Well. At the end of the day, the companies will pick thresholds and rates to maximize their profits, based upon the data that they have available. They don't know every detail of our personal lives -- which would also be kind of unsettling -- so they have to resort to a simplified picture. Simplifications are always prejudiced at the individual level but if the prejudice is reflected in the numbers, it's balanced. It may not feel perfectly just but at least it's more just than an imbalanced prejudice.
A real life example: In many (all?) countries, it's common to pay more for your car insurance if you are young, if you are male, or if you recently acquired your driver's license. One might frame this as a prejudice towards young, male drivers as being reckless. But statistically they are just that, so the prejudice is balanced (within a reasonable tolerance). It's unfair to the careful young, male driver, but well, life is not always fair.
Women's healthcare costs several times more than mens over their lifetime (and that's before factoring in pregnancy). Countries are just fine "equalizing" that even though that means that men pay way more for their health insurance than they should have to. How many people complain about this happening?
Collectivism is just fine when you are the one on the receiving end. Either we set absolute lines of discrimination across all industries or we disallow it entirely.
Generally we're most of us in society pretty comfortable with paying for the continuation of the species. There are exceptions to every rule, of course; some of us (not including me) are unhappy we pay to pave the streets, too.
The problem is that retirement is a Ponzi scheme and it's not clear if the current 3rd world children will care (or even have the means to care) about our 1st world problems in the future.
The difference with machine learning is that the model isn't designed by humans, through an actuarial process we can keep our brains wrapped around. It's a black box. We are OK attributing "crash risk" to young male drivers, because we can observe both that they are as a cohort statistically likely to crash and also understand why that would be the case. On the other hand, we're not comfortable with the idea that a computer program might spot some irrelevant correlation that determines that Armenians shouldn't get car loans --- and we're especially not comfortable with the idea that we might learn that this has happened only after years of unintended discrimination, because there is no straightforward way to interrogate the model for every possible bogus correlation it may have snagged on.
That's the computer science problem being worked on here.
I'm perfectly happy with algorithms detecting that certain people are more likely to be safe drivers than average, and giving them lower rates, and concentrating premiums on the groups more likely to be in accidents, even if I don't understand why Armenians (in your example) get in more crashes.
You've just defined away the problem. It's not that we're not OK with computers accounting for the idea that Armenians get into more car accidents (we might or might not be). It's that we don't know if that's actually the case, because ML-generated models aren't that simple (if they were, we wouldn't need ML, just a bunch of actuaries using R).
The distinction is that with young male drivers, we have two supporting classes of information:
* A clear statistical observation
* A conceptual understanding of why the observation is likely to be valid
With machine learning, we might have neither of these classes of information.
<shrug> What's the null hypothesis here? From my point of view, you're the one who defined into existence as a problem something that is not a problem.
I've run into many people who share this sentiment, and it always surprises me. I've never once met a person who was cheerful about experiencing algorithm-driven prevenge. I think it's very easy to sit back and say "I think we should let this happen!" if they've never knowingly experienced loss based on this phenomenon.
A great example is how very resentful many young white men of college age are that universities are requiring them to take sensitivity courses designed to reduce the instance of campus rape, but strictly speaking men of that age are the overwhelming majority of bad actors in that environment. Statistically and logistically speaking, it's smarter and cheaper to just require all men of college age to take courses reminding them that rape is not okay rather than dealing with the moral, legal and healthcare costs of the alternative.
In some cases, our relatively primitive algorithms pick up on correlations that should not be acted on because we're actively working to correct them. For example, it would be inappropriate to pre-reject job applications based on skin color if in a certain culture, it's less likely for that person to have a college degree.
Even if that insight is correct, it's usually part of something that society hopes to correct or that applicants should be given the benefit of the doubt about, otherwise very serious negative responses will emerge.
Acting on existing categories may reinforce. It may not. For now, it's a case-by-case basis we'll have to act on. Maybe one day, modeling techniques and data sources will become sophisticated and robust enough to make every decision for us. That day is not today.
> . I think it's very easy to sit back and say "I think we should let this happen!" if they've never knowingly experienced loss based on this phenomenon.
I'm sure that I've experienced increased costs based on this.
The issue is that I don't consider that the morality of forcing my will on other people depends at all on whether their current behavior is advantageous or disadvantageous to me.
> The issue is that I don't consider that the morality of forcing my will on other people depends at all on whether their current behavior is advantageous or disadvantageous to me.
Why on earth would I believe anyone who says this? I don't think humans are capable of such abstractness. It's not a matter of wanting to, biology itself is at odds with this mindset.
But also, there are many markets which are not effectively free markets. Insurance is a good one, and health service is an especially good one. In these cases, it's very dangerous to start agreeing that insurance agencies can start to pay "pass the puck" with human life.
No, bogus correlations and inaccurate conclusions are explicitly NOT the computer science problem being worked on here. You are trying to frame the problem as algorithms wrongly classifying people. That's just a standard statistics problem (called "overfitting") and has nothing to do with fairness.
The problem being worked on here is "what if Armenians shouldn't get car loans because they don't pay them back as much as other groups?" I.e., algorithms rightly classifying people leads to results that we believe are "unfair".
This research is illustrating that you can't simultaneously have accuracy and fairness. You need to explicitly decide how much accuracy you are willing to give up to get fairness. I.e. it's computing the tradeoffs needed to evaluate the ethical question: how many Armenian deadbeats should you extend credit to in order to be "fair"?
