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A.I. Doesn't Get Black Twitter

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Re: A.I. Doesn't Get Black Twitter

#21

Post by ralfd »

a3n wrote: post #16
wRastel27 wrote: post #15

Is this a result of the AI being written by a bunch of non-black people? It seems to me that this problem is more of a reflection of the people working on the code.

There may be some of that, but I think it has more to do with AI still being young, and it's easier to work with less ambiguous text for learning. That and everyday speech move much faster than formal writing. Now that my son has gone off to college and is less accessible as a resource, I expect to almost totally lose touch with the speech of "these kids today."

> I expect to almost totally lose touch with the speech of "these kids today."

Is this long term really a problem though? Regression to the mean: No one cares about the specific slang of the 60s anymore. And the hip slang of today will be out of fashion in 15 years.

In a similar vain the extreme T9 keyboard text message abbreviations of the youth in the 2000s did go out of fashion because of automatic spell checking and speech recognition of modern smartphones.

Re: A.I. Doesn't Get Black Twitter

#22

Post by pjc50 »

aorth wrote: post #8

I bet it doesn't get Scottish Twitter either. I saw this the other day and thought it was pretty funny: https://mobile.twitter.com/MarkHamiIl/status/778141129564905...

Obligatory Burnistoun: https://www.youtube.com/watch?v=sAz_UvnUeuU

I thought most people would be able to get written Scots dialect, but I did struggle with Walter Scott's Rob Roy where one of the characters speaks it phonetically.

There's also a guy doing a newspaper column in Scots in The National, causing quite a bit of controversy.

Spoken language is another matter. I've had to interpret between an Ulsterman and an Afrikaaner both of whom were nominally speaking English as a first language.

Re: A.I. Doesn't Get Black Twitter

#24

Post by ams6110 »

panarky wrote: post #10
bmmayer1 wrote: post #6

Earlier quoted context omitted.

Especially as it relates to this: "This means that blogs or websites that employ African-American language could actually be pushed down in search results because of Google’s language processing." Isn't the ideal system to not assume anything about the content it's analyzing, but to base the model on behavioral patterns instead? In the case of Google ranking, either a site has traffic/low bounce/backlinks/social cred…

> Isn't the ideal system to not assume anything about the content it's analyzing ... Because Google no longer just searches for literal strings of text, ranked by links. They now infer your meaning and find results that match your intent -- even if the exact search string doesn't exist in the search results. For more on this, read about Hinton's work on "thought vectors" [1]. So if Google can't determine the meaning…

I can't say I would think that Google does a particularly good job at interpreting the intent of my search queries. I often need to reformulate them to get what I'm after. But I doubt that my searches are similar to those of the general public so that is understandable.

Re: A.I. Doesn't Get Black Twitter

#25

Post by TazeTSchnitzel »

bmmayer1 wrote: post #6
panarky wrote: post #5

This is an interesting ethical challenge. If the training corpus for a machine learning model contains stereotypes and biases, then the output of the model will reflect those prejudices. When that model is used in large-scale applications, it will not just repeat those biases, it will amplify them. It propagates biased language, which feeds back into the model in a self-reinforcing vicious cycle of increasing bias. S…

Especially as it relates to this: "This means that blogs or websites that employ African-American language could actually be pushed down in search results because of Google’s language processing." Isn't the ideal system to not assume anything about the content it's analyzing, but to base the model on behavioral patterns instead? In the case of Google ranking, either a site has traffic/low bounce/backlinks/social cred…

Showing only (or prioritising) same-language results helps with relevancy. A German speaker who searches for “gift” is not looking for ideas for presents, and an Italian speaker who searches for “peperoni” is not looking for the American variant of salami.

A further problem, though, is that search engines try to understand the query and the content searched at a deeper level than mere keyword matches. For example, they may try to pick up synonyms (if I search for “movies in ”, perhaps results for “films” might be helpful too) and associations.

Re: A.I. Doesn't Get Black Twitter

#26

Post by Kapura »

The best part about black twitter is how dynamic and innovative it is. There is a certain playfulness with language which is frowned upon in many academic settings & especially by linguistic prescriptivists, but healthy languages change over time.

And "more diverse datasets" isn't enough. I get that you need to train an AI on a set of old data, but any truly good NLP would be able to adapt to new terms and phrases as they're invented. Not saying it's an easy problem to solve, but many people I've spoken to don't seem to even be aware that language recognition is necessarily a moving target.

Re: A.I. Doesn't Get Black Twitter

#28

Post by TazeTSchnitzel »

wRastel27 wrote: post #15

Is this a result of the AI being written by a bunch of non-black people? It seems to me that this problem is more of a reflection of the people working on the code.

Partly that, but my guess would be that it's because it's largely trained from written English (not the sort you see on Twitter and in SMS), and written English is usually one of the standard varieties, and the standard varieties bear little resemblance to AAVE.

I would guess the same problem occurs with other dialects which are sufficiently different from the standard.

Re: A.I. Doesn't Get Black Twitter

#29

Post by tedunangst »

mattdeboard wrote: post #4
xiaoma wrote: post #3

"Approximately 8 of the 319 million people in the United States read the Wall Street Journal, about 2 percent of the population. If you look at the language — standardized English — being fed into many natural language processing units, it’s based on the language of that 2 percent. " It's hard to take an article seriously when it opens with a logical fallacy. Yes, the WSJ uses standard English and only 8 million peop…

Nah. It'd be wrong in the way you suggest if they said it's the language of only that 2%, I think. It IS "the" language of that two million. Just not ONLY those two million.

"I read the Wall Street Journal, about 0.00000031 percent of the population. If you look at the language — standardized English — being fed into many natural language processing units, it’s based on the language of that 0.00000031 percent."

Equally true, right? But somehow it seems just a little misleading.

Re: A.I. Doesn't Get Black Twitter

#30

Post by wyager »

panarky wrote: post #5

This is an interesting ethical challenge. If the training corpus for a machine learning model contains stereotypes and biases, then the output of the model will reflect those prejudices. When that model is used in large-scale applications, it will not just repeat those biases, it will amplify them. It propagates biased language, which feeds back into the model in a self-reinforcing vicious cycle of increasing bias. S…

> If the training corpus for a machine learning model contains stereotypes and biases, then the output of the model will reflect those prejudices.

Depends what you mean by "the corpus". Let's take the example of loan applications. Maybe the training data contains credit scores, biographical information, and personal essays about the applicants from loan officers.

Let's say loan officers tend to use more negative language when talking about black people.

Any decent machine learning system given racial data will not adopt those biases. In fact, it will counteract them. It will determine that e.g. the semantic content of the essays is an indicator of loan suitability, and it will also notice if it has to e.g. add something to the essay score for black people to make the best decisions.

As long as the output data is objective, competent ML systems will account for any bias in the input data.

> Should we attempt to detect prejudices in machine learning models

We don't have to. If the output examples we are training on are true, the ML algorithm won't adopt any incorrect biases.

This is literally the point of machine learning. You want to make the best possible decision. If your algorithm has incorrect human biases, you're losing money. ML naturally accounts for this stuff.

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