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Deep-Fried Data

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Re: Deep-Fried Data

#121

Post by yummyfajitas »

srean wrote: post #120
yummyfajitas wrote: post #117

Earlier quoted context omitted.

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 li…

So we both agree that if the bias is linear, and your model is linear, you capture it. Similarly if the model involves interaction (score x is_black), and you include linear interaction terms, you'll also capture it.

Now the question arises; what if things are more complex?

In real life they always are; both your biasing factor and the rest of the model. So we've cooked up all sorts of fun models like SVMs, random forests and neural networks to analyze such complicated models and find hidden features and relations that we didn't think of. Bias is one such feature.

If I built an algorithm that learned to display different ads to mobile and desktop people (i.e., treat mobile "time on site" differently from desktop "time on site"), would you be surprised by this?

Re: Deep-Fried Data

#122

Post by coldtea »

sangnoir wrote: post #99
coldtea wrote: post #86

Earlier quoted context omitted.

> By providing quality software that lets me and thousands of others get useful work done, and not forcing us to accept onerous licensing terms in the process? That software (desktop Linux/UNIX) comes for free, and yet, only 1% or less (from browser stats of major traffic points) seem to opt to use it as their desktop. Is this the kind of difference the parent was describing? Letting a small minority of people avoid…

> That software (desktop Linux/UNIX) comes for free, and yet, only 1% or less (from browser stats of major traffic points) seem to opt to use it as their desktop. >Is this the kind of difference the parent was describing? Cancer kills 171.2 per 100,000[1]. So by your metrics, the Linux desktop folks make a bigger difference than curing cancer as 1% > 0.1712% 1. Cancer mortility. see https://www.cancer.gov/about-cance…

>Cancer kills 171.2 per 100,000[1]. So by your metrics, the Linux desktop folks make a bigger difference than curing cancer as 1% > 0.1712%

This is the kind of illogical result stemming from only reasoning half-way.

First of all, the cure for cancer wouldn't affect only the ones that die but also the ones that don't but do suffer complications from current treatment, from going broke from paying for therapy/losing their job in the process, to severe chemo side-effects. It also hugely affects the families and loved ones of those who currently die of cancer.

Second, ever considered the kind and magnitude of impact? Saving even 171 persons (those in a single bunch of 100,000) from dying from cancer is, arguably, quite a bigger deal than sparing millions to have to use Windows or OS X or some commercial UNIX.

Re: Deep-Fried Data

#123

Post by eridius »

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…

The problem is you're modeling a biased reality. And accurately modeling a biased reality may in many cases accentuate the bias. Take for example the previously-mentioned case of using an algorithm to determine where to focus your policing efforts. If the data you have says that more arrests are done in a particular part of the city, then you'll want to put more police there, right? But areas where there are more police will tend to see more arrests. So the fact that you're putting more police in an area where you see more arrests is just going to make the bias more extreme, causing even more arrests there. This causes a feedback loop. So you may be accurately modeling reality, but you're modeling a pre-existing bias and making it worse. And who knows why that pre-existing bias was even there? The fact that there were more arrests there may not be because that area actually has more crime committed, it could be due to other factors, such as racial profiling by police, and in that case your algorithm is now accidentally racist because it's perpetuating racial profiling.

Re: Deep-Fried Data

#124

Post by dragonwriter »

eridius wrote: post #123
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…

The problem is you're modeling a biased reality. And accurately modeling a biased reality may in many cases accentuate the bias. Take for example the previously-mentioned case of using an algorithm to determine where to focus your policing efforts. If the data you have says that more arrests are done in a particular part of the city, then you'll want to put more police there, right? But areas where there are more pol…

The problems are really twofold:

(1) Defining the proper goals, and

(2) Measuring the right things (such as the real goals of interest rather than biased proxies.)

With police deployments, you are assuming the solution (rather than letting your algorithm optimize it) by saying "I want to put more police where more arrests occur". What you really want is probably something more like (the exact goal may be different, of course) "I want to deploy police resources where it will most effectively reduce the incidence of crime, weighted by some assigned measure of severity." Then let your ML algorithm crunch the various measurable factors and produce an optimum deployment to do that.

(But, then again with that goal -- and similar problems exist with many likely real goals -- you run into the other problem, which is measuring the incidence of crime -- measuring crime reports may be the obvious approach, but there's plenty of evidence that lots of factors can bias crime reports, including communities having bad experience with police being less likely to report crimes.)

