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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”

#101
post #45

Earlier quoted context omitted.

When mentioning this, I find it helpful to include this page: https://www.ftc.gov/faq/consumer-protection/get-my-free-cred... If you are one of the people just finding out about this today, and you Google "free credit report", you come up with a loooot of bad, scammy links. This is how you get your legally-mandated, really free, annual credit report, starting from an ftc.gov address so you know it's really the right…

Credit Karma is legit and free. Also gives you your credit score and credit monitoring (all free). (Disclaimer: I work there).

So how do they make money? Who pays you?

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

#102

Earlier quoted context omitted.

If you can come up with an algorithm that reproduces this conclusion - with accuracy even remotely close to the "racist" ones - banks will cross heaven and earth to pay you $billions. Unfortunately the only public analysis I'm aware of is from a blog using Zillow data: https://randomcriticalanalysis.wordpress.com/2015/11/22/on-t... This effect is reproduced in various walks of life, e.g. education. You'll be doing so…

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 for one of course do not believe that people from other regions/continents are somehow inherently worse or better.

But I do believe that certain elements of different cultures and different ways of social upbringing can have a lasting positive or negative effect on a person. (although who am I to judge what is positive or negative? I try not to do this, I just see differences)

If these things didn't affect how we later as adults view the world, interact with others and respond to different types of challenges in our life then you could expect that basically everyone around the world would have the same moral value system and beliefs about almost everything.

This is obviously not the case.

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

#103

Earlier quoted context omitted.

> 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. Because maximizing prediction accuracy is inherently unbiased. Bias is when the predictions made are inaccurate to the detriment of a group of people. If you had a prediction algorithm that functioned using time trav…

You entirely miss the point! The point is that in supervised learning for example, if you optimize prediction accuracy with respect to your human generated examples, you will get a model that exactly reproduces the racist judgment of the human that generated your training set.

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

#104
> We argue that while this law will pose large challenges for industry, it highlights opportunities for machine learning researchers to take the lead in designing algorithms and evaluation frameworks which avoid discrimination.

This sounds like a very slippery slope.

Let me get this straight, what the EU wants to do here. If I make a business that decides how to, say, make loans to people, and I have software that analyzes how they filled out an application or took a written/logical test or something I may have to:

1) Disclose the findings and methodology of my proprietary algorithm

2) Potentially undo decisions that are deemed discriminatory

3) Thereby potentially be forced to choose who I do and do not do business with

Some regulations are good, they help us avoid things like moral hazards. Some regulations, however, act more like power grabs that take away freedom from individuals and businesses.

I'm sorry to say but this sort of thing sounds like the latter.

There are some uncomfortable facts about people and society that algorithms will uncover, and that people will try using to avoid making mistakes. Here's a poignant example, albeit with much less sophistication: http://www.nytimes.com/2015/10/31/nyregion/hudson-city-bank-...

> Instead, some officials say, some banks have quietly institutionalized bias in their operations, deliberately placing branches, brokers and mortgage services outside minority communities, even as other banks find and serve borrowers in those neighbourhoods.

Alright sure. I think we all know it's common sense to not open a jewellery store in inner city Chicago. The local clientele would not be able to afford such products and services, and that's enough of an argument, it's probably business school 101. Just like how you're not going to find a good market for a food delivery startup in Middletown, USA. The economics probably don't work. EDIT - and if you think these businesses are wrong, and they should be in these communities, let their competitors eat their lunch!

But it's viewed as discriminatory. Back to the paper, it's discussing essentially the same thing: it's the natural evolution of "don't start a jewellery store in inner city Chicago" turning into "don't start a jewellery store in neighbourhood where access to urban center is less than X, available nearby 3-lane highways is less than Y, percent of women from 18-29 is less than Z, proportion of people from culture W who are in the bottom quartile of people who buy jewellery is more than V, ...".

The solution here isn't to regulate and force people to open jewellery stores in inner city Chicago. The solution, if we really want to bring diamonds to Englewood, is to drastically reduce the poverty and crime rates so that businesses will want to sell there (https://en.wikipedia.org/wiki/Englewood,_Chicago#Socioeconom...).

And if you think having access to a local business is a "right", then you must concede that such a public good should be provided by a central government of some type. And then you have to ask yourself, do you want the government managing your bank loans, or would you rather have private companies compete on prices and service? If you want the latter, then you need to accept that certain things are just going to arise (or not arise) naturally.

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

#105
post #77

Good luck arguing the 'why' when you have a trained neural network and all you have is the network and weights. On a more serious note, I love transparency but again, this is an overeager regulation (not surprising from the EU). You almost never get the true reasons for being rejected, be it in an interview or for a credit, etc. You have to figure that out yourself from the often very vague rejection letter. Our mind…

If all you have is a network and weights, you have a black box. While it might be magic and make the right decision most of the time, we need to look in and see why those decisions are being made, somehow.

This generally basically means seeing how the net was trained, and its initial conditions. If you threw away your training methodology and data set, why should anyone trust that your algorithm can make suitable decisions? Are we to assume that nobody who writes AIs which are making influential decisions has vested interests?

If you look for example at common law; we have a system where decisions by judges are binding, and future equivalent cases are required to uphold the previous decisions. The only way this system can work of course, is if someone is keeping a record of the previous decisions. The alternative is we'd have a system of judgement based on heresay, and we can have about as much confidence in it as asking a random person on the street to make the decision.

The technical challenge is really about storage and retreival. If we know that discarding training data makes our neural networks "unaccountable for their decisions", then our technical requirement must be that we store the training data in its entirity, such that we can look back and maybe glimpse at why a neural network may behave as it has.

