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EU regulations on algorithmic decision-making and a “right to explanation”

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Re: EU regulations on algorithmic decision-making and a “right to explanation”

#121

Earlier quoted context omitted.

I do in fact have an algorithm to remove racism from models, but I doubt its worth "$billions". The whole point of my argument is that it shouldn't be necessary. Surely you don't really believe racist stereotypes are true?

I do, in fact. There is extensive research supporting the fact that many (though not all) are accurate. The typical racist stereotype is about twice as likely to replicate as the typical sociology paper. Here are a couple of review articles to get you started: http://www.spsp.org/blog/stereotype-accuracy-response http://emilkirkegaard.dk/en/wp-content/uploads/Jussim-et-al-... I didn't say that simply removing "racism…

> Simply because it's socially unacceptable to think otherwise?

Please stop insinuating this at people in HN comments. You've done it frequently, and it's rude. (That goes for "simply because of your mood affiliation?", etc., too.)

Re: EU regulations on algorithmic decision-making and a “right to explanation”

#122
post #120

Earlier quoted context omitted.

But men are much riskier drivers. That's has nothing to do with sexism or discrimination against men, it's just an empirical fact. Why should women have to subsidize the cost of men?

Why is it acceptable to you to have sex explicitly in the model, but not race? If black men are empirically riskier drivers than Asian men (something insurance companies are currently legally forbidden to discriminate on), would you be fine with charging black men more so Asian men don't "subsidize" them?

Only because race has a history of irrational prejudice and discrimination. And because race shouldn't really be predictive of anything, or at least that's the politically accepted belief. So it's been made illegal to take that into account. Which is fine, if it was just limited to that. But it's led to hundreds of other categories also being "protected", and now they are outlawing categorizing people at all. It's ridiculous.

I'm perfectly fine with Asian drivers getting cheaper insurance, if they really are somehow safer drivers. Why should they pay more, if they aren't more risky?

What about wooden buildings getting higher insurance rates? Wooden buildings are more likely to be damaged in natural disasters, burn down, suffer water damage, etc. But that's not the fault of the owner. Any specific owner could take really good care of their building, have sprinklers and alarms, maintain everything to perfect condition, etc. Yet they still pay the higher rate, so it's not a fair system. But as a group, wooden buildings are just less safe.

An older person is probably going to pay much more in life insurance than a young person. Or an overweight person, or a smoker. It may not be fair on an individual level. After all not every smoker gets cancer, not every overweight person gets diabetes, etc. But as a group, some groups have more risk than others.

The point of insurance is to predict risk as accurately as possible. Using whatever information is available. If we had time machines, we could go into the future and see what people would get into car accidents and take away their licenses. Or tear down buildings that are going to fall down and hurt people. But because we don't have that information, insurance acts as a way to hedge risk. There is zero benefit to society by making their predictions less accurate, and perhaps serious economic costs.

Re: EU regulations on algorithmic decision-making and a “right to explanation”

#123
post #121

Earlier quoted context omitted.

I do, in fact. There is extensive research supporting the fact that many (though not all) are accurate. The typical racist stereotype is about twice as likely to replicate as the typical sociology paper. Here are a couple of review articles to get you started: http://www.spsp.org/blog/stereotype-accuracy-response http://emilkirkegaard.dk/en/wp-content/uploads/Jussim-et-al-... I didn't say that simply removing "racism…

> Simply because it's socially unacceptable to think otherwise? Please stop insinuating this at people in HN comments. You've done it frequently, and it's rude. (That goes for "simply because of your mood affiliation?", etc., too.)

As the person he was replying to, that statement wasn't rude in context. The belief being discussed really is socially unacceptable. My argument in fact, relied on that.

Re: EU regulations on algorithmic decision-making and a “right to explanation”

#124
post #121

Earlier quoted context omitted.

I do, in fact. There is extensive research supporting the fact that many (though not all) are accurate. The typical racist stereotype is about twice as likely to replicate as the typical sociology paper. Here are a couple of review articles to get you started: http://www.spsp.org/blog/stereotype-accuracy-response http://emilkirkegaard.dk/en/wp-content/uploads/Jussim-et-al-... I didn't say that simply removing "racism…

> Simply because it's socially unacceptable to think otherwise? Please stop insinuating this at people in HN comments. You've done it frequently, and it's rude. (That goes for "simply because of your mood affiliation?", etc., too.)

Dang, I'm really confused here. As Houshalter points out, he was deliberately making an argument based on social unacceptability of my beliefs. Searching for the phrase on hn.algolia.com, I've used the term 5 times on HN ever, never in the manner you imply.

I'm also confused about my use of the term "mood affiliation". Searching my use of the term, most of the time I use it to refer to an external source with a data table/graph that supports a claim I make, but a tone which contradicts mine. For example, I might cite Piketty's book which claims the best way to grow the economy is to funnel money to the rich (this follows directly from his claims that rich people have higher r than non-rich people). What's the dang-approved way to point this out?

