9 ms·

A.I. Doesn't Get Black Twitter

90 points by jnordwick 10y ago

kriaq 10y ago
Exactly why googles filtering system will fail
xiaoma 10y ago
"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 people read (subscribe to?) it. That does not mean that only 8 million people in the US use so-called standard English. There are also a lot of people who don't subscribe to that particular publication but still speak or write in "standard" English and that group is much larger than the group of WSJ readers.
- mattdeboard 10y ago
  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.
  - lqdc13 10y ago
    Then why bring up WSJ at all? What about all the other papers? and what about all the other sources of training data for NLP research? WSJ dataset is not the only such dataset. NLP is not very good with standard English yet and usually doesn't generalize from topic to topic. Dialects and other languages - especially those without formal rules - will come when we can deal with standard English.
    - gordonguthrie 10y ago
      All language has rules - there is no language 'without formal rules'
      - morgante 10y ago
        What are the "formal rules" of AAVE?
        thaumasiotes 10y ago
        https://en.wikipedia.org/wiki/African_American_Vernacular_English#References https://en.wikipedia.org/wiki/African_American_Vernacular_En...
        morgante 10y ago
        I'm looking for formal rules. I'm not saying AAVE doesn't exist, I'm saying its rules (such as they are) are not formalized. Tons of rule books exist for Standard English and part of learning it is to memorize the correct rules. I don't believe any formal equivalent exists for AAVE.
        iamdave 10y ago
        What, then, are your parameters for formal? It has to be in a book, codified and signed off on? If so, aren't these kind of antithetical to AAVE on the face of it?
        morgante 10y ago
        > It has to be in a book, codified and signed off on? The point is for rules to be formal then the must be formalized somehow and codified. This could be online or in a book, but the point is that there must be some clear delineation between when the rules are followed and when they are broken. Otherwise what's the meaning of "formal" rules? > If so, aren't these kind of antithetical to AAVE on the face of it? Yes. My argument is that AAVE, almost by definition, is an informal dialect without formalized rules. This is getting rather off topic, but I think it relates to the original point of why NLP might start with Standard English even if you are not biased. A large corpus of Standard English text (such as from the WSJ) will generally be very internally consistent precisely because it follows a set of formal rules codified into a style guide. As there is no such equivalent for AAVE, even gathering a large and internally consistent corpus of AAVE text seems prohibitively difficult. That being said, I do hope researchers are working on gathering text from Twitter to build up new training sets.
  - tedunangst 10y ago
    "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.
  - Dylan16807 10y ago
    You can read the WSJ without going about your everyday life forming sentences the same way. It's a terrible claim no matter how you interpret it.
- ubernostrum 10y ago
  It's hard to take a poster seriously when they show willful ignorance of the way language serves as a marker of class, ethnicity and race, and instead have to manufacture quibbles to try to discredit an article that might cause slightly less comfort with the notion of the Glorious AI Future™ -- Coming Soon™!
  - Dylan16807 10y ago
    I think your post is the most aggressive instance of putting strawman words in someone else's mouth I've seen all month.
- briholt 10y ago
  > When researchers evaluated their model against natural language processing tools...researchers found that the software flagged African-American English as “not English” at a much higher frequency than standard English. Likewise, notice the deceptive switch between "English" and "standard English." So basically language bots trained to identify standard English are correctly identifying non-standard English dialects as not standard English.
panarky 10y ago
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. Should we attempt to detect prejudices in machine learning models and actively counter them, creating a self-reinforcing virtuous cycle of balance and tolerance? ---- As an aside, for an article about language, this one has a lot of typos. Doesn't anyone proofread before posting?
- bmmayer1 10y ago
  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 or it doesn't--why is Google attempting to 'read' and analyze the content itself? (Other than to serve ads of course)
  - deleted 10y ago
    [deleted]
  - panarky 10y ago
    > 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 of African American language as well as it can standard English, then that content is likely to be less visible in search results. [1] https://www.theguardian.com/science/2015/may/21/google-a-step-closer-to-developing-machines-with-human-like-intelligence https://www.theguardian.com/science/2015/may/21/google-a-ste...
h4nkoslo 10y ago
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=1329&bih=706&source=lnms&tbm=isch&sa=X&sqi=2&ved=0ahUKEwiX4MjizqjPAhVX5mMKHQ9oCQEQ_AUIBigB https://www.google.com/search?q=white+man+white+woman&biw=13... https://www.google.com/doodles/veterans-day-2015 https://www.google.com/doodles/veterans-day-2015 http://www.cbc.ca/news/trending/google-doodle-juno-reaches-jupiter-8-bit-nasa-scientists-1.3664827 http://www.cbc.ca/news/trending/google-doodle-juno-reaches-j...
- Chinjut 10y ago
  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 whether that's even an accurate assessment in the financial terms in which it was presumably intended: Do ethics stop at treating well only the people with most profit potential? Is impact on business our only arbiter?
