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Deep-Fried Data

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Re: Deep-Fried Data

#111

Post by aab0 »

skybrian wrote: post #12

If you think the Internet is as safe and controlled as a shopping mall, you probably should be reading Krebs on Security more. People tend to move towards the more mall-like areas of the Internet due to spam and abuse that they don't want to deal with. This can be low-level stuff, or (as in the cases of Kreb himeself) sometimes the attackers get out the big guns, and you need to run for cover. And that's why we're ha…

> If you think the Internet is as safe and controlled as a shopping mall, you probably should be reading Krebs on Security more.

That's an amusing comparison, given how much of Krebs focuses on offline ATM skimming, copying credit cards at point-of-sale terminals, hacking major retailers's CC databases, and using stolen cards at retail and mall stores to cash them out...

Re: Deep-Fried Data

#112

Post by sho_hn »

coldtea wrote: post #66
wtracy wrote: post #51

Earlier quoted context omitted.

> And I don't mean open source projects. Then what do you mean? You described exactly what some of the largest, most successful FOSS projects (Firefox, KDE, Gnome, Libre Office, FreeBSD) are already doing. > Let's group together smart people wanting to make a difference and have a hit list of things we (people) actually need. Well, the FSF maintains a list of "high priority Free Software projects" that need help, but…

> You described exactly what some of the largest, most successful FOSS projects (Firefox, KDE, Gnome, Libre Office, FreeBSD) are already doing. They "make a difference"? How exactly? At best, I can understand that for Firefox.

> They "make a difference"? How exactly?

KDE so far:

* Is used productively by large numbers of users doing cool stuff, from your fellow developers to the scientists controlling the Large Hadron Collider to the VFX artists who made Gravity

* Helped popularize open source as a development and licensing model, which now comprises much of the industry

* Helped Linux get attenttion and grow, ditto

* Wrote technology in use on billions of computers (KHTML -> WebKit, large parts of Qt via tight symbiosis, taglib, ...)

* Had a strong effect on industry tool choices (e.g. raising up CMake into the de-facto cross-platform C++ build system by adopting and helping improve it with requests and code, or hosting the valgrind bug tracker, or helping make SVN scale after adopting it as replacement for CVS)

* Has made many hundreds of people more experienced and competent engineers through providing mentorship, and enabled them to apply those skills and that knowledge elsewhere

Most of these are ongoing.

Considering how easy it is in this industry to spend your days making things no one needs or wants, or worse, actively screws over people, I'm pretty happy with how I spent my last ~12 years as a KDE developer :)

Re: Deep-Fried Data

#113

Post by [deleted] »

coldtea wrote: post #66
wtracy wrote: post #51

Earlier quoted context omitted.

> And I don't mean open source projects. Then what do you mean? You described exactly what some of the largest, most successful FOSS projects (Firefox, KDE, Gnome, Libre Office, FreeBSD) are already doing. > Let's group together smart people wanting to make a difference and have a hit list of things we (people) actually need. Well, the FSF maintains a list of "high priority Free Software projects" that need help, but…

> You described exactly what some of the largest, most successful FOSS projects (Firefox, KDE, Gnome, Libre Office, FreeBSD) are already doing. They "make a difference"? How exactly? At best, I can understand that for Firefox.

[deleted]

Re: Deep-Fried Data

#114

Post by [deleted] »

coldtea wrote: post #66
wtracy wrote: post #51

Earlier quoted context omitted.

> And I don't mean open source projects. Then what do you mean? You described exactly what some of the largest, most successful FOSS projects (Firefox, KDE, Gnome, Libre Office, FreeBSD) are already doing. > Let's group together smart people wanting to make a difference and have a hit list of things we (people) actually need. Well, the FSF maintains a list of "high priority Free Software projects" that need help, but…

> You described exactly what some of the largest, most successful FOSS projects (Firefox, KDE, Gnome, Libre Office, FreeBSD) are already doing. They "make a difference"? How exactly? At best, I can understand that for Firefox.

[deleted]

Re: Deep-Fried Data

#115

Post by yummyfajitas »

mrow84 wrote: post #110
yummyfajitas wrote: post #97

Earlier quoted context omitted.

This is simply not true. Most algorithms can and will correct for biases in their inputs. See this (somewhat technical) article where I go into explicit (simulations in numpy) levels of detail: https://www.chrisstucchio.com/blog/2016/alien_intelligences_... The best analogy I've come up with for the non-technical is that algorithms are like humans trying to draw inferences on octopus society. Some octopi might have b…

To correct biased measurements (in a careful way) you need 1. Enough knowledge about the structure of the bias to be able to devise a model for it. 2. Some measurements from which to fit the model, with errors that are uncorrelated with the errors in your original data. These things are not always easy to obtain, even in relatively mundane settings. It is also a distinctly non-automatic procedure - it requires someon…

I'm not making the claim that an algorithm magically fixes everything. I'm claiming that sometimes they do which makes bias less likely to be present in the ML model.

You don't need a human data scientist to decide bias exists, model it and fix it at all. If you read the post I linked to, you can observe a synthetic example of linear regression (with redundant encodings) accidentally fixing bias.

So yes, if your model is expressive enough and you have sufficient data, it will automatically fix bias. Is it really shocking that an algorithm which is good at finding hidden patterns will find a hidden pattern?

Re: Deep-Fried Data

#116

Post by srean »

yummyfajitas wrote: post #109
wrsh07 wrote: post #107

Earlier quoted context omitted.