In the rare occasion I use it to refer to a comment on HN, I'm usually asking whether I or another party are disagreeing merely at DH2 Tone (as per Paul Graham's levels of disagreement) or some higher level. What's the dang-approved way to ask this?

http://paulgraham.com/disagree.html

Let me know, I'll do exactly as you ask.

Re: EU regulations on algorithmic decision-making and a “right to explanation”

#125
post #121

Earlier quoted context omitted.

> Simply because it's socially unacceptable to think otherwise? Please stop insinuating this at people in HN comments. You've done it frequently, and it's rude. (That goes for "simply because of your mood affiliation?", etc., too.)

Dang, I'm really confused here. As Houshalter points out, he was deliberately making an argument based on social unacceptability of my beliefs. Searching for the phrase on hn.algolia.com, I've used the term 5 times on HN ever, never in the manner you imply. I'm also confused about my use of the term "mood affiliation". Searching my use of the term, most of the time I use it to refer to an external source with a data…

> usually asking whether I or another party are disagreeing merely at DH2 Tone (as per Paul Graham's levels of disagreement) or some higher level. What's the dang-approved way to ask this?

That seems like a good way to ask it right there.

Possibly I misread you in this case (possibly even in every case!) but my sense is frequently that you are not asking someone a question so much as implying that they have no serious concern, only emotional baggage that they're irrationally unwilling to let go of. That's a form of derision, and it doesn't lead to better arguments.

But if I've gotten you completely wrong, I'd be happy to see it and apologize.

Re: EU regulations on algorithmic decision-making and a “right to explanation”

#126

I made a couple of experiments on discrimination free machine learning models with naive Bayes, and I changed my perspective on data science. Usually people are concerned about maximising prediction accuracy, and never stop to think about what correlations is the model finding down below, and the human biases present in the data annotations. Removing sensitive variables (gender, race, etc) doesn't always help, and sp…

You wrote a program that rigs the input data in favor of one class in such a way that the classifier results are more or less uncorrelated with membership in that class. Am I understanding the code correctly? I don't see how you eliminated "human biases present in the data annotations" here. It seems like your program merely rigs the input in order to get the kind of output you like to see. Obviously you can tamper w…

Yes, in the last example you can see it as rigging the input data. I replicated the results of the experiment in the paper, and I made some remarks of this sorts in my own report and discussion.

The idea is that a sensitive parameter should not contribute to a decision, so, for example, the probability of group A or group B having access to a loan should be the same:

P(Loan|A) should be the same as P(Loan|B)

This is dangerous, as the discrimination metric is sensitive to biases present in the data. It can, effectively, make it easier for the discriminated group A to get a Loan in comparison to a person in group B in same situations. This happens if the bias is not in the annotations, but in the demographics of the dataset.

This is a really interesting problem, and I don't have answers for it.

Re: EU regulations on algorithmic decision-making and a “right to explanation”

#127

Is my reading of this law correct? It sounds like it outlaws all use of algorithms to evaluate people. The right to explanation is just for the rare exceptions where a "member state" authorizes it. But the legalese is difficult to parse, and no one else seems to get this impression. I think this is really bad. That's the majority of uses of machine learning. It also has a lot of economic value to predict things like…

> It sounds like it outlaws all use of algorithms to evaluate people. The right to explanation is just for the rare exceptions where a "member state" authorizes it. Who says that this is rare? That's a pretty standard way EU regulations are written to mean "Action A can only be allowed if conditions B are met". If the regulation is approved, member states take a look and change their laws accordingly. They could outl…

> If the regulation is approved, member states take a look and change their laws accordingly.

Small nitpick id I may: you seem to be confusing Regulations with Directives, which must get transcribed within a certain timeframe in national law. Regulations apply directly - no transcription needed.

Also: either way, EU law has primacy over national law.

Re: EU regulations on algorithmic decision-making and a “right to explanation”

#128

Earlier quoted context omitted.

> It sounds like it outlaws all use of algorithms to evaluate people. The right to explanation is just for the rare exceptions where a "member state" authorizes it. Who says that this is rare? That's a pretty standard way EU regulations are written to mean "Action A can only be allowed if conditions B are met". If the regulation is approved, member states take a look and change their laws accordingly. They could outl…

> If the regulation is approved, member states take a look and change their laws accordingly. Small nitpick id I may: you seem to be confusing Regulations with Directives, which must get transcribed within a certain timeframe in national law. Regulations apply directly - no transcription needed. Also: either way, EU law has primacy over national law.

Thanks for pointing this out, my post was not entirely clear on that matter. This proposed regulation does in fact contain some directive-like clauses with regards to the issue at hand. In this specific case they require the member states to disallow certain practices in their national law.

It's also not uncommon for member states to enact laws dealing with the effects of a (directly applicable) regulation.

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