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0 pointswrsh079y ago0 comments

This is very optimistic. There are well known and documented cases of ml algorithm bias and its cause [1].

And it's not surprising that data itself contains some biases from the humans creating it. Suppose police are asking machine learning where more crime is committed - there will be a feedback loop. Where are they currently making more arrests? If they spend more time there, the bias will be exaggerated.

The op correctly gauges how we should be cautious. Your post, I'm afraid, is misleading at best.

[1] https://www.google.com/amp/s/www.technologyreview.com/s/6017...

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yummyfajitas9y ago

Of course data contains biases. But again, please read the article I linked; algorithms will have a tendency to correct that bias.

The examples in the article you link to are not algorithmic bias at all. They consist of:

1) Humans at Facebook manipulating trending results.

2) Google's keyword algorithm (accurately) reflecting the fact that people with black names are more likely to have arrest records.

Lets distinguish "bias" from "accurately learning things you wish it wouldn't learn" or "accurately learning things you wish weren't true."

None of what I'm saying is remotely controversial. If I told you statistics could detect and correct bias in a mobile phone compass, you'd just think "cool stats bro". Is this article remotely controversial? https://www.chrisstucchio.com/blog/2016/bayesian_calibration...

The specific feedback loop you describe - variable detection probability => variable # of detections - can be directly mitigated. For a non-controversial example drawn from sensor networks (sensors report events with a delayed eraction, the longer you wait the more events you detect), see here: https://www.chrisstucchio.com/blog/2016/delayed_reactions.ht...

(You can find similar examples all over the place. I just link to the ones I wrote because they spring immediately to mind.)

In a compass, a sensor network, adtech or other quant finance, the idea that machine learning can fix biased inputs is not remotely controversial. The concept that statistics suddenly stops working to fix racism is just silly anthropomorphism.

wrsh07OP9y ago

Aha - I think I see our miscommunication. When you say bias you mean statistical bias.

Yes, machine learning is able to correct for that kind of bias - 538's polls forecast is a good example of that.

But you don't get to redefine racial bias to be something innocuous. Yes, black names are more likely to have arrest records, but that "fact" is super misleading [1].

Finally, you're talking past me. I'm not saying that statistics is broken. I'm saying that we should be especially mindful of the OPs point when they say this:

> So what’s your data being fried in? These algorithms train on large collections that you know nothing about. Sites like Google operate on a scale hundreds of times bigger than anything in the humanities. Any irregularities in that training data end up infused into in the classifier.

I think the OP author also has a related post about the kind of bias I'm talking about: http://idlewords.com/talks/sase_panel.htm

[1]: http://www.huffingtonpost.com/kim-farbota/black-crime-rates-...

yummyfajitas9y ago

Without getting into a dispute about the definition of "bias", I'm saying that algorithms can accurately measure reality even if input(x=white, all else equal) != input(x=black, all else equal).

You are saying that algorithms are accurately measuring a reality you wish were different. I don't disagree with this.

The right thing to do is to actually answer unpleasant moral questions like "if blacks are 4x more likely to be dangerous criminals, what should we do about it?" But I guess overloading the word "bias" is a nice substitute for clearly thinking things through.

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yummyfajitas9y ago

Of course data contains biases. But again, please read the article I linked; algorithms will have a tendency to correct that bias.

The examples in the article you link to are not algorithmic bias at all. They consist of:

1) Humans at Facebook manipulating trending results.

2) Google's keyword algorithm (accurately) reflecting the fact that people with black names are more likely to have arrest records.

Lets distinguish "bias" from "accurately learning things you wish it wouldn't learn" or "accurately learning things you wish weren't true."

(You can find similar examples all over the place. I just link to the ones I wrote because they spring immediately to mind.)

wrsh07OP9y ago

Aha - I think I see our miscommunication. When you say bias you mean statistical bias.

Yes, machine learning is able to correct for that kind of bias - 538's polls forecast is a good example of that.

But you don't get to redefine racial bias to be something innocuous. Yes, black names are more likely to have arrest records, but that "fact" is super misleading [1].

Finally, you're talking past me. I'm not saying that statistics is broken. I'm saying that we should be especially mindful of the OPs point when they say this:

I think the OP author also has a related post about the kind of bias I'm talking about: http://idlewords.com/talks/sase_panel.htm

[1]: http://www.huffingtonpost.com/kim-farbota/black-crime-rates-...

yummyfajitas9y ago

Without getting into a dispute about the definition of "bias", I'm saying that algorithms can accurately measure reality even if input(x=white, all else equal) != input(x=black, all else equal).

You are saying that algorithms are accurately measuring a reality you wish were different. I don't disagree with this.

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