  - h4nkoslo 10y ago
    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.
    - dang 10y ago
      Let's not have race rants on HN, please.
aorth 10y ago
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/778141129564905472 https://mobile.twitter.com/MarkHamiIl/status/778141129564905...
- vidarh 10y ago
  When I first moved to London, one of my co-worker was a very exuberant young Scottish woman. Her accent was near impossible for me, and she fully realised this, but at one point we came to the mutual understanding that I did in fact pick up most of what she was saying, even though it was only because she talked so much, that she didn't really need to intentionally repeat what she was saying.
- pjc50 10y ago
  Obligatory Burnistoun: https://www.youtube.com/watch?v=sAz_UvnUeuU 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.
wRastel27 10y ago
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.
- a3n 10y ago
  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."
  - ralfd 10y ago
    > 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.
    - thaumasiotes 10y ago
      > Is this long term really a problem though? How long term? What's "a problem"? We know with certainty that given the passage of enough time, the speech of "these kids today" will be completely unintelligible to you.
  - gugagore 10y ago
    Do you think written Standard English is less ambiguous than any other English, or written language? Because I don't see why that would be the case.
    - Dylan16807 10y ago
      >Do you think written Standard English is less ambiguous than any other English Definitely. Things are clearer when you write formally and strictly adhere to grammatical rules. When I'm speaking casually I omit words and (ab)use punctuation much differently, and while that is perfectly acceptable to do, it is more complex and ambiguous to parse. As far as I'm aware the same pattern exists in a lot of languages and dialects, and it's possible to define formal AAVE too.
danharaj 10y ago
There is nothing about a computer algorithm that renders it immune to the power structures around it. The people who train these systems don't even realize they are creating a biased model. Borrowing from postcolonial theory, this is related to the concept of epistemic violence. The way we organize human information, the way we categorize and evaluate viewpoints and ways of understanding the world is a battleground for imperialism. Intent does not matter. The algorithm has no intent, the researchers don't intend to create a vehicle of oppression, and yet it happens anyway. They have created a difference in economic utility between standard, hegemonic English, and marginalized dialects of English which inevitably will have social ramifications. If this study weren't conducted, would we have ever found out?
Kapura 10y ago
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.
Animats 10y ago
The article refers to another article about "Black Twitter"[1], but the examples are all about hashtags. It doesn't seem to be a dialect; it's about subject matter of interest to the community. [1] http://www.theatlantic.com/technology/archive/2015/04/the-truth-about-black-twitter/390120/ http://www.theatlantic.com/technology/archive/2015/04/the-tr...
gcr 10y ago
For context, Olga Russakovsky is the lead author behind the ImageNet Large Scale Visual Recognition Challenge, This is one of the most popular computer vision benchmarks in the world. Olga has expertise dealing with issues of dataset bias. A large number of ImageNet classes are dogs and birds, so most of the representational power in CNNs trained on this set are devoted to distinguishing between similar-looking dog breeds and bird species.
yepperz 10y ago
It would be helpful if the article would cite an example of what it is purporting to report. Otherwise it's pointless.
- savanaly 10y ago
  This should help you get the gist of it? https://www.reddit.com/r/blackpeopletwitter/ https://www.reddit.com/r/blackpeopletwitter/
fallous 10y ago
This, I suspect but do not know, may be a failing of the current direction of NL AI solutions. Selecting and using datasets to train NLP systems is a hard thing, especially if you truly treat it as a pattern to be learned rather than an enhancement of an underlying description of the particular language you're trying to learn. Are we headed towards a time when deeply trained networks seem to work in an acceptably large number of cases, but how and why are unknown and susceptible to fatal flaws that rarely but catastrophically reveal themselves? I suspect that is the case with the current path of machine learning but hope I am wrong. It just seems to me that the inevitable results of certain efforts in machine learning are magic boxes that "work" but no one understands why or how, which smacks of the days of medicine prior to an understanding of the germ theory of disease where certain efforts at sanitation due to the belief in "ill humors" did improve health but were based on utterly false underlying theories, and those successes tended to reinforce other false solutions that did not improve health but in fact was harmful.
- earljwagner 10y ago
  Yes, there's growing awareness of this problem. This blog post summarizes the paper "Machine Learning: The High-Interest Credit Card of Technical Debt": https://blog.acolyer.org/2016/02/29/machine-learning-the-high-interest-credit-card-of-technical-debt/ https://blog.acolyer.org/2016/02/29/machine-learning-the-hig...
eplanit 10y ago
This reminds me of the Ebonics controversy of the 1990s[1]. [1] https://en.wikipedia.org/wiki/Oakland_Ebonics_controversy https://en.wikipedia.org/wiki/Oakland_Ebonics_controversy
rasz_pl 10y ago
Is this really advocating AI blackface? Will work just as well as this https://www.youtube.com/watch?v=oCWqlDK52c4 https://www.youtube.com/watch?v=oCWqlDK52c4