Go play with the simulation to see. The various fairness criteria all achieve lower than maximal profits.
I think what the research itself (as opposed to politics surrounding it) was about is summed up in the last paragraph:
A key result in the paper by Hardt, Price, and Srebro shows that—given essentially any scoring system—it's possible to efficiently find thresholds that meet any of these criteria.
The crucial thing is that this "credit score" doesn't actually predict defaults quite well, because blues who are 50% likely to default have higher score than oranges who are 50% likely to default. If you base decision on a simple credit score cutoff, the blue defaulters will screw you up because of their higher score. You can defend from this by tweaking cutoffs per-group to achieve profit maximization or various "fairness" goals. In a sense, it's an attempt at producing non-garbage out from garbage in.
And of course whether real-world implementations of this idea are "good" or "bad" is another can of worms altogether and varies case by case. They probably could have came up with some better examples than credit scores and labeling people by colors.
Let me repeat what I said: Go play with the simulation to see. The various fairness criteria all achieve lower than maximal profits.
The best predictor is one which is explicitly discriminatory based on race: it takes both FICO score and race into account. There are worse predictors which also discriminate explicitly based on race, but in some "fair" way.
Finally, the worst predictor throws away directly relevant racial information.
This is not about garbage input data - that's not the problem being addressed here at all. The input data is perfectly fine. The problem is just that the input data says "race discrimination is the best way to make money".
I think you are being deceived by your real-world presumptions about the fictional example somewhat unfortunately and misleadingly engineered by Google. From your previous post:
> The problem being worked on here is "what if Armenians shouldn't get car loans because they don't pay them back as much as other groups?" I.e., algorithms rightly classifying people leads to results that we believe are "unfair".
No. If you stop playing with simulation and look at the input data, Armenians don't pay back less. They pay exactly as much as Iranians except that Iranians consistently have higher FICO scores because Iran infiltrated FICO with their suckxnet(TM) worm which replaces R binaries with hacked versions. Or something like that :)
The general abstract idea is: you have some input "score" which is know to inaccurately predict the outcome, use the input score and measurements of its biases to produce more accurate prediction than naive threshold classifier would.
And yes, the other guys talking about women's healthcare costing more or men causing more traffic accidents got it wrong too. I somewhat arbitrarily responded to you because you said something about "the real computer science problem being worked on here" and then continued to talk about other things like everybody else.
The general abstract idea is: you have some input "score" which is know to inaccurately predict the outcome, use the input score and measurements of its biases to produce more accurate prediction than naive threshold classifier would.
Why don't you quote the place in the paper where they make accuracy go up, fix overfitting, or build an improved risk score? Or even just quote a place in the paper where the risk score is treated as anything other than an accurate black box?
They pay exactly as much as Iranians except that Iranians consistently have higher FICO scores because Iran infiltrated FICO with their suckxnet(TM) worm which replaces R binaries with hacked versions.
In the example provided in the paper (see Fig 7) that's explicitly NOT true. Blacks pay back their loans a lot less than asians/whites holding FICO fixed. For example, at a FICO score of 500, blacks pay back their loans 10% of the time while Asians do about 40%.
I.e., blacks have consistently lower FICO scores because they don't pay back their loans. Further, FICO score is biased in favor of blacks. If we made it more accurate we'd be actively discriminating against blacks. For example, a black person with a financial situation reflecting a FICO of 500 would have their FICO score lowered to approx 450 to reflect their higher default rate.
Did you even read the paper, or the linked article?
OK, I see what's the problem. By "higher score" I meant "higher score than the other group for given default risk", not "higher score than the other group on average". So, by my nomenclature, fig. 7.1 shows Blacks having "higher" (call it inflated) FICO scores because for every N an N% risk Black scores higher than an N% risk Asian. At the same time fig. 7.2 shows Blacks having lower scores in general, which must be because they default more, as you observed.
I think we can agree that using the same cutoff for both races gives less accuracy than using higher cutoff for Blacks and lower for Asians, for some values of "higher" and "lower". You are right that "race discrimination is the best way to make money", but at the same time I think I'm right that this happens because FICO score already is racist to begin with, which is the "garbage in" I talked about.
> This is also incorrect. The conclusions of the paper would remain valid if FICO were calibrated identically for all groups.
I guess you are right, this was just the first disparity I noticed in the orange/blue example and I got fixated on it. And yes, I haven't read the whole thing and my maths may be a bit rusty nowadays :)
Now I see that the problem they attempt to solve with "equal opportunity" is FICO's (in)ability to fish reliable borrowers out of the whole population. Currently, FICO identifies a small number of reliable Black borrowers whom they give high scores, plus there are many Blacks who would pay back diluted in a sea of unreliable borrowers with low scores. While in, say, Asians, the ratio of reliable borrowers who had been given high scores is higher (fig. 8).
I think the issue is a bit more nuanced than "algorithms rightly showing that fairness is opposite to profit".
In particular
> The best predictor is one which is explicitly discriminatory based on race: it takes both FICO score and race into account. [...] the worst predictor ["race blind"] throws away directly relevant racial information.
As I noted, this is a case of reverse bias directly compensating for FICO's bias. Max profit simply grants loans to all FICO score buckets whose default risk is sufficiently small to be worth it. If FICO vs risk was race-independent, "max profit" would use the same FICO threshold for every race and hence would be equivalent to "race blind".
