Almost like algorithms trained on biased data will exhibit bias, no?

Basically, the algorithm doesn’t suck.  You suck.

I’ve changed my mind on this.  You suck, but the algorithm sucks, too.

Think about is this way: take image classification, particularly facial recognition—all pixels have to be converted to numbers for the computer to use.  Seems reasonable to convert color values to something like RGB, right?  But, the RGB for a white pixel is (255,255,255) while RGB for a black pixel is (0,0,0).  Lighter pixels get higher values than darker pixels.  What’s a picture of a black face made of, mostly?  Darker pixels and hence lesser numerical value (take this instance of “lesser” to be purely in the numerical sense here, and not as a value judgment, but it does matter to the value judgment later on).

If your algorithm uses something like average pooling (basically, take the average color value of a small patch of the image, like a 3x3 square), those darker areas contribute less overall numerical value to the data that gets propagated through the algorithm.

Therefore, an algorithm that makes some typical default assumptions like “convert pixels to RGB” and “use average pooling” will take a darker-skinned face as input and see a bunch of vectors of lesser overall magnitude that make it difficult for the algorithm to pick up key differences that allow it to place this instance into one region of a partitioned dataset (aka, it makes it harder to categorize because it may not think there’s much there—it’s seeing a lot of things that appear to be close to 0, and 0 is basically the absence of information in many cases).  So it may not be able to differentiate a black man from a black woman very easily.  Or worse, it may not even see a person there at all, and in that case even if “not a person” isn't one of it’s possible category labels, the output is going to be kind of random, because the algorithm as structured can’t make sense of an image that it’s presented with.

These assumptions and algorithmic choices don’t exist in a vacuum.  “Numbers” themselves aren’t biased, of course, but the way you use them and put them together can cause systems to behave in a biased fashion.

I will be starting a natural language processing lab in a computer science department in the fall, but my approach to NLP has always been firmly grounded in linguistics, society and culture, and a strong interest in dialogue, narrative, and literature.  I hope my lab can be notable in this regard and help keep the humanities “cool” in the eyes of the STEM cohorts.

I’m glad eduroam is such a paneuropean experience 😂

Panworld experience apparently

some of you have never cried over a verb conjugation table and it shows

Metal with throat singing? Yes please!

AI Winter

GUYS THIS IS AMAZING

SERIOUSLY

6000 YEARS

STORIES THAT ARE OLDER THAN CIVILIZATIONS

STORIES THAT WERE TOLD BY PEOPLE SPEAKING LANGUAGES WE NO LONGER KNOW

STORIES TOLD BY PEOPLE LOST TO THE VOID OF TIME

STORIES

GUYS LOOK AT THIS

OH MY GOD YOU GUYS

GUYYYYYSSSS

“Here’s how it worked: Fairy tales are transmitted through language, and the shoots and branches of the Indo-European language tree are well-defined, so the scientists could trace a tale’s history back up the tree—and thus back in time. If both Slavic languages and Celtic languages had a version of Jack and the Beanstalk (and the analysis revealed they might), for example, chances are the story can be traced back to the “last common ancestor.” That would be the Proto-Western-Indo-Europeans from whom both lineages split at least 6800 years ago. The approach mirrors how an evolutionary biologist might conclude that two species came from a common ancestor if their genes both contain the same mutation not found in other modern animals.” 

How do they control for stories that were borrowed, which almost certainly happened?

“ Unlike genes, which are almost exclusively transmitted “vertically”—from parent to offspring—fairy tales can also spread horizontally when one culture intermingles with another. Accordingly, much of the authors’ study focuses on recognizing and removing tales that seem to have spread horizontally. When the pruning was done, the team was left with a total of 76 fairy tales.”

This article doesn’t say how, but I bet those methods are in the paper.

For this, they used a library of cultural traits for each culture a fairy tale occurred in, and then measured the likelihood that trait t occurs in culture c due to either phylogenetic proximity (inheritance) or spatial proximity (diffusion), using autologistic regression:

(Autologistic regression is a graphical model where connected nodes have dependencies on each other, except instead of an undirected graph, ALR is a special case that requires sequential binary data and assumes a spatial ordering.  In this case, the binary data are the cultural features).

Cultural traits states are generated using Monte-Carlo simulation and phylogenetic or spatial influence are fitted as local dependencies between the nodes in the graph representing cultural traits.  I can’t find this in the paper (though it may be mentioned in the citation of the method they used), but presumably if the spatial influence exceeds the phylogenetic influence by a certain threshold, the trait is removed.

The full paper is here.

[source]

Fuck my dash is about to blow up again.  OK, folks, let’s get this thing to more notes than there were KIAs/MIAs in WW1 (~8 million)

Hey Tumblr, want to escape this dying hole of a website by reading a book? Well, I wrote one with @thoughtlibrarian! Above is a blurry picture of me looking high and smug while holding it.

The Pointless Rules of English and How to Follow Them is about English grammar rules and how they’re all made up and mostly classist nonsense. We talk about why they exist, which ones you probably do have to follow in a formal context and how you can remember them.

ADDITIONALLY, we also have a big chunk on better writing, including a whole dang section on how to write inclusively, aka, How To Not Be A Dick With Your Words. We touch on how to write about:

… as well as your regular old writing advice about audience, tone, how to not get dragged into an internet argument with a boring Reddit user, etc.

Scattered throughout are language facts that we just think are interesting, such as this one:

I made an awful YouTube video which explains a little more about the book if you’re interested! It’s available on Amazon (US version here), or can be ordered through your local bookstore of choice! 💕

Petition to change the German word for “octopus” to “achtopus”

I know precisely where this is, I grew up in this town.

Here’s StreetView: https://www.google.com/maps/@36.7239185,-108.1879251,3a,75y,179.47h,81.85t/data=!3m6!1e1!3m4!1s4kp6mmFA7vvXO6x6YhNl0Q!2e0!7i13312!8i6656

Here are some facts:

1) The store was there 1st and the sign came later

2) About 200 ft out of frame to the right there is an elementary school—I used to have basketball practice there

3) This went viral after appearing on America’s Funniest Videos—it is probably the most fame Farmington, NM has ever acquired

4) As far as I know, the sign is still there—I haven’t been back to that part of town in a number of years but maybe I can go check it in May

…

…

This is when STEM bros need more social scientists *cough* Anthropologists *cough* on their teams.

Again, ML algorithms are trained on data from humans.  If there is bias in the data (and there is), humans may not be able to spot it, but you can bet a deep learning algorithm will spot it and be raring to reproduce it.  These algorithms are really good at learning what we teach them, and we’re teaching them racism

Thinking about embracing Tumblr’s bold new policy against pretty much all images/representations of the human form and becoming a hardcore iconoclast.

Tumblr, brought to you by Byzantine emperor Leo III the Isaurian.

Tumblr, motivated by the military successes of the Umayyad Caliphate as evidence of God’s divine ordainment to strict Second Commandment fundamentalism

The death of Tumblr. (2018)