on whatever examples that the developers tried (i.e. there was a business case for using it), and against other possible code.

Including, likely, pictures of their own selves. That's certainly what I would test it on, until it vaguely worked.

What 'awful lot of assumptions' do you think I am making? I don't imagine we are in disagreement about this.

Blasting out an ML algorithm in an afternoon that causes millions of people to see wildly different representations of people depending on their race or gender seems, maybe, like a bad thing you wouldn’t want to defend?

Sure, more testing would be better, but nobody cares how many landscape shots it messed up, right?

Years ago reddit was, IIRC, not very staffed at all compared to their traffic. It's a pretty privileged take to say they should have done expensive QA entirely around your particular things that you care about.

But that’s the problem. The entire premise of these algorithms is based around what you (or the developers producing it) care about. It’s the common thread between image crops preferring white faces and women’s breasts, and automated cars preferring dead black pedestrians over vehicle collisions.

If you don’t have the capacity to use new technologies without increasing harm, maybe you don’t have the capacity to use them.

No, I'm positing the premise that it was shipped with an absolute minimum of QA that didn't approach the level of trying to build 'inclusive' reference sets, on the cheap. It wasn't about caring in terms of priority of what was tested, it was about NOT caring that much in any direction and shipping it.

And it was a naive image cropping algorithm, years ago, and not making use of any sophisticated 'new technologies'. The beauty of the algorithm is that it was a simple function that could have been written in 1975 and required no training, deep learning or any of that. If you want to talk self-driving cars, you've got a much more relevant measure of harm and I'm right there with you.

As it is, I'd say there's a disconnect between where you and those years-ago shoestring developers stand on Maslow's hierarchy of needs. They were being scrappy with limited resources, and you're mad at them for not having an amount of QA that would have seemed unbelievable to them under their resource constraints.

> No, I'm positing the premise that it was shipped with an absolute minimum of QA that didn't approach the level of trying to build 'inclusive' reference sets, on the cheap. It wasn't about caring in terms of priority of what was tested, it was about NOT caring that much in any direction and shipping it.

Right. I’m saying that not caring is the problem. We mostly agree on the facts. I’m objecting to not caring as an acceptable position.

We’re not talking about shoestring developers. We’re talking about platforms that millions of people including heads of state use to affect billions of people’s lives.

> We’re not talking about shoestring developers. We’re talking about platforms that millions of people including heads of state use to affect billions of people’s lives.

Jedberg was >5 years ago, maybe closer to 10 for this algorithm. They were relatively shoestring compared to now, and especially compared to current-day FAANG.

Well I don't see any sense continuing this discussion, since you just keep refusing to engage with the harm done by doing something without caring about its impact.

It's an incredibly privileged definition of 'harm done'. They had less budget and manpower than you wish they had, and their shitty off-brand cropping algo occasionally had too much titty. If that's the level of harm that registers with you, you're clearly doing very well.

I’m gonna regret responding after saying I was removing myself but I’m hoping this will help you or at least other people reading understand what the harm is.

I’m not objecting to an algorithm that exposes me to more cleavage. I couldn’t care less. I’m objecting to an algorithm that exposes millions of men to a subtle but routine equation of men with faces, and women with breasts. It’s not a privilege for women to expect not to be casually sexualized because someone thought their algorithm was helpful but didn’t bother to find out it wasn’t.

Likewise the impact of racial bias. I’m not offended by seeing white faces. But it’s not a privileged position for POC to object to being basically erased from images because whoever developed the algorithm didn’t bother acknowledging more than one skin tone exists.

These things have real impact, not just on how it affects the people who are directly underrepresented. It also affects how the people who are overrepresented perceive and ultimately act toward them.

This sort of discussion makes me think of a book I once read by someone who was raised in a fundamentalist Christian sect, maybe Pentecostalism? Anyway, they described how, in their upbringing, the devil and his temptations was so omnipresent, that it was almost as if he was more powerful than God. Whenever you took your mind off the dangers of sin, the devil would come in and take control, one was continually warned. Eventually, this created a kind of cognitive dissonance, because God was remote but the devil was always by one's side, whereby they ended up losing their faith and becoming atheistic.

If we are aware of a negative principle that infests and invades every part of life, though we do our utmost to repel it and mitigate it and remediate it, how can we maintain the conviction that it is not the natural order of things, but is a product of solvable human faults?

I'm not denying the importance of anti-racism or of examples of bias as mentioned here; my point is that when faced with an all pervasive adversary, whether a personified devil or something more abstract, human minds tend to find relief in submitting to the seemingly inevitable via some rationalization.

Machine learning? It’s not in my book.

Nobody is trying to assign blame to the person who wrote the algorithm, it's just being pointed out that the the output it produces is sub optimal in this specific way.

I used a similar entropy-based image cropper in 2009 and saw a slightly different result – it had a foot fetish.

Suffice to say the twitter algorithm fails badly with NSFW images (where often the focus is ... not face)

Except the article said that turned out not to be a factor.

> Women's breasts tended to have more entropy than their face

Aha, perhaps that's the problem then.

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