Analogously, "max profit" could be equivalent to "equal opportunity" if FICO was better at finding Blacks who can pay and putting them in low risk buckets (high score) so that it becomes feasible and profitable for banks to grant them loans. I believe this is what authors meant by incentivising classifiers to improve accuracy and yes, I was wrong suggesting that they found a way to improve accuracy here by using the input classifier as a black box. Their ideas only compensate for the aforementioned race-biased score inflation, which isn't the entirety of the problem, and put some financial burden for some other mispredictions on banks/FICO to pressure them into getting their shit together.
I think trying to apply "equal opportunity" in the real world may indeed turn into handouts to people who can't pay, because <handwaving> it's possible that poor people are just hard to classify correctly and if certain ethnicities are poorer than others, they will appear to be given less opportunity even though actually it's simply poor people in general who are being given less opportunity. If FICO finds ways to classify poor Blacks better, it may turn out that applying the same solutions to other groups will improve their true positive rates too and hence "Black opportunity disadvantage" will stay.</handwaving>
But otoh, it also is possible that <handwaving> for some reasons Blacks are classified with less accuracy than Asians, which contributes to the lower overall true positive rate of the race.</handwaving> Authors appear to be assuming this possibility, though I don't think the distinction can be made from data presented in the paper alone.
TL;DR: I think you were oversimplifying things, and so was I :)
"We proposed a fairness measure that accomplishes two important desiderata. First, it remedies the main conceptual shortcomings of demographic parity as a fairness notion. Second, it is fully aligned with the central goal of supervised machine learning, that is, to build higher accuracy classifiers."
No, you are misunderstanding things. They aren't improving accuracy, reducing overfitting, or improving generalizability. If you think I'm wrong, feel free to cite the part of the paper where they claim to build a better FICO score (to go with their example).
You're misreading the meaning of "fully aligned." If you read the 3 paragraphs preceding that sentence, the authors are referring to organization incentives being "aligned."
If a computer program spots an irrelevant unhelpful correlation its generalization error will noticeable go up. If it is an irrelevant "helpful" correlation it means there is a problem with the data (such as leakage), not with the algorithm. If there is a problem with the data, all bets are of, both for black and white box models.
A blackbox model will probably not find that being an Armenian alone will lead to more crashes. Being non-linear in nature it will find interactions (young male Armenians are more likely to crash than young males in general). If the importance of such a feature is not significant enough to distinguish it from noise, then regularization may automatically remove it.
Even if we observe that 99 out 100 Armenians crash their cars, and you decline someone a loan, because he/she is Armenian, you may just have discriminated against the 1 Armenian who is a safe driver. Young male drivers who drive safely have a worse time getting loans, because their group (the set of young male drivers) spoiled it for them. So their only hope of getting a loan is you adding more features (like nationality), to be able to distinguish them as safe drivers, not removing them and lumping them into the status quo.
No, it's not at all the same. Those articles are about eliminating overfitting and improving accuracy. This article is about how much fairness constraints will reduce your accuracy/objective function.
Did you read the paper, or just the web page with the simulator? The loan simulator is a motivating example, not the entirety of the work. The work is a comparison between the basic approach of demographic parity and the authors contributions, which seeks to minimize the expense you seem to be talking about.
I read the paper a month ago, and I'm quite familiar with this field for my own reasons [1]. They attempt to minimize the expense I'm talking about. They don't reduce overfitting, improve accuracy, or even make any changes to the underlying predictive algorithm.
The simulator is a great illustration of exactly what they did; the entirety of the work is generalizing that to arbitrary predictors (subject to a few conditions) and bounding the accuracy penalty.
Feel free to cite the theorem improving accuracy if you disagree.
[1] One idea I'm kicking around is the following. Banks/others are legally required to issue bad loans for fairness. I suspect there is a lot of money to be made hacking this, I just haven't figured out how yet.
I saw a talk by a fintech company CTO who lend to people in European countries without reliable credit scoring. They used lots of data points, effectively a black box system.
But sometimes they loan to people they wouldn't normally loan to, just because that loan adds the kind of data they would otherwise not get, and they take a risk on default in exchange for more data. I'm assuming smaller loan amounts.
So I guess that is one way for lending institutions to say they are issuing "bad loans for fairness" while still benefiting from it in terms of getting more training data and making the model more robust.
I don't get the impression that most people are afraid of irrelevant correlations, but rather relevant ones that nevertheless disproportionately affect certain populations.
To use your Armenian example, it could be that while being Armenian doesn't actually affect your driving, a "true" model could still end up being bad for Armenians if being Armenian is correlated with the things that actually do affect crash risk.
But what about this: we don't need to solve all social ills every single time. What if we let the algorithm correctly decide crash risk, and if we notice that it unduly impacts Armenians and that's not an outcome we want, we via a separate channel compensate the Armenians? That is, acknowledge the fact that Armenians may crash more, but give them a government subsidy to offset the higher premiums, and work to bring the premiums down (i.e.: fix the underlying issues that being Armenian is correlated with).
It's related to something I've been thinking about lately with regard to minimum wage. I like the idea that everyone should have a livable income, but tying the implementation to businesses that have low wage jobs seems like mixing concerns. For example, my company doesn't have any minimum wage jobs, but shouldn't my company chip into this social ideal same as any other?
What if we let the businesses pay whatever the market will bear, and if we decide that as a social concern people should get more than that, we subsidize them from the government, which is wear this concern is coming from in the first place.
