
A Horrible Experiment - marvindanig
https://mobile.twitter.com/bascule/status/1307440596668182528
======
kingosticks
There's a whole lot of random people chiming in and guessing on Twitter
(shocker) so it is worth pointing out that:

> The algorithm does not do face detection at all (it actually replaced a
> previous algorithm which did).

From
[https://twitter.com/ZehanWang/status/1307461285811032066?s=2...](https://twitter.com/ZehanWang/status/1307461285811032066?s=20)

Not to say there is or isn't a problem to be fixed, more work is needed to
understand. Such as the analysis at
[https://twitter.com/vinayprabhu/status/1307460502017028096?s...](https://twitter.com/vinayprabhu/status/1307460502017028096?s=20)

~~~
8fGTBjZxBcHq
Why is it worth pointing that out? Like you said, the problem is there either
way. What are lay people supposed to gain from hearing that the algorithm's
designers don't consider it to be based on face detection?

~~~
kingosticks
Because it has totally derailed many of the twitter threads. Thankfully we get
less conspiracy theories here but I figured we could try and avoid it
altogether.

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dazbradbury
[https://twitter.com/SergioSemJ/status/1307493041742254080?s=...](https://twitter.com/SergioSemJ/status/1307493041742254080?s=19)

One theory is it's actually the glasses increasing the likelihood of correctly
identifying a face (or "feature" element in the photo). Interesting looking
through the various tests in the thread, as clearly lots of variables here,
but no way this will be exhaustive.

Suspect this might kick off some serious arguments at Twitter HQ for allowing
users to pick the cropping vs. an algorithm regardless of any bias found one
way or not.

~~~
DanBC
Here's an example where neither person is wearing glasses.

[https://twitter.com/LOVEMUNY/status/1307789227535499266](https://twitter.com/LOVEMUNY/status/1307789227535499266)

It also includes an example where the black man is wearing glasses and the
white man isn't.

------
DanBC
See also Zoom backgrounds:
[https://twitter.com/colinmadland/status/1307111816250748933?...](https://twitter.com/colinmadland/status/1307111816250748933?s=20)

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TazeTSchnitzel
Wonderful example of the ethics of ML. The people whose eyes they tracked
gravitated towards certain kinds of faces (not necessarily just based on skin
colour), so now Twitter's model crops other faces out of pictures, among other
problems (now it likes cleavage apparently).

The old approach may have been a bit mechanical but it avoided the non-
neutrality of unconscious human perception. The assumption that what human
eyes gravitate to is what an image should be cropped to is a very big and
questionable one. The first thing I look at in a picture is not always the
most important part, and the image as a whole has value.

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noxer
The real problem I see here is the default assumption from many people that
these is racial bias in the algorithm with no evidence thereof whatsoever.
Thats how twitter works, shitstorm first, research later, evidence or
corrections for false assumptions - never.

~~~
svrb
It's also how HN works. In a comment far below there's a link to a decent
controlled experiment completely debunking this claim (outcome using
controlled dataset: 40 white to 52 black faces chosen). But that comment is
heavily downvoted.

[https://twitter.com/vinayprabhu/status/1307497736191635458?s...](https://twitter.com/vinayprabhu/status/1307497736191635458?s=19)

~~~
noxer
People want it to be true. Instead they should want to know if its true.

~~~
svrb
> People want it to be true.

This is the really disturbing thing. Why would someone _want_ Twitter to have
race-based image cropping? And yet the behavior of many in the various threads
is precisely as if they did! I think it may actually make them happier if it's
true. Who are the real racists, again? It's hard to tell sometimes.

And after all, doesn't the Twitter OP acknowledge this inevitable result when
calling it a "horrible" experiment?

~~~
noxer
>Why would someone want Twitter to have race-based image cropping?

-Victim mentality -Virtue signaling -Having a "proof" for a common enemy (twitter, social media , big evil corp etc. etc.)

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fr2null
Interestingly, swapping the glasses does seem to make the thumbnail Obama. [1]

1\.
[https://twitter.com/SergioSemJ/status/1307493041742254080?s=...](https://twitter.com/SergioSemJ/status/1307493041742254080?s=19)

~~~
nichos
So does the smile:
[https://twitter.com/MASCARPOWN/status/1307454405059452928?s=...](https://twitter.com/MASCARPOWN/status/1307454405059452928?s=09)

And actually swapping the smile:

[https://twitter.com/DrWhen2/status/1307497434872905728?s=19](https://twitter.com/DrWhen2/status/1307497434872905728?s=19)

Twitter is nothing if not full of victims and people talking about things they
know nothing about.

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greenduck
This is pretty egregious.

For those of us working on machine learning, does anybody know of datasets
that have decent representation of different colors of skin. That seems like
the cause here and thinking back, my datasets aren't the most diverse out
there.

~~~
TazeTSchnitzel
ML inherits the biases of the data it is trained on. If you want “unbiased” ML
models, you must train them on data that is carefully constructed to avoid the
biases deemed undesirable.