90 points by jnordwick 10y ago

kriaq 10y ago

Exactly why googles filtering system will fail

"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 people read (subscribe to?) it. That does not mean that only 8 million people in the US use so-called standard English. There are also a lot of people who don't subscribe to that particular publication but still speak or write in "standard" English and that group is much larger than the group of WSJ readers.

mattdeboard 10y ago
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.
- lqdc13 10y ago
  Then why bring up WSJ at all? What about all the other papers? and what about all the other sources of training data for NLP research? WSJ dataset is not the only such dataset. NLP is not very good with standard English yet and usually doesn't generalize from topic to topic. Dialects and other languages - especially those without formal rules - will come when we can deal with standard English.
  - gordonguthrie 10y ago
    All language has rules - there is no language 'without formal rules'
    - morgante 10y ago
      What are the "formal rules" of AAVE?
      - thaumasiotes 10y ago
        https://en.wikipedia.org/wiki/African_American_Vernacular_English#References https://en.wikipedia.org/wiki/African_American_Vernacular_En...
        morgante 10y ago
        I'm looking for formal rules. I'm not saying AAVE doesn't exist, I'm saying its rules (such as they are) are not formalized. Tons of rule books exist for Standard English and part of learning it is to memorize the correct rules. I don't believe any formal equivalent exists for AAVE.
        iamdave 10y ago
        What, then, are your parameters for formal? It has to be in a book, codified and signed off on? If so, aren't these kind of antithetical to AAVE on the face of it?
        morgante 10y ago
        > It has to be in a book, codified and signed off on? The point is for rules to be formal then the must be formalized somehow and codified. This could be online or in a book, but the point is that there must be some clear delineation between when the rules are followed and when they are broken. Otherwise what's the meaning of "formal" rules? > If so, aren't these kind of antithetical to AAVE on the face of it? Yes. My argument is that AAVE, almost by definition, is an informal dialect without formalized rules. This is getting rather off topic, but I think it relates to the original point of why NLP might start with Standard English even if you are not biased. A large corpus of Standard English text (such as from the WSJ) will generally be very internally consistent precisely because it follows a set of formal rules codified into a style guide. As there is no such equivalent for AAVE, even gathering a large and internally consistent corpus of AAVE text seems prohibitively difficult. That being said, I do hope researchers are working on gathering text from Twitter to build up new training sets.
- tedunangst 10y ago
  "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.
- Dylan16807 10y ago
  You can read the WSJ without going about your everyday life forming sentences the same way. It's a terrible claim no matter how you interpret it.
ubernostrum 10y ago
It's hard to take a poster seriously when they show willful ignorance of the way language serves as a marker of class, ethnicity and race, and instead have to manufacture quibbles to try to discredit an article that might cause slightly less comfort with the notion of the Glorious AI Future™ -- Coming Soon™!
- Dylan16807 10y ago
  I think your post is the most aggressive instance of putting strawman words in someone else's mouth I've seen all month.
briholt 10y ago
> When researchers evaluated their model against natural language processing tools...researchers found that the software flagged African-American English as “not English” at a much higher frequency than standard English. Likewise, notice the deceptive switch between "English" and "standard English." So basically language bots trained to identify standard English are correctly identifying non-standard English dialects as not standard English.