I think that people are afraid of conditions that one can't control, and are correlated with negative characteristics.
Lets say that I am male, and am young. Lets say that as a young male driver, I am likely to be a cause of an accident 20% of the time i.e. 1 in 5 young male drivers will cause an accident. Lets say as a young female driver, I am likely to be the cause of an accident 5% of the time.
So on the face of it, young male drivers are riskier, and should pay appropriately.
But lets say there was another measurable factor, such as a 'recklessness' score, which describes how recklessly you behave. And lets say that if you are 'reckless', you are likely to be the cause of an accident 90% of the time. And the maths works out that if you take the 'not reckless' group from men and women, they are equally likely to be the cause of an accident, i.e. there are more reckless men than women.
If you were to take the stats at face value, then a good proportion of people are being over and under-charged, because you are using a proxy measure rather than digging deeper for a root cause/don't have the data available. I think this is exactly what people are afraid of, businesses/people being lazy and making assumptions that are correlative, based on characteristics that one can't change.
No, that's not what this research is about at all.
This research is about what happens when combining the recklessness score with gender-based distributions provides more accurate results than recklessness alone. Many people consider this to be "unfair". This article shows how much profit you need to sacrifice to get fairness.
> That is, acknowledge the fact that Armenians may crash more, but give them a government subsidy to offset the higher premiums
I'm just incredibly impressed that you came up with a way to make this sound even worse.. can you imagine the government giving subsidies to Armenian drivers with good records because insurance companies are overcharging them due to the statistical performance of their category?
A tax benefit for being an outlier from the mean? My god. Not only does that sound like a terrible idea, but intensely complicated to organize in the general case.
And what happens when an individual is in multiple categories? What if only gay Armenians who listen to disco are the at-risk driving category? How do we aggregate the tax subsidies, multiplicatively, additively, ..? This sounds like an administrative nightmare, I congratulate you on your evil ingenuity.
I dont think most ML algos are as black box as you think, and if there were such an algo that regularly had a problem with finding "irrelevant correlations" then it wouldnt be a very good ML algo and people wouldn't use it.
You're conflating algorithms with data to some degree. The algorithm is responsible for finding the correlation, but whether that correlation is "irrelevant" depends entirely on the relationship between the training data and the full (presumably unknown) distribution over that set, as well as on fuzzy social interpretions (e.g., we may simply decide by fiat that race is an irrelevant feature for deciding mortgage approvals because we want to enforce that as true within the system).
In your example there could be deeper reasons for Armenians having higher crash rates. Let's say it is a combination of zip code, the food they are eating from a local Armenian Bell, and the phase of the moon.
However with a lot of ML classes we are told to look for the simpler explanation which would be nationality in this case, in the absence of collection of the real reasons.
Not sure how that can be gotten around. I'll read the paper.
So (assuming males are more costly to insure) either (a) females pay more than they should and are effectively subsidizing males or (b) males pay less than they should and the insurance companies will go broke..
Yes, female premiums went up to cover the cost of not having that factor in the model.
Fortunately I'm sure people are hard at work finding "proxy factors" which happen to map to pretty well gender but have an acceptable parallel explanation for why they're meaningfully descriptive of an applicants risk profile :)
Or (c) insurance companies still make positive returns on both male and female customers, and the regulation just reduces their overall profits without creating losses which must be subsidized by female customers.
(d) Insurance companies increase insurance premiums for every customer not in the young male demographic, blames "regulation". Results skyrocket. Management awards themselves seven digit raises to "keep attracting the best talent".
Which made the insurance for women, who are traditionally safer drivers because not taking unless risks, go up. Well facts can't be racist but they are at the same time.
Auto and life insurance are usually more expensive for men, but health insurance is usually more expensive for women. It would be interesting to see who comes out ahead overall.
But if you were a man who was an excellent driver, would you like it that you get to pay more just because you were born into a gender that was "more reckless"?
Plus, women want equal rights, so why not be equal in this, too?
I think for things like insurance, or healthcare taxes (the model adopted by most countries) it's fair to normalize the price for everyone. Otherwise, the system doesn't really work.
The whole purpose of insurance is to normalize the price. Without insurance, people who take the most risks with their health/driving would be more likely to pay (or at least owe) millions, while the health-conscious and risk-averse would generally pay much closer to $0 (until they get old or unlucky).
It's interesting that insurance premiums can act as behavioral incentives in some cases, but I doubt it has ever led someone to change their gender or race.
Risk analysis is just an implementation detail that aids price discovery in a competitive market. If an insurance provider can identify customers with lower risk than the consensus, they can offer lower premiums and attract that business. If they identify customers with higher risk, they can charge higher premiums and push that unprofitable business to a competitor.
Supposing this price discovery were perfect, customers who make a claim would have already paid premiums equal to the claim, and customers who never make a claim would pay nothing (plus fees in each case). This aspect of insurance obviously serves no core purpose. The purposeless of it is just hidden in the noise of uncertainty, and we see pricing differences emerge out of the uncertainty.
The actual purpose is to blunt the effect of catastrophic losses by spreading a smaller burden across a larger population. One caveat is that insurance can sometimes have the effect of "rewarding" risky behavior and "punishing" cautious behavior. Now, it's interesting that price differences can sometimes balance that by reversing the punishment/reward incentives, but only if prices are based on risk factors that the customer can actually control.
