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comment by Dagon · 2016-08-05T18:02:26.987Z · LW(p) · GW(p)
I think there's a fundamental goal conflict between "fairness" and precision. If the socially-unpopular feature is in fact predictive, then you either explicitly want a less-predictive algorithm, or you end up using other features that correlate with S strongly enough that you might as well just use S.
If you want to ensure a given distribution of S independent of classification, then include that in your prediction goals: have your cost function include a homogeneity penalty. Not that you're now pretty seriously tipping the scales against what you previously thought your classifier was predicting. Better and simpler to design and test the classifier in a straightforward way, but don't use it as the sole decision criteria.
Redlining (or more generally, deciding who gets credit) is a great example for this. If you want accurate risk assessment, you must take into account data (income, savings, industry/job stability, other kinds of debt, etc.) that correlates with ethnic averages. The problem is not that the risk classifiers are wrong, the problem is that correct risk assessments lead to unpleasant loan distributions. And the sane solution is to explicitly subsidize the risks you want to encourage for social reasons, not to lie about the risk by throwing away data.
Replies from: Slider, Stuart_Armstrong↑ comment by Slider · 2016-08-18T14:30:42.584Z · LW(p) · GW(p)
"If you want accurate risk assessment, you must take into account data (income, savings, industry/job stability, other kinds of debt, etc.) that correlates with ethnic averages."
While not strictly true this is true in essence. The failure point is telling though. What you need is to make generalization that are more general than single individuals. Why that categorization dimensions needs to be ethnicity is not forced at all. Why it would not be gender? Why is it not that you have a certain gene?
When you take such a grouping of indivudals and say that "this average is meaningfull to the decision that I am going to make" that is no longer strictly need.
In dissocaited theorethical talk you could argue and backup as some groupings being more meanignful than others. But the whole discriminatory problems come from people applying a set of groupings that are just common or known without regard to the fit or justifiabliy for he task at hand. That is we first fix the categories and then argue about their ranks rather than letting rankings define categories.
↑ comment by Stuart_Armstrong · 2016-08-09T13:32:46.565Z · LW(p) · GW(p)
Redlining seems to go beyond what's economically efficient, as far as I can tell (see wikipedia).
Redlining (or more generally, deciding who gets credit) is a great example for this. If you want accurate risk assessment, you must take into account data (income, savings, industry/job stability, other kinds of debt, etc.) that correlates with ethnic averages.
Er, that's precisely my point here. My idea is to have certain types of data explicitly permitted; in this case I set T to be income. The definition of "fairness" I was aiming for is that once that permitted data is taken into account, there should remain no further discrimination on the part of the algorithm.
This seems a much better idea that the paper's suggestion of just balancing total fairness (eg willingness to throw away all data that correlates) with accuracy in some undefined way.
Replies from: Dagon↑ comment by Dagon · 2016-08-10T06:44:02.014Z · LW(p) · GW(p)
I may have been unclear - if you disallow some data, but allow a bunch of things that correlate with that disallowed data, your results are the same as if you'd had the data in the first place. You can (and, in a good algorithm, do) back into the disallowed data.
In other words, if the disallowed data has no predictive power when added to the allowed data, it's either truly irrelevant (unlikely in real-world scenarios) or already included in the allowed data, indirectly.
Replies from: Stuart_Armstrong↑ comment by Stuart_Armstrong · 2016-08-10T19:09:40.079Z · LW(p) · GW(p)
The main point of these ideas is to be able to demonstrate that a classifying algorithm - which is often nothing more than a messy black box - is not biased. This is often something companies want to demonstrate, and may become a legal requirement in some places. The above seems a reasonable definition of non-bias that could be used quite easily.
comment by Lumifer · 2016-08-05T14:29:55.486Z · LW(p) · GW(p)
I'm not sure of the point of all this. You're taking a well-defined statistical concept of independence and renaming it 'fairness' which is a very flexible and politically-charged word.
If there is no actual relationship between S and Y, you have no problem and a properly fit classifier will ignore S since it does not provide any useful information. If the relationship between S and Y actually exists, are you going to define fairness as closing your eyes to this information?
Replies from: Stuart_Armstrong↑ comment by Stuart_Armstrong · 2016-08-09T13:50:39.795Z · LW(p) · GW(p)
I'm reusing the term from the paper, and trying to improve on it (as fairness in machine learning is relatively hot at the moment).
If the relationship between S and Y actually exists, are you going to define fairness as closing your eyes to this information?