For example, I might create an AI which is used to decide whether to give someone a mortgage. I could have millions of samples as training data, but I might chose to sort the training data by race, and begin training a network initially so that non-white people who have been rejected mortgages initially overtrain the network to correlate race with rejection, then use a limited sample of white man, mostly accepted mortgage requests to train it the other way.

Of course this is extreme, and an expert who looked at such data set would quickly notice that the AI is unfit for purpose, and blatantly racist. But he needs the training set to even have a chance of concluding that. Without it, he has effectively random numbers which tell him next to nothing.

These laws aren't meant to stifle innovation or economic benefits, but only to ensure that fair treatment is practiced in their development. As far as I see it, if you have a neural network, a sound justification of its design and the methodology you used, combined with a complete data set and training set which can be analysed for biases, then there's no reason these regulations should get in your way.

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

#106

Earlier quoted context omitted.

> The point is that in supervised learning for example, if you optimize prediction accuracy with respect to your human generated examples, you will get a model that exactly reproduces the racist judgment of the human that generated your training set. In which case you aren't optimizing prediction accuracy. Prediction accuracy is measured by whether the predictions are true. If you have bias in the predictions which d…

You need to think about how we measure truth and even what truth is. In machine learning we tend to assume the annotations and labels are "true" and build a system towards that version of the "truth". > Prediction accuracy is measured by whether the predictions are true. The more I think about this sentence, the less sense it makes. Prediction accuracy can only be measured against records of something, and that recor…

> Prediction accuracy can only be measured against records of something, and that record will be a distortion and simplification of reality.

Prediction accuracy can be measured against what actually happens. If the algorithm says that 5% of people like Bob will default and you give loans to people like Bob and 7% of them default then the algorithm is off by 2%.

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

#107

Earlier quoted context omitted.

> The point is that in supervised learning for example, if you optimize prediction accuracy with respect to your human generated examples, you will get a model that exactly reproduces the racist judgment of the human that generated your training set. In which case you aren't optimizing prediction accuracy. Prediction accuracy is measured by whether the predictions are true. If you have bias in the predictions which d…

You have described the reason why many of us don't consider maximizing profit to be a desired behavior in all circumstances.

I think you misunderstand. In this case for once the profit-maximizing thing is the thing we want them to do and vice versa.

You can make a legitimate objection if the algorithm predicts that 20% of black people will default on their loans and in reality only 10% of black people default on their loans. But if the algorithm is doing that then it's losing the bank money. They're giving loans to some other less creditworthy people instead of those more creditworthy people, or not giving profit-generating loans at all even though they have money to lend. A purely profit-motivated investor is not interested in that happening.

But if it happens that disproportionately many of some group of people are in actual fact uncreditworthy, giving the uncreditworthy people credit anyway is crazy. It's the thing that caused the housing crisis. An excessive number of them will default, lose the lender's money and ruin their own credit even further. It only hurts everybody.

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

#108

Earlier quoted context omitted.

A fair point, but as a society, we have decided that racial discrimination is not a valid mechanism for banks to profit by. That does result in everyone paying a bit more in interest as the risk pool is larger, but a acceptable tradeoff. In terms of gaming, verification is just as important as scoring If the data you have going into to the system is rigged, and income is not being properly validated, bad things will…

As a society we have directed banks to make bad loans to blacks and charge non-blacks extra to make up the difference? I'd be surprised if even 10% of people know this decision was made. Also, what makes it acceptable to engage in this form of surreptitious wealth redistribution on racial lines?

Not being a racist makes it acceptable to not take race or a surrogate for race into condition for a loan ;-)

(Please don't take that the wrong way. I am not accusing anyone of racism. Simply stating that at some points our ideals is more valuable then an additional point of profits for the bank).

Disparate impact and it's use in credit scoring is mostly governed by Equal Credit Opportunity Act (ECOA), but most of the banks I am aware of go steps further in ensuring that disparate impact does not occur.

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

#109

Earlier quoted context omitted.

I think the case of zip code based discrimination is even named. It's called redlining, and is being used in machine learning world to describe indirect discrimination based on certain attributes (eg. discriminate people based on their zip code, a zip code with mostly black population for example)

I personally draw the line of acceptable and unacceptable discrimination at "if the disadvantaged person can change that aspect". Skin color cannot be changed, but zipcode certainly can. There is no rule I'm aware of that restricts visible minorities from living in rich neighbourhoods.

Except of course of the fact that visible minorities can not in fact afford to live there, or get a loan to live there, because they currently live in a poor neighborhood.

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

#110

Disclaimer - I work in the financial services industry. This is nothing new in most financial service machine learning algorithms have to meet these requirements. This is why why supervised analytics are more popular, and one of the reason why algorithms such as credit scores are typically generated and structured as scorecards with explicit reason codes for each independent characteristic, variable or vector. This i…

> It's my PoV that It's not predictability versus red-tape, and the people trying to do unaccountable analytics in this space are (perhaps in-advertedily) perpetuating racism. I was with you until that last line. It is not racism to provide better credit conditions to groups that demonstrably have a lower risk of defaulting on a credit. It would be racism if you offer worse conditions to a certain group without any r…

You said: I was with you until that last line. It is not racism to provide better credit conditions to groups that demonstrably have a lower risk of defaulting on a credit.

It absolutely is, under ECOA, which explicitly maintains that you may not discriminate by race for loans.

And btw, as someone very familiar with credit scoring once mentioned to me, a small part of the reason the credit scores do not take into account what you currently make is that ability to pay is often a very poor predictor for willingness to pay. this burned a lot of people in 2006/2007 when it was assumed a good credit score justified a big loan, without any proper verification of income, assets and liabilities.

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