Re: Deep-Fried Data

#125

Post by yummyfajitas »

eridius wrote: post #123
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…

The problem is you're modeling a biased reality. And accurately modeling a biased reality may in many cases accentuate the bias. Take for example the previously-mentioned case of using an algorithm to determine where to focus your policing efforts. If the data you have says that more arrests are done in a particular part of the city, then you'll want to put more police there, right? But areas where there are more pol…

I directly addressed this critique two posts up. Why don't you go read that post?

https://news.ycombinator.com/item?id=12627359

Re: Deep-Fried Data

#126

Post by eridius »

yummyfajitas wrote: post #125
eridius wrote: post #123

Earlier quoted context omitted.

The problem is you're modeling a biased reality. And accurately modeling a biased reality may in many cases accentuate the bias. Take for example the previously-mentioned case of using an algorithm to determine where to focus your policing efforts. If the data you have says that more arrests are done in a particular part of the city, then you'll want to put more police there, right? But areas where there are more pol…

I directly addressed this critique two posts up. Why don't you go read that post? https://news.ycombinator.com/item?id=12627359

I did read it, but you're talking about correcting for measurement biases in order to recover an accurate view of reality. But what I'm saying is that accurately measuring reality may in fact be how you get bias, because the very thing you're measuring may be biased. If you're aware the bias exists and have tools that can measure the bias itself then maybe you can correct for the bias, but you can't just expect your algorithm to automatically correct itself in the presence of bias because its goal is to model reality, not to figure out whether there's inherent bias in the thing it's modeling.

Re: Deep-Fried Data

#127

Post by odbol_ »

keyle wrote: post #44
pimlottc wrote: post #19

> Many [programmers] work jobs that are intellectually stimulating, but ultimately leave nothing behind. There is a large population of technical people who would enjoy contributing to something lasting. This hits pretty close to home.

Same here. I'm battling with this thought a lot. Beyond jobs, I think there should be communities of developers, designers, producers, writers, getting together and figuring out this stuff. And I don't mean open source projects. Let's group together smart people wanting to make a difference and have a hit list of things we (people) actually need. A group that would organise people into mission driven development. I'm…

I think the First Things First manifesto is a good start: http://firstthingsfirst2014.org

Re: Deep-Fried Data

#128

Post by odbol_ »

jaywunder wrote: post #61

I don't think I understand what the exact point of this talk was. Maybe the thesis was stated at the end of the talk when he said that he wishes the internet were more like a city rather than a mall. I think the internet can be like a city, and I think a great example of a place where people with conflicting ideas talk together is HN. Sure HN can be an echo chamber at times. But there's quite a few times when people…

There's already been studies on how Google can manipulate elections just by reordering search results, or how Facebook can alter your mood by tweaking what goes into your news feed. It's not hard to imagine how bad things could get if that kind of power got into the hands of the next Mussolini or Stalin (which is a very real possibility aka Trump)

Re: Deep-Fried Data

#129

Post by odbol_ »

b2m9 wrote: post #102
rubidium wrote: post #98

Earlier quoted context omitted.

Ok, this tweaks my curiosity. Why would one put SSL on a static personal/blog type website (assuming one doesn't care about the google penalty)?

For example, ISPs are not able to crawl your traffic if it's via HTTPS. I've worked on data sets gathered by major ISPs and it's scary how much they know about their users (especially if they also have a mobile phone with the same company). ISPs use such intelligence for personalised marketing (either for their own product catalogue or 3rd parties)

The URL isn't encrypted though, is it? Since there's no dynamic content on the page, they already know exactly what you're looking at.

Re: Deep-Fried Data

#130

Post by detaro »

odbol_ wrote: post #129
b2m9 wrote: post #102

Earlier quoted context omitted.

For example, ISPs are not able to crawl your traffic if it's via HTTPS. I've worked on data sets gathered by major ISPs and it's scary how much they know about their users (especially if they also have a mobile phone with the same company). ISPs use such intelligence for personalised marketing (either for their own product catalogue or 3rd parties)

The URL isn't encrypted though, is it? Since there's no dynamic content on the page, they already know exactly what you're looking at.

The domain isn't, the full URL is. (But content size etc probably still allow identification of an individual page on a small site, and the context of the domain is already valuable)
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