Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

A.I. Doesn't Get Black Twitter

inverse.com

Post reply on HN
Print view View original

71–80 of 105 posts

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

#71

Post by wyager »

gugagore wrote: post #33
wyager wrote: post #30

Earlier quoted context omitted.

> 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 lang…

You are only talking about supervised learning. And the bias people are probably talking about is bias in the data, not bias in the "input", as you put it. See http://arxiv.org/abs/1607.06520 for a counterexample to your point.

In that paper it sounds like the algorithm is finding true relationships that the author doesn't like. What's the problem with the algorithm?

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

#72

Post by wyager »

leftpad wrote: post #54
wyager wrote: post #30

Earlier quoted context omitted.

> 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 lang…

> If the output examples we are training on are true, the ML algorithm won't adopt any incorrect biases. This is rarely the case when working wtih real data, and thus inspecting whether our models are biased against protected classes is probably one of the most important things an ML practitioner should do.

We have a good understanding of the error profiles of most ML algorithms. We can put tight bounds on the difference between predictions and validation data. If ML algorithms make mistakes, it's usually due to noise or low-quality data, not the algorithm itself.

There is no reason an ML algorithm would be biased against a "protected class". It doesn't know what those are. It's possible that the algorithm will uncover a truth that you don't like, like that there as differences in risk profiles across race or gender, but that doesn't mean the algorithm is incorrectly biased. It just means that reality is at odds with how you might want it to be.

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

#73

Post by alphonsegaston »

morgante wrote: post #55
gordonguthrie wrote: post #51

Earlier quoted context omitted.

All language has rules - there is no language 'without formal rules'

What are the "formal rules" of AAVE?

Here's a good overview. The reference section at the end has a pretty clear delineation of these rules, as well as links to further reading.

http://s3.amazonaws.com/academia.edu.documents/41737546/The_...

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

#74

Post by sevenless »

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…

Enjoyed

> Some aspects of communication are likely to prove more challenging, Hinton predicted. “Irony is going to be hard to get,” he said. “You have to be master of the literal first. But then, Americans don’t get irony either. Computers are going to reach the level of Americans before Brits.”

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

#75

Post by leftpad »

wyager wrote: post #72
leftpad wrote: post #54

Earlier quoted context omitted.

> If the output examples we are training on are true, the ML algorithm won't adopt any incorrect biases. This is rarely the case when working wtih real data, and thus inspecting whether our models are biased against protected classes is probably one of the most important things an ML practitioner should do.

We have a good understanding of the error profiles of most ML algorithms. We can put tight bounds on the difference between predictions and validation data. If ML algorithms make mistakes, it's usually due to noise or low-quality data, not the algorithm itself. There is no reason an ML algorithm would be biased against a "protected class". It doesn't know what those are. It's possible that the algorithm will uncover…

Not talking about the algorithm. It's the data that are biased. The choice of algorithm just determines how interpretable those biases actually are.

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

#76

Post by morgante »

alphonsegaston wrote: post #73
morgante wrote: post #55

Earlier quoted context omitted.

What are the "formal rules" of AAVE?

Here's a good overview. The reference section at the end has a pretty clear delineation of these rules, as well as links to further reading. http://s3.amazonaws.com/academia.edu.documents/41737546/The_...

That link says access denied.

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

#77

Post by h4nkoslo »

Chinjut wrote: post #13
h4nkoslo wrote: post #7

It's interesting that they frame the problem as "woe be to the black users, the AI might discriminate against them". First, approximately all of Google's most valuable users (or any other ad-based platform) are non-black. Secondly, the problem is empirically the opposite. https://www.google.com/search?q=white+man+white+woman&biw=13... https://www.google.com/doodles/veterans-day-2015 http://www.cbc.ca/news/trending/go…

Er, how are those couple doodles examples of the empirically opposite problem? They have nothing to do with AI. (Also... is any representation of people of color an instance of anti-white bias? Because otherwise, I don't see what the problem is with those doodles.) Also: "approximately all of Google's most valuable users (or any other ad-based platform) are non-black"... "most valuable users"? Really? Putting aside w…

The problem opposite of artificial intelligence unintentionally erasing blacks is a company's built-in intelligences intentionally erasing whites, minimizing their achievements, and deconstructing their identities. Whites are not "allowed" to have achievements; they must be couched in terms of general social achievements which of course are (according to the doodles) aspirationally majority non-white, and ideally non-white and non-male.

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

#78

Post by h4nkoslo »

mseebach wrote: post #14
h4nkoslo wrote: post #7

It's interesting that they frame the problem as "woe be to the black users, the AI might discriminate against them". First, approximately all of Google's most valuable users (or any other ad-based platform) are non-black. Secondly, the problem is empirically the opposite. https://www.google.com/search?q=white+man+white+woman&biw=13... https://www.google.com/doodles/veterans-day-2015 http://www.cbc.ca/news/trending/go…

Google, probably more than any other company in the world, gets that their "most valuable users" aren't large coherent groups of any sort, Google wins by catering to everybody at once (aka "the long tail", to use a 2000s-ism). This is what drives their relentless focus on machine learning everything, because machine learning allows you to serve microscopic constituencies. You don't need machine learning to sell beer…

"You don't need machine learning to sell beer to white, suburban males."

This is both bigoted and empirically false. Do you imagine suburban whites to be a particularly unsophisticated demographic? Imagine saying "you don't need machine learning to sell fried chicken to urban blacks".

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

#79

Post by wyager »

leftpad wrote: post #75
wyager wrote: post #72

Earlier quoted context omitted.

We have a good understanding of the error profiles of most ML algorithms. We can put tight bounds on the difference between predictions and validation data. If ML algorithms make mistakes, it's usually due to noise or low-quality data, not the algorithm itself. There is no reason an ML algorithm would be biased against a "protected class". It doesn't know what those are. It's possible that the algorithm will uncover…

Not talking about the algorithm. It's the data that are biased. The choice of algorithm just determines how interpretable those biases actually are.

> It's the data that are biased.

Which data are you referring to? In most cases, the training data isn't human-generated, and if it is, we usually want to match human behavior as close as possible.

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

#80

Post by alphonsegaston »

morgante wrote: post #76
alphonsegaston wrote: post #73

Earlier quoted context omitted.

Here's a good overview. The reference section at the end has a pretty clear delineation of these rules, as well as links to further reading. http://s3.amazonaws.com/academia.edu.documents/41737546/The_...

That link says access denied.

Guess it went down. This one lacks the outlined breakdown at the end, but try the pdf link here:

http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.516....

EDIT:

If you want a really deep dive, you can also check out "African American English: A Linguistic Introduction" from Cambridge University press:

www.cambridge.org/us/academic/subjects/languages-linguistics/sociolinguistics/african-american-english-linguistic-introduction?format=PB&isbn=9780521891387

Post reply on HN