~~~
GuB-42
I remember I took an introduction class in machine learning when I was a
student.

I was surprised how important bias removal was. Preparing the dataset was more
than half of the work. And the class was more about statistics than anything
else.

It wasn't about racial bias, ethics, or anything like that. More like if you
feed too many "5" in your handwriting recognition algorithm, it will will
start seeing 5 everywhere.

~~~
disgruntledphd2
Preparing the dataset is 99.9% of pretty much all ML and data science.

Just like all software engineering is maintenance, all DS is data cleaning.

------
pityJuke
Some more info:

\- This apparently done via a NN [1]

\- There are mixed results, with various factors playing a factor [2].

Worth digging into this thread, and the original thread where this was figured
out. It's intriguing.

[1]:
[https://blog.twitter.com/engineering/en_us/topics/infrastruc...](https://blog.twitter.com/engineering/en_us/topics/infrastructure/2018/Smart-
Auto-Cropping-of-Images.html)

[2]:
[https://mobile.twitter.com/NotAFile/status/13073372942491033...](https://mobile.twitter.com/NotAFile/status/1307337294249103361)

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t0astbread
Why does Twitter even try so hard to make preview crops "meaningful"? Sure, it
produces interesting results sometimes (sometimes funny, sometimes less so)
but isn't it a lot of work for not a lot of benefit?

~~~
matthewmacleod
Cropping well is quite a hard problem!

I know it seems on the surface like “just resize it” is a good approach, but
it’s not super uncommon to see quite extreme aspect images, and even for
normal use there are lots of different constraints in different systems that
mean a really simple system can produce undesirable results.

I used to get this all the time back when I built custom content management
systems. Users would upload images which were automatically cropped into
various sizes for use in particular bits of content; really long or wide
images would look awful when scaled, and automatic fixed-size crops would
often end up cutting out the actual interesting bit of the image. That’s what
various “automatically generate interesting thumbnails” algorithms try to
avoid.

In the end for me the best tool was auto-crop-with-preview-and-manual-
override, and I’m beyond infuriated every time I try to upload an image to
Twitter and it doesn’t allow this obvious and simple way to avoid the issue.

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nailer
Misleading - it seems to be looking for contrast

[https://twitter.com/kim/status/1307548258491801600?s=19](https://twitter.com/kim/status/1307548258491801600?s=19)

UPDATE: someone wrote a boy to test the conspiracy and properly debunked it,
source code is online:

[https://twitter.com/vinayprabhu/status/1307497736191635458?s...](https://twitter.com/vinayprabhu/status/1307497736191635458?s=19)

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daenz
Isn't Mitch McConnell in the news right now far more "intensely" than Obama?
If I was writing a system to pick a thumbnail, I would pick the thumbnail that
is most relevant to current tweets, which is definitely McConnell.

A quick way to test this would be to use two well known figures, where the PoC
is definitely more in the news than the other.

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dmitriid
It's funny how these big companies willingly convert a non-problem into
massive problems with hundreds of hours of engineering effort and huge social
implications.

Non-problem: don't mess with the images, and display them as is.

Problem: see Twitter

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rambojazz
I'm out of the loop. What's happening here? All I see is a picture.

~~~
s_gourichon
Glad you asked. There are two pictures in each tweet, shown left and right.

You can click on each to see the full picture.

Each picture is actually two portraits (Mitch McConnell and Barack Obama), one
above one below, separated by a big gap.

Since apparently Twitter automatically chooses to crop pictures in default
tweet display, the OP posted different cases to see where Twitter would crop
the image. And it happens that Twitter chose one of the portrait always, until
the OP reversed color. Some people wonder if it is because Barack Obama is
black. Whether that's the reason or not, the observable fact remains.

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Gys
It seems this needs a twitter account to see? Maybe someone can post a
screenshot?

~~~
acqq
And for those of us who need a textual description to get it, maybe someone
can post an explanation or description of what is expected and what's an
observed behavior?

Anybody, please?

~~~
yc-kraln
Twitter has an algorithm to pick which part of oblong images to showcase in
the feed. People have been posting examples where, given the "choice" between
a white person and a person of color, the white person is always picked,
regardless of orientation or where they are in the picture.

This was noticed in a thread about how Zoom's background detection seems to
not work great for people of color, and how the WebEx cross-talk protection
seems to favor men over women.

------
Kednicma
Humans can't make unbiased computers. Every computer which humans make is
indelibly human as well. This is the anthropic bias.

~~~
noxer
It's not a biased if isn't a result of a human bias. If the algo picks the
spot with the highest contrast for example, that can not be biased it may lead
to unwanted results but its not biased.

~~~
Aeronwen
Someone decided the algo was producing usable results when it was picking the
highest contrast areas.

~~~
noxer
Because it did. I'm sure they made tests and decided it worked good enough. It
wasn't made only for faces/persons especially not for images with 2 faced far
apart. It also never was meant to decide between 2 possibly "image centers".
Its not biased because you can craft an image where it procedure unwanted
results. The only persons biased are the people who see a bias in everything.