That's what the paper essentially does, and that's what I think is wrong. Race and income are correlated; being ignorant of race means being at least partially ignorant of income. I'm aiming for something like "once you know income (and other allowable variables) then race should not affect the decision beyond that".
Replies from: Lumifer↑ comment by Lumifer · 2016-08-09T16:50:17.018Z · LW(p) · GW(p)
What are "allowable" variables and what makes one "allowable"?
I'm aiming for something like "once you know income (and other allowable variables) then race should not affect the decision beyond that".
That's the same thing: if S (say, race) does not provide any useful information after controlling for X (say, income) then your classifier is going to "naturally" ignore it. If it doesn't, there is still useful information in S even after you took X into account.
This is all basic statistics, I still don't understand why there's a need to make certain variables (like race) special.
Replies from: Stuart_Armstrong↑ comment by Stuart_Armstrong · 2016-08-10T19:27:12.520Z · LW(p) · GW(p)
As I mentioned in another comment, the main point of these ideas is to be able to demonstrate that a certain algorithm - which may be just a complicated messy black box - is not biased.
I still don't understand why there's a need to make certain variables (like race) special.
a) Because many people treat variables like race as special, and there is social pressure and legislation about that. b) Because historically, people have treated variables like race as more relevant than it is economically efficient for them to do so. c) Because there are arguments (whose validity I don't know) that one should ignore variables like race even when it is individually economically efficient not to. eg cycles of poverty, following of social expectations, etc...
A perfect classifier would solve b), potentially a), and not c). But demonstrating that a classifier is perfect is hard; demonstrating that a classifier is is fair or unbiased in the way I define above is much easier.
What are "allowable" variables and what makes one "allowable"?
This is mainly a social, PR, or legal decision. "Bank assesses borrower's income" is not likely to cause any scandal; "Bank uses eye colour to vet candidates" is more likely to cause problems.
From the legal perspective, it's probably quite simple. "This bank discriminated against me!" Bank: "After controlling for income, capital, past defaults, X, Y, and Z, then our classifiers are free of any discrimination." Then whether they're allowable depends on whether juries or (mainly) judges believe that income, .... X, Y, and Z are valid criteria for reaching a non-discriminatory decision.
Now, for statisticians, if there are a lot of allowable criteria and if the classifier uses them in non-linear ways, this makes the fairness criteria pretty vacuous (since deducing S from many criteria should be pretty easy for non-linear classifiers). However, the perception of fairness is probably going to be what's important here.
Replies from: Lumifer↑ comment by Lumifer · 2016-08-11T15:00:09.027Z · LW(p) · GW(p)
the main point of these ideas is to be able to demonstrate that a certain algorithm - which may be just a complicated messy black box - is not biased
If you're looking to satisfy a legal criterion you need to talk to a lawyer who'll tell you how that works. Notably, the way the law works doesn't have to look reasonable or commonsensical. For example, EEOC likes to observe outcomes and cares little about the process which leads to what they think are biased outcomes.
Because many people treat variables like race as special ... social pressure ... more relevant than it is economically efficient for them to do so ...
Sure, but then you are leaving the realm of science (aka epistemic rationality). You can certainly build models to cater to fads and prejudices of today, but all you're doing is building deliberately inaccurate maps.
I am also not sure what's the deal with "economically efficient". No one said this is the pinnacle of all values and everything must be subservient to economic efficiency.
From the legal perspective, it's probably quite simple.
I am pretty sure you're mistaken about this.
the perception of fairness is probably going to be what's important here
LOL.
I think this is a fundamentally misguided exercise and, moreover, one which you cannot win -- in part because shitstorms don't care about details of classifiers.
Replies from: Stuart_Armstrong↑ comment by Stuart_Armstrong · 2016-08-11T20:46:35.854Z · LW(p) · GW(p)
Do you not feel my definition of fairness is a better one than the one proposed in the original paper?
Replies from: Lumifer↑ comment by Lumifer · 2016-08-11T20:51:42.290Z · LW(p) · GW(p)
I feel this all is a category error. You're trying to introduce terms from morality ('fairness') into statistics. That, I'm pretty sure, is a bad idea. And the word 'bias' already has a well-defined meaning in stats.
If you want to introduce moral judgement into your results, first construct a good map, and then adjust it according to taste. At least then you have a better chance of seeing the trade-offs you're making.
comment by Petter · 2016-08-15T07:23:01.581Z · LW(p) · GW(p)
Looks like a solid improvement over what’s being used in the paper. Does it introduce any new optimization difficulties?