Since the 80's, the state of Montana has banned insurance underwriting based on gender. There have been attempts to reverse that law, but none have passed.
My male Montana cousins always had slightly cheaper insurance than I did, but my female Montana cousins more than made up for it.
Discrimination is far more nuanced than it first appears.
We accept that younger drivers pay more for car insurance. And that disabled people pay more for their health insurance. And that the government (in the UK) has a special business grants scheme for ethnic minority entrepreneurs.
Obviously discrimination depends on context. If a car insurance firm had a special policy for ethnic minorities, people would (rightly) be outraged. But in the context of a government intervention, based on the evidenced disadvantages that ethnic minorities face, discrimination is accepted.
>[...] concept called equal opportunity. Here, the constraint is that of the people who can pay back a loan, the same fraction in each group should actually be granted a loan.
This does not seem fair to me, because if this is applied then your race (group) would determine your credit score threshold which feels discriminatory to me.
I feel that, by definition, it is not discriminatory only if none of your attributes that you could be discriminated against (gender, race, ...) are taken into account at all.
Maybe the concept of 'equal opportunity' is just some compromise between discrimination and making less informed decisions.
I think it's useful to figure out why there are discrepancies between two groups.
For example, let's take blacks in the US. The data tells you that a black person is more likely to be a criminal than a white person. There are two possible reasons for this: (1) blacks are more prone to crime, or (2) blacks are more likely to live in circumstances that make them criminals. With access to only anecdotal data, I strongly believe that (2) is true, and that if you took into account enough circumstances (e.g. single parent, school district, income level, parents' wealth) you'd be able to remove race from your model and still arrive to the "equal opportunity" result. That way, you wouldn't discriminate based on race, but you would still help the people most needy of help (poor, uneducated, etc.). I think the same applies to colege applications and sex wage gap, which is why I strongly oppose any kind of affirmative action.
Life expentancy sex gap might be a different case. IIRC, men die earlier because of some behavioural/social tendencies (working dangerous jobs, supressing emotions, risky behaviour (speeding, smoking)), so maybe there are some non-discriminatory (or at least less-discriminatory) ways of determining life expectancy (e.g. testosterone level, job description, ...). However, there are also biological differences that seem very strongly linked to the quality of being male (i.e. the Y chromosome). E.g. prostate cancer is less lethal than breast cancer, and women are more likely to get MS than men. These particular examples suggest that men should live longer, but I'm guessing there might be other gender-specific ilnesses that might reduce their (our) expected lifespan. If that's the case, I don't think it's too discriminatory to have different insurance fees for different sexes.
In all such scenarios, however, there could still be broader societal goals that would override specific instances of (non-)discrimination. For exapmle, AFAIK women's health is more expensive (because of pregnancy), but having more children benefits everyone in the society (in the West), so it makes sense to "discriminate" against men by letting women pay less for their health insurance.
> The data tells you that a black person is more likely to be a criminal than a white person. There are two possible reasons for this:
I'd like to add a third possible reason for your consideration. Since "criminality" i.e. guilt of committing a crime is determined after a process engaging the law enforcement and justice systems, we have to examine whether there are inherent biases in those systems that result in skewed statistics. For instance, do police officers selectively target blacks for monitoring and investigation? Are blacks discriminated against in the courtroom as a result of procedure or human nature?
Using a unique data set we link the race of police officers who kill suspects with the race of those who are killed across the United States. We have data on a total of 2,699 fatal police killings for the years 2013 to 2015. This is 1,333 more killings by police than is provided by the FBI data on justifiable police homicides. When either the violent crime rate or the demographics of a city are accounted for, we find that white police officers are not significantly more likely to kill a black suspect. For the estimates where we know the race of the officer who killed the suspect, the ratio of the rate that blacks are killed by black versus white officers is large — ranging from 3 to 5 times larger. However, because the media may under report the officer’s race when black offic-ers are involved, other results that account for the fact that a disproportionate number of the un-known race officers may be more reliable. They indicate no statistically significant difference be-tween killings of black suspects by black and white officers. Our panel data analysis that looks at killings at the police department level confirms this. These findings are inconsistent with taste-based racial discrimination against blacks by white police officers. Our estimates examining the killings of white and Hispanic suspects found no differences with respect to the races of police officers. If the police are engaged in discrimination, such discriminatory behavior should also be more difficult when body or other cameras are recording their actions. We find no evidence that body cameras affect either the number of police killings or the racial composition of those killings.
As you're probably aware, black Americans are arrested for marijuana possession far more frequently than whites. You may also know that there's not much evidence that black people consume marijuana with greater regularity than whites do.
...And this is a uniform phenomenon. It's not that some states treat the races equally and others treat them really unequally. Only in Hawaii are the rates even close to equal, and that's biased by the fact that blacks make up only 1.6 percent of the population. In the state with the second-lowest disparity, Alaska, blacks are 1.6 times more likely to be arrested. In the state with the biggest, Iowa, blacks are 8.34 times more likely to be arrested. D.C. has the second biggest; in the District, blacks are 8.05 times more likely to be arrested.
Do the findings control for which group uses more in public? My experience living in Chicago and Waterloo, Iowa (the "blackest" city in Iowa--http://www.iowadatacenter.org/Publications/aaprofile2016.pdf) suggests that this disparity alone could explain the disparity in arrest rates. It's quite possible that there's a cultural difference in drug abuse that could skew the results.
There are a lot of different questions here, though. This study only addresses one.