panarky 10y ago

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. Should we attempt to detect prejudices in machine learning models and actively counter them, creating a self-reinforcing virtuous cycle of balance and tolerance? ---- As an aside, for an article about language, this one has a lot of typos. Doesn't anyone proofread before posting?

bmmayer1 10y ago
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 or it doesn't--why is Google attempting to 'read' and analyze the content itself? (Other than to serve ads of course)
- deleted 10y ago
  [deleted]
- panarky 10y ago
  > 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 of African American language as well as it can standard English, then that content is likely to be less visible in search results. [1] https://www.theguardian.com/science/2015/may/21/google-a-step-closer-to-developing-machines-with-human-like-intelligence https://www.theguardian.com/science/2015/may/21/google-a-ste...

h4nkoslo 10y ago

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=1329&bih=706&source=lnms&tbm=isch&sa=X&sqi=2&ved=0ahUKEwiX4MjizqjPAhVX5mMKHQ9oCQEQ_AUIBigB https://www.google.com/search?q=white+man+white+woman&biw=13... https://www.google.com/doodles/veterans-day-2015 https://www.google.com/doodles/veterans-day-2015 http://www.cbc.ca/news/trending/google-doodle-juno-reaches-jupiter-8-bit-nasa-scientists-1.3664827 http://www.cbc.ca/news/trending/google-doodle-juno-reaches-j...

Chinjut 10y ago
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 whether that's even an accurate assessment in the financial terms in which it was presumably intended: Do ethics stop at treating well only the people with most profit potential? Is impact on business our only arbiter?
- h4nkoslo 10y ago
  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.
  - dang 10y ago
    Let's not have race rants on HN, please.

aorth 10y ago

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/778141129564905472 https://mobile.twitter.com/MarkHamiIl/status/778141129564905...

vidarh 10y ago
When I first moved to London, one of my co-worker was a very exuberant young Scottish woman. Her accent was near impossible for me, and she fully realised this, but at one point we came to the mutual understanding that I did in fact pick up most of what she was saying, even though it was only because she talked so much, that she didn't really need to intentionally repeat what she was saying.
pjc50 10y ago
Obligatory Burnistoun: https://www.youtube.com/watch?v=sAz_UvnUeuU 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.

wRastel27 10y ago

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.