Replies from: Stuart_Armstrong↑ comment by Stuart_Armstrong · 2016-08-15T09:53:40.944Z · LW(p) · GW(p)
I suspect it makes optimisation easier, because we don't need to compute a tradeoff. But that's just an informal impression.
comment by Slider · 2016-08-18T15:11:23.375Z · LW(p) · GW(p)
Repeating a scenario from long ago.
You have a village that is pested by bees but also farms crops. Lets hypothetically blow it out of proprotino and say that a certain number of people die from bee-sting and a certain number die from starvation.
And lets say that bees poolinate plants and there are also non-poisonous pollinators around (such as maybe butterflies).
Somebody see a small flying insect that has yellow and black stripes in it. He argues that because it looks like a bee and bees frigging kill people we should swat it immidetly. Now considred the counterargument of someone that knows that a non-poisonouns bee mimic also lives nearby. And then let is be clear that if they swatted eveything that looked like a bee there would be singficantly less pollinators left to make the harvest yield good and related starvation deaths.
When someone is swatting a bee lookalike they are not probably thinking about the starvation deaths they are causing.
I think I left the matter in the state that just because a grouping gives information given no new information it doesn't lessen the amoutn of information that you do need. Even after not getting poisoned you need to find food. Thus everybody agrees that people should bother checking on what they are about to swat and should be about dilligent about swatting bees and should be dilligent about not swatting butterflies.
But what does not really stand for long is that somebody who summarily just swats all beelikes is being dilligent. ALL WHILE everybody agrees taht swattting is more right than not swatting. But coloration is not the only info you can deduce from bugs. But mimicry works because ti takes signficantly more cognitive effort to make those distinctions. Thus how right you use the easily avaible information doesn' tsave you from not gathering the hard to get infromation or how poorly you performed on it.
Thus the vilalge is better off educating people about the tellsigns of the mimics and that does not detract from the villages need to keep remembering that bees are poisonous
comment by bogus · 2016-08-05T21:21:26.445Z · LW(p) · GW(p)
It's not clear to me how this "fairness" criteria is supposed to work. If you simply don't include S among the predictors, then for any given x in X, the classification of x will be 'independent' of S in that a counterfactual x' with the exact same features but different S would be classified the exact same way. OTOH if you're aiming to have Y be uncorrelated with S even without controlling for X, this essentially requires adding S as a 'predictor' too; e.g. consider the Simpson paradox. But this is a weird operationalization of 'fairness'.
comment by bogus · 2016-08-05T21:21:09.950Z · LW(p) · GW(p)
It's not clear to me how this "fairness" criteria is supposed to work. If you simply don't include S among the predictors, then for any given x in X, the classification of x will be 'independent' of S in that a counterfactual x' with the exact same features but different S would be classified the exact same way. OTOH if you're aiming to have Y be uncorrelated with S even without controlling for X, this essentially requires adding S as a 'predictor' too; e.g. consider the Simpson paradox. But this is a weird operationalization of 'fairness'.
comment by bogus · 2016-08-05T21:20:38.255Z · LW(p) · GW(p)
It's not clear to me how this "fairness" criteria is supposed to work. If you simply don't include S among the predictors, then for any given x in X, the classification of x will be 'independent' of S in that a counterfactual x' with the exact same features but different S would be classified the exact same way. OTOH if you're aiming to have Y be uncorrelated with S even without controlling for X, this essentially requires adding S as a 'predictor' too; e.g. consider the Simpson paradox. But this is a weird operationalization of 'fairness'.
comment by bogus · 2016-08-05T21:20:17.888Z · LW(p) · GW(p)
It's not clear to me how this "fairness" criteria is supposed to work. If you simply don't include S among the predictors, then for any given x in X, the classification of x will be 'independent' of S in that a counterfactual x' with the exact same features but different S would be classified the exact same way. OTOH if you're aiming to have Y be uncorrelated with S even without controlling for X, this essentially requires adding S as a 'predictor' too; e.g. consider the Simpson paradox. But this is a weird operationalization of 'fairness'.
Replies from: Stuart_Armstrong↑ comment by Stuart_Armstrong · 2016-08-09T13:54:21.215Z · LW(p) · GW(p)
in that a counterfactual x' with the exact same features but different S would be classified the exact same way.
Except that from the x, you can often deduce S. Suppose S is race (which seems to be what people care about in this situation) while X doesn't include race but does include, eg, race of parents.
And I'm not aiming for S uncorrelated with Y (that's what the paper's authors seem to want). I'm aiming for S uncorrelated with Y, once we take into account a small number of allowable variables T (eg income).