There are lots of ways for the system to be racially skewed/biased without being a product of personal bias - so many that I think the focus on "racist police" makes it hard to recognize a lot of easily-provable problems.
Stop-and-frisk is my go-to example of a system that produces bias regardless of the race or biases of the officers involved. In theory, it's an efficient use of limited police resources, it can be implemented race-blind, and it "only catches criminals". There's room to talk about harassment of non-criminals, but at least regarding the people who get arrested its an understandable idea.
In practice, criminality is a product of conviction. Stop-and-frisk mostly catches 'possession' crimes like personal-use drugs and illegal weapons (and since a 3-inch pocketknife is illegal in many cities, we shouldn't mistake this for violent intent). As a result, living in a stop-and-frisk area massively increases your odds of being charged with a low-grade crime - it's not as though carrying marijuana or a Leatherman is rare among un-policed groups. Even if you attempt a crude race-blind implementation, like policing based on neighborhood crime rate, you end up with a vicious cycle where crime rates are high because enforcement is high.
So I think we do a disservice when we limit our discussion and investigation to officer bias. Even when it's not present, it's still easy to build an unequal system.
I'll defend the parent comment by observing that we can reproduce this effect with non-judicial metrics, like "murder rate in majority foo-race communities". Assuming the reporting rate for murders is very high across the board, this escapes bias in both policing and conviction rates, and sends us back to other explanations.
Even so, the policing/judicial question is really important.
There are some sociology results (I haven't seen a replication failure?) suggesting that not only race but stereotypical appearance within race influences sentence duration. There are all kinds of low-level-illegal practices whose effective criminality is shaped entirely by policing - think of any "side hustle", where selling loose cigarettes leads to lots of charges, but selling moonshine (the closest analogy I can think of) leads to relatively few.
More obviously, incidental discovery and stop-and-frisk policing produce wildly imbalanced charge rates. Walking down a city street with a pocketknife and pot is always illegal, but the odds of it being a crime depend heavily on who you are.
I'm not saying anything new, but it's a soapbox worth getting up on. It's a big deal in race, but it's also worth remembering the power of selective policing whenever we talk about criminalizing something widespread.
>> I'll defend the parent comment by observing that we can reproduce this effect with non-judicial metrics, like "murder rate in majority foo-race communities". Assuming the reporting rate for murders is very high across the board, this escapes bias in both policing and conviction rates, and sends us back to other explanations.
A community has a fair amount of power in deciding where crime happens. The police (by policy or culture) can choose to push illegal activity to certain neighborhoods. If the police come down hard on drugs, prostitution, etc. in white neighborhoods, then that activity will move to non-white neighborhoods. The problems that come along with increased illegal activity, including higher murder rates, will be concentrated in non-white neighborhoods.
Blacks are disproportionally policed, leading to higher likelyhoods of being accused of a crime.
Blacks are convicted more often than white people arrested for the same crime.
When black people are convicted of a crime, they are more likely to be sentenced to incarceration compared to whites convicted of the same crime.
Blacks generally get harsher sentences when compared to whites who've commited the same crime.
https://www.americanprogress.org/issues/race/news/2012/03/13...http://www.huffingtonpost.com/kim-farbota/black-crime-rates-...
>if you took into account enough circumstances (e.g. single parent, school district, income level, parents' wealth) you'd be able to remove race from your model and still arrive to the "equal opportunity" result. That way, you wouldn't discriminate based on race, but you would still help the people most needy of help (poor, uneducated, etc.).
This is pretty much the only way to help people who were born addicted to meth inside a trailer in the mountains who also happen to be blessed with being white.
> if you took into account enough circumstances (e.g. single parent, school district, income level, parents' wealth) you'd be able to remove race from your model and still arrive to the "equal opportunity" result.
The problem is, when given access to a large number of classifiers, some of which have inevitably been affected by a pre-existing racial bias, a black box machine learning algorithm will likely become discriminatory as well if race is not in some way represented and equalized.
For instance, many justice systems in the U.S. use machine learning software to determine the likelihood that a criminal will reoffend, and use that prediction to determine sentencing. Race is never used explicitly as a classifier, but the program ended up being significantly more likely to rate blacks as more likely to reoffend [1]. Classifiers like "had parents with previous criminal convictions" can be misleading when blacks are more likely to be convicted for the same crime as whites. It doesn't mean that the white person's parents didn't engage in criminal activity or other reprehensible behavior that might cause their child to become a violent, repeat offending criminal - just that they were able to get away with it more easily because of a biased system.
Machines end up just as biased as the data they've been trained on, so if we are going to use computers to judge things that have such a significant impact on people's lives, we can't risk racism slipping through the cracks.
The problem is, when given access to a large number of classifiers, some of which have inevitably been affected by a pre-existing racial bias, a black box machine learning algorithm will likely become discriminatory as well if race is not in some way represented and equalized.
This is simply not true. Black box machine learning algorithms will have the tendency to correct bias in their inputs. Insofar as they do systematically deliver wrong answers, this is actually called "variance" and has no particular sign. It's just as likely to be biased in favor of $protected_class as against that class.
Also, you do know that Pro Publica's R script actually found no bias, right? The bias was actually in the selection of anecdotes in their article, which obscured the fact that their statistical analysis could not reject the null hypothesis.
But how do you do that? Race is baked into a lot of the attributes your classifier is going to find informative. Is someone a good credit risk? To decide, you look at features like past payment history, available balance, zip code, etc.