a3n 10y ago
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."
- ralfd 10y ago
  > 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.
  - thaumasiotes 10y ago
    > Is this long term really a problem though? How long term? What's "a problem"? We know with certainty that given the passage of enough time, the speech of "these kids today" will be completely unintelligible to you.
- gugagore 10y ago
  Do you think written Standard English is less ambiguous than any other English, or written language? Because I don't see why that would be the case.
  - Dylan16807 10y ago
    >Do you think written Standard English is less ambiguous than any other English Definitely. Things are clearer when you write formally and strictly adhere to grammatical rules. When I'm speaking casually I omit words and (ab)use punctuation much differently, and while that is perfectly acceptable to do, it is more complex and ambiguous to parse. As far as I'm aware the same pattern exists in a lot of languages and dialects, and it's possible to define formal AAVE too.

danharaj 10y ago

There is nothing about a computer algorithm that renders it immune to the power structures around it. The people who train these systems don't even realize they are creating a biased model. Borrowing from postcolonial theory, this is related to the concept of epistemic violence. The way we organize human information, the way we categorize and evaluate viewpoints and ways of understanding the world is a battleground for imperialism. Intent does not matter. The algorithm has no intent, the researchers don't intend to create a vehicle of oppression, and yet it happens anyway. They have created a difference in economic utility between standard, hegemonic English, and marginalized dialects of English which inevitably will have social ramifications. If this study weren't conducted, would we have ever found out?

Kapura 10y ago

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.

Animats 10y ago

The article refers to another article about "Black Twitter"[1], but the examples are all about hashtags. It doesn't seem to be a dialect; it's about subject matter of interest to the community. [1] http://www.theatlantic.com/technology/archive/2015/04/the-truth-about-black-twitter/390120/ http://www.theatlantic.com/technology/archive/2015/04/the-tr...

gcr 10y ago

For context, Olga Russakovsky is the lead author behind the ImageNet Large Scale Visual Recognition Challenge, This is one of the most popular computer vision benchmarks in the world. Olga has expertise dealing with issues of dataset bias. A large number of ImageNet classes are dogs and birds, so most of the representational power in CNNs trained on this set are devoted to distinguishing between similar-looking dog breeds and bird species.

yepperz 10y ago

It would be helpful if the article would cite an example of what it is purporting to report. Otherwise it's pointless.

savanaly 10y ago
This should help you get the gist of it? https://www.reddit.com/r/blackpeopletwitter/ https://www.reddit.com/r/blackpeopletwitter/

fallous 10y ago

This, I suspect but do not know, may be a failing of the current direction of NL AI solutions. Selecting and using datasets to train NLP systems is a hard thing, especially if you truly treat it as a pattern to be learned rather than an enhancement of an underlying description of the particular language you're trying to learn. Are we headed towards a time when deeply trained networks seem to work in an acceptably large number of cases, but how and why are unknown and susceptible to fatal flaws that rarely but catastrophically reveal themselves? I suspect that is the case with the current path of machine learning but hope I am wrong. It just seems to me that the inevitable results of certain efforts in machine learning are magic boxes that "work" but no one understands why or how, which smacks of the days of medicine prior to an understanding of the germ theory of disease where certain efforts at sanitation due to the belief in "ill humors" did improve health but were based on utterly false underlying theories, and those successes tended to reinforce other false solutions that did not improve health but in fact was harmful.

earljwagner 10y ago
Yes, there's growing awareness of this problem. This blog post summarizes the paper "Machine Learning: The High-Interest Credit Card of Technical Debt": https://blog.acolyer.org/2016/02/29/machine-learning-the-high-interest-credit-card-of-technical-debt/ https://blog.acolyer.org/2016/02/29/machine-learning-the-hig...

eplanit 10y ago

This reminds me of the Ebonics controversy of the 1990s[1]. [1] https://en.wikipedia.org/wiki/Oakland_Ebonics_controversy https://en.wikipedia.org/wiki/Oakland_Ebonics_controversy

rasz_pl 10y ago

Is this really advocating AI blackface? Will work just as well as this https://www.youtube.com/watch?v=oCWqlDK52c4 https://www.youtube.com/watch?v=oCWqlDK52c4