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…

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…

Are you saying that it can form a good estimate of the conditional probability ? I can believe that if the sampling process preserves the conditional.

Otherwise one would have to make assumptions about (or in other words, model) the corruption process. The bias compensation machinery then has to be deliberate, wont happen on its own.

Some sampling processes do not modify the conditional. In those cases no special machinery would be required.

Re: Deep-Fried Data

#117

Post by yummyfajitas »

srean wrote: post #116
yummyfajitas wrote: post #109

Earlier quoted context omitted.

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…

Are you saying that it can form a good estimate of the conditional probability ? I can believe that if the sampling process preserves the conditional. Otherwise one would have to make assumptions about (or in other words, model) the corruption process. The bias compensation machinery then has to be deliberate, wont happen on its own. Some sampling processes do not modify the conditional. In those cases no special mac…

tOne approach is to directly model the corruption process. Being the model-based-Bayesian guy I am, this is something I like to do.

But if your model is sufficiently expressive you don't need to explicitly build or model the corruption process. In the example in my linked blog post, test scores might be biased against blacks. But race is also redundantly encoded, so the algorithm has enough information to fix the bias completely by accident.

Fundamentally what I'm saying here is that bias is a statistics problem and has a statistics solution. Insofar as your complaint is algorithms finding the wrong answer, the solution is better stats.

And nothing whatsoever that I've said here would be remotely controversial if the topic were remote sensing.

Re: Deep-Fried Data

#118

Post by mrow84 »

yummyfajitas wrote: post #115
mrow84 wrote: post #110

Earlier quoted context omitted.

To correct biased measurements (in a careful way) you need 1. Enough knowledge about the structure of the bias to be able to devise a model for it. 2. Some measurements from which to fit the model, with errors that are uncorrelated with the errors in your original data. These things are not always easy to obtain, even in relatively mundane settings. It is also a distinctly non-automatic procedure - it requires someon…

I'm not making the claim that an algorithm magically fixes everything. I'm claiming that sometimes they do which makes bias less likely to be present in the ML model. You don't need a human data scientist to decide bias exists, model it and fix it at all. If you read the post I linked to, you can observe a synthetic example of linear regression (with redundant encodings) accidentally fixing bias. So yes, if your mode…

I don't really understand that claim. You are explicitly adding a bias that is linearly dependant on your race variable, and then allowing your regression to recover that bias by introducing noisy measurements of race (which you as the modeller knew was the thing causing the bias). As you say, that is unsurprising.

That result does not, however, address my point, which is that if the structure of the bias is difficult to understand, or perhaps even just difficult to model, and if relevant measurements (with errors that are uncorrelated with your original errors) are unavailable, then bias correction is essentially impossible.

Re: Deep-Fried Data

#119

Post by yummyfajitas »

mrow84 wrote: post #118
yummyfajitas wrote: post #115

Earlier quoted context omitted.

I'm not making the claim that an algorithm magically fixes everything. I'm claiming that sometimes they do which makes bias less likely to be present in the ML model. You don't need a human data scientist to decide bias exists, model it and fix it at all. If you read the post I linked to, you can observe a synthetic example of linear regression (with redundant encodings) accidentally fixing bias. So yes, if your mode…

I don't really understand that claim. You are explicitly adding a bias that is linearly dependant on your race variable, and then allowing your regression to recover that bias by introducing noisy measurements of race (which you as the modeller knew was the thing causing the bias). As you say, that is unsurprising. That result does not, however, address my point, which is that if the structure of the bias is difficul…

The point is that the bias is linear, and my model is linear, so the model fixes things. The example is synthetic (so we could know what the right answer is and check if we recover it) so of course I put everything in.

In the linked article, I explicitly reference a real world case where the same linear model was used to discover that grades and test scores are biased in favor of blacks: http://ftp.iza.org/dp8733.pdf

In more complicated situations, the bias would need to be amenable to detection by a neural network, an SVM or random forest. The entire purpose of models like this is that lots of hidden patterns are detected.

Even if relevant measurements are unavailable, one can use redundant encoding to fix bias. Delip Rao explains redundant encoding here, for example, though he is more concerned that ML models might learn facts he wants to remain hidden: http://deliprao.com/archives/129

Re: Deep-Fried Data

#120

Post by srean »

yummyfajitas wrote: post #117
srean wrote: post #116

Earlier quoted context omitted.

Are you saying that it can form a good estimate of the conditional probability ? I can believe that if the sampling process preserves the conditional. Otherwise one would have to make assumptions about (or in other words, model) the corruption process. The bias compensation machinery then has to be deliberate, wont happen on its own. Some sampling processes do not modify the conditional. In those cases no special mac…

tOne approach is to directly model the corruption process. Being the model-based-Bayesian guy I am, this is something I like to do. But if your model is sufficiently expressive you don't need to explicitly build or model the corruption process. In the example in my linked blog post, test scores might be biased against blacks. But race is also redundantly encoded, so the algorithm has enough information to fix the bia…

> But if your model is sufficiently expressive you don't need to explicitly build or model the corruption process

This is the claim that I am having trouble with.

Say I have two random variable X,Y with some joint distribution. If a corruption process can mess with the samples drawn from it, I cannot see how it could possibly recover either the joint or the conditional.

Are you saying that the corruption is benign like missing at random or missing completely at random ? Then its much more believable.

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