Past payment history: if you're black, prior discriminatory behaviors may have limited your ability to open credit accounts, and thus you have less history to go on. Available balance has the same reasoning. Zip code correlates with race.
I'd hope very few people are including an "is_black" feature in their classifiers. If you eliminate anything that is informative towards race though, you're likely going to have a classifier that doesn't work very well.
The problem is that we have datasets that have arisen from a history that included significant racism, both overt and latent. There is no way to separate those effects from the data. You either get an "optimal" classifier that is racially biased in ways we don't want, or you get one that intentionally gives up some perceived performance in favor of fairness.
Why should it not be fair? Under this constraint, the perfect equal opportunity model would be a model that accurately represents who will pay back a loan.
In practice, the first variables to be used for classification are the ones that have a biggest effect. Then you're going to use them (from order of importance) as eliminatory or classificatory
> because if this is applied then your race (group) would determine your credit score threshold which feels discriminatory to me.
But the opposite is also discriminatory, which is what the article is showing. Because then you're using the same ruler to evaluate different groups, and of course the minority person with 2 jobs can't match the credit score of an Ivy-League educated WASP
For more information about "ethics and algorithms", read: "Weapons of Math Destruction" [1] or at least listen to the EconTalk podcast between the author and host Russ Roberts [2]
> Restricting to equal opportunity thresholds transfers the "burden of uncertainty" away from these groups and onto the creators of the scoring system. Doing so provides an incentive to invest in better classifiers.
Personally, I find this as the key outcome here. The accountability is on the people/systems who make the decision and that leads to an appropriate incentive. Win-win as a start.
I've played around with this concept, trying to replicate some previous work [1].
It's a sensitive topic, because sometimes we're actually tampering with the data, trying to eliminate known human or selection biases.
The first defence against discrimination is, in my honest oppinion, for everyone working with data to be aware of these problems. To know that, besides ROC, precisions and recalls, we should measure the impacts of the models in sensitive demographics (gender, race, nationality, sexuality).
And one of the things that I learned (in [1]) is that, even if you're carfull with the features you use, you might still have a negative effect.
> And one of the things that I learned (in [1]) is that, even if you're carfull with the features you use, you might still have a negative effect.
One needs to understand how these features interplay with each other. For example, you may not directly use a protected class feature (race) to make your prediction but you might end up using a secondary or tertiary variable (like location) to end up learning a protected class feature due to statistical correlations.
What strikes me as interesting in this example is that it seems to assume the classifier is run once and that its decisions have no effect on future decisions.
I would imagine that if we are separating people into groups based on demographic or social factors to make decisions, then those decisions may have an impact on that entire group. (In this example, maybe granting more loans to the blue group alters the group characteristics and leads to higher profit in the long term despite higher immediate risks).
Is there an area of ML research that considers this kind of concept?
I don't think there is though it may fall under ethical considerations of AI or economics of AI. Would be hard to do a study since AI has really only been around for less than 50 years.
But how can we find out if a company uses ML in a non-discriminating way? If we cannot see it, and check it, then there is no incentive for companies to use it.
My guess is that at most companies spending time on making an algorithm non-discriminating will be viewed as a waste of time and money.
I work for a big bank... As long as people freak out when banks are found to be discriminating they will do their best to not discriminate.
Banks are built on trustworthiness. Having your bank's name in the headlines for discriminatory practices can have a severe negative impact on trustworthiness. They have a whole teams of people devoted to this topic and every year at most banks every employee has to learn about, "reputational risk."
Having worked at big banks for over a decade now I am 90% certain that discrimination by banks at this point is primarily due to carelessness.
That is a story that banks like to believe about themselves, but it's worth considering that emergent discrimination (and the broader bucket of emergent malfeasance) is a property of all complex systems, not just machine learning. So, for instance, Wells Fargo needed to do more than just hope that its trustworthiness would survive its incentive systems.
Not taking steps to prevent emergent discrimination or malfeasance is careless. Do you think the board of directors wants to find out that the CEO intentionally ignored calls to the ethics hotline? That he didn't take steps to ensure whistleblowers weren't punished?
The consequences of these things are very bad for stock prices! This is doubly true for banks which take reputational risk far more seriously than most businesses.
Like the line in Fight Club [1] about recalls it is a simple formula:
Only if the cost * probability of a PR disaster outweighs the cost of a poor credit risk model, is a bank economically incentivized to not discriminate.
Then again, even if you take care to not directly discriminate, you will probably indirectly discriminate. For instance, in your credit risk model, remove the `gender` column from the features, and use the remaining features to try to predict it. If performance is better than random guessing, you are using proxy features (like `income`) to discriminate on `gender`. You will find that nearly every feature you use is correlated with `race`. Now what? Throw away all these features and let your competition eat your lunch?
Along the lines of what Pedro Domingos said [2], you can not solve the problem of discrimination by making poorer performing machine learning models that adhere to your view of what is ideal. Discrimination won't disappear because you made a model that makes you feel good. Want no discrimination of women? Work on closing the wage gap. Don't cripple your statistically correct ML models or sweep the discrimination under the rug, covered by correlated variables.
It is not so much carelessness, as it is the nature of the beast. And banks remain in business by how much they can trust their customers first and foremost, trustworthiness by customers is a second (and customer trust is very much malleable: It is the perception of trust, not objective trust like "can we trust this customer to pay back their loan").
