Machine Bias: Man Is to Computer Programmer as Woman Is to Homemaker?
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Re: Machine Bias: Man Is to Computer Programmer as Woman Is to Homemaker?
#2If I were training a classifier to predict whether a sentence is talking about household activities v/s not, wouldn't the occurrence of man/woman in the sentence be a good feature? Today, woman do perform household activities more (whether we like it or not), and wouldn't it make sense to use that piece of information when performing some predictive analysis?
The technical sense of "bias" arises when the train and test distributions differ. Obviously if you train with a dataset of text from a foreign country's news and then apply it on an American context, the difference in the data distributions will introduce bias, but why do we need a social twist to this already well-functioning term? If the same classifier is trained and evaluated in India (with its sexist roles, say), then there's no (technical) bias and I don't see why it's a bad application.
Re: Machine Bias: Man Is to Computer Programmer as Woman Is to Homemaker?
#3If I were training a classifier to predict whether a sentence is talking about household activities v/s not, wouldn't the occurrence of man/woman in the sentence be a good feature? Today, woman do perform household activities more (whether we like it or not), and wouldn't it make sense to use that piece of information when performing some predictive analysis? The technical sense of "bias" arises when the train and te…
>wouldn't it make sense to use that piece of information when performing some predictive analysis?
No, because eventually your system will graduate from predicting the results of society's bias to reinforcing society's bias. That is a bad thing.
Re: Machine Bias: Man Is to Computer Programmer as Woman Is to Homemaker?
#4In the sjw-religion, why is "homemaker" considered inferior to "computer programmer"? One of the oldest and most important human occupations versus hunched over at a desk slaving for a salary until being outsourced to a bot in 5 years? I've never understood the default sjw/"feminism" assumptions that anything feminine is "bad".
Re: Machine Bias: Man Is to Computer Programmer as Woman Is to Homemaker?
#5If I were training a classifier to predict whether a sentence is talking about household activities v/s not, wouldn't the occurrence of man/woman in the sentence be a good feature? Today, woman do perform household activities more (whether we like it or not), and wouldn't it make sense to use that piece of information when performing some predictive analysis? The technical sense of "bias" arises when the train and te…
I think you have a really good point here. The problem is that we have this current bias in society and people wish to change it. I think there is a fear, that if we reflect this bias, in the way we talk, we re-enforce the bias.
It seems an effective tool, if you want to change thinking then police the way words can be used around the topic. It is however worrying that machines could start playing a role in this. It could become a powerful tool in steering public opinion. This doesn't seem too bad, but that could be used to favour an incumbent political party, or more than likely to sell products we otherwise don't really want.
But you are right machines need accuracy and removing that bias could be detrimental to the task they're solving.
Re: Machine Bias: Man Is to Computer Programmer as Woman Is to Homemaker?
#6If I were training a classifier to predict whether a sentence is talking about household activities v/s not, wouldn't the occurrence of man/woman in the sentence be a good feature? Today, woman do perform household activities more (whether we like it or not), and wouldn't it make sense to use that piece of information when performing some predictive analysis? The technical sense of "bias" arises when the train and te…
>wouldn't it make sense to use that piece of information when performing some predictive analysis? No, because eventually your system will graduate from predicting the results of society's bias to reinforcing society's bias. That is a bad thing.
Can you give an example of a situation where an ML application would be reinforcing a problematic bias but still have good performance metrics? My point is that a wrongly-applied ML application would suffer in just plain accuracy. For instance, a Automatic Carrier Counsellor might give "homemaker" as a suggested career choice to women, but then before we start calling it biased, it would already be wrong. If the same algorithm had dug deeper, it would have learn that the said woman would be a great programmer.
Re: Machine Bias: Man Is to Computer Programmer as Woman Is to Homemaker?
#7If I were training a classifier to predict whether a sentence is talking about household activities v/s not, wouldn't the occurrence of man/woman in the sentence be a good feature? Today, woman do perform household activities more (whether we like it or not), and wouldn't it make sense to use that piece of information when performing some predictive analysis? The technical sense of "bias" arises when the train and te…
I think you have a really good point here. The problem is that we have this current bias in society and people wish to change it. I think there is a fear, that if we reflect this bias, in the way we talk, we re-enforce the bias. It seems an effective tool, if you want to change thinking then police the way words can be used around the topic. It is however worrying that machines could start playing a role in this. It…
My point isn't that accuracy and bias are orthogonal, but that bias is contained in the accuracy metric.
Re: Machine Bias: Man Is to Computer Programmer as Woman Is to Homemaker?
#8Earlier quoted context omitted.
I think you have a really good point here. The problem is that we have this current bias in society and people wish to change it. I think there is a fear, that if we reflect this bias, in the way we talk, we re-enforce the bias. It seems an effective tool, if you want to change thinking then police the way words can be used around the topic. It is however worrying that machines could start playing a role in this. It…
My point isn't that accuracy and bias are orthogonal, but that bias is contained in the accuracy metric.
Totally agree with you. I'm not at all trying to say accuracy and bias can be orthogonal.
I'm trying to say some people think they have a good enough reason to throw away accuracy if that means they can change a societal bias. But that can only be a good thing if you agree with the change being made.
Re: Machine Bias: Man Is to Computer Programmer as Woman Is to Homemaker?
#9Earlier quoted context omitted.
>wouldn't it make sense to use that piece of information when performing some predictive analysis? No, because eventually your system will graduate from predicting the results of society's bias to reinforcing society's bias. That is a bad thing.
Can you give an example of a situation where an ML application would be reinforcing a problematic bias but still have good performance metrics? My point is that a wrongly-applied ML application would suffer in just plain accuracy. For instance, a Automatic Carrier Counsellor might give "homemaker" as a suggested career choice to women, but then before we start calling it biased, it would already be wrong . If the sam…
Recidivism prediction systems will usually tell you that black people are more likely to get arrested/convicted again. They do so accurately, but also result in longer sentences for black people.
Re: Machine Bias: Man Is to Computer Programmer as Woman Is to Homemaker?
#10Earlier quoted context omitted.
Can you give an example of a situation where an ML application would be reinforcing a problematic bias but still have good performance metrics? My point is that a wrongly-applied ML application would suffer in just plain accuracy. For instance, a Automatic Carrier Counsellor might give "homemaker" as a suggested career choice to women, but then before we start calling it biased, it would already be wrong . If the sam…
Recidivism prediction systems will usually tell you that black people are more likely to get arrested/convicted again. They do so accurately, but also result in longer sentences for black people. https://arxiv.org/abs/1610.07524
Yeah but doesn't that have more to do with the way the predictions are used?
It seems to me to be a stupid thing to do. This person seems more likely to get convicted again, lock 'em up longer. Instead of asking why is this person more likely to get convicted again? Can we prevent this in a redemptive non punitive way?
It's really useful to have that prediction/data but how you use it is more important