It also depends on how you (mathematically) define "fairness" [3]. You can define fairness in ways that still allow you to discriminate.
[1] A new car built by my company leaves somewhere traveling at 60 mph. The rear differential locks up. The car crashes and burns with everyone trapped inside. Now, should we initiate a recall? Take the number of vehicles in the field, A, multiply by the probable rate of failure, B, multiply by the average out-of-court settlement, C. A times B times C equals X. If X is less than the cost of a recall, we don't do one.
The problem here is that non-discrimination costs you money (in practice: lots!), because it requires you to issue bad loans to non-Asian minorities.
Secondly, it isn't mathematically possible to be "nondiscriminatory" - there are multiple definitions of that term and they are mutually conflicting. For example, as this article shows, "equal outcomes", "equal opportunity" and "equal treatment" (group unaware) don't make the same decisions.
So no matter which definition of "fair" you choose, some intrepid reporter can choose a different definition and then write a clickbait article calling you racist.
> But how can we find out if a company uses ML in a non-discriminating way?
It is an interesting question. However, there are ways to answer this question as we have been measuring discrimination before algorithms. Algorithms are a way of reaching a decisions. Therefore, we can measure discrimination if we audit decisions generated by algorithms. In other words, look at the input-output of the algorithm and measure the impact. In the US, the doctrine of disparate impact has long been used a guideline to evaluate discrimination [0].
Since the original article shows nice examples that there is no single "non-discriminatory way" possible - at the very least, you have to do the classic tradeoff between equal opportunity (and discriminatory outcomes) or equal outcomes (and discriminatory opportunities), you can safely assume that all companies use ML in somewhat discriminating way. At the very least, if they ask your ZIP code and it "matters" in their decision system in any way, then that's a positive sign of discrimination (one way or another) since it's so correlated with race among other things.
Nice to see that the debate has reached the ears of the main people working on this field.
What is important to note here is that we need to tweak the mathematical model to the culture we want to achieve. In other words, the objective function of the optimization problem needs not only match the current state of the world, and provide an hindsight in one's own economic interests, it also needs to take into account the culture that we want to reflect, and influence. Otherwise, these statistics are just a giant status quo amplifier.
I'm not sure that's clear. There are actually two ways to achieve the outcome we want; tweaking the model or changing the inputs.
What I mean, say the model identifies that a certain group has a greater risk due to systemic problems. If you change something about the group, you can change the calculated risk without changing the model. And this may very well be a better way to achieve the outcome you want.
Specifically, by preventing insurance companies from using a more accurate mode, what you're demanding is that the random people who happen to have taken the same insurance packages but are not part of the group should make an extra contribution to fix these systemic problems.
But why them? Shouldn't we all contribute instead, hopefully using a fair system for assessing how much should each pay?
Instead of tweaking the model, you can change the inputs by providing a state-backed guarantee to the underprivileged groups. Isn't it more fair overall?
No I don't think I expressed myself clearly.
The problem is that machine learning learns from biased data. The input data is the total US population, which has wealthier groups than others. Because of the way we train machine learning (using all the data we have), the bias that is in the original data gets transfered to the logic.
Specifically, a machine learning will produce different numbers for 2 individuals with the exact same characteristics except the race. And that is the problem that needs to be addressed.
Let's put it in another context. Let's say I'm a white athlete, and I'm very good at running the 100m race. Actually I run just as fast as a black person who is my main rival. Now if someone has to select one of us to go to the Olympics, they should toss a coin to decide who goes. If you use a ML algorithm, it would absolutely send the black person, because no white people has won a 100m race in the last 20 olympics. That's the kind of bias ML does and that needs to be addressed.
Tweaking the models to lead towards the world we'd like to live in seems like a noble goal, but I wonder how you should incentivize this. If discriminatory models provide higher profits, the idealistic models won't be used.
It's referring to the much-talked-about effect of emergent discrimination, where the model fitting process has the effect of amplifying the status quo, despite the fact that the status quo is informed in large part by structural injustices. For (oversimplified) instance: poor black people represent a cohort of loan applicants likely to default, and the model fitting process may go a step worse and attribute "default risk" to all black people.
The key thing to understand is that we're talking about discrimination that is usually unintended and unexpected by the designers of these systems.
It's an unambiguous fact that the status quo is shaped heavily be many generations of de jure discrimination including chattel slavery and continting structural inequalities in political power that still exist that were designed to protect those other unequal institutions.
It's a subjective political view that any or all of those things are injustices, of course, since justice is a subjective thing.
This happens to be something I believe strongly, but the comment you're responding to doesn't make sense even if you strongly disagree with that, as it supposes we can snapshot American society as it is in 2016 and synthesize from it reliably just and sensible decisions. Nobody believes this, no matter what their politics (unless there's a "status-quo-ism" I'm unaware of).
The problem (or at least, one of the more important problems) being addressed in this work is the unintended amplification of the status quo --- the implicit notion that if something is a certain way now, it is best that it always be that way.
If membership in a discrimination-protected category is not completely binary, equal-opportunity policies incentivize people who could identify with multiple categories to choose the one with the lowest discrimination -adjusted threshold.
Is it possible to make the equal opportunity and the max profit thresholds the same?
maybe with a special tax system (the closer your threshold is to the "equal opportunity" the less tax you pay)?
edit: i forgot its illegal to discriminate
I did not introduce it out of nowhere it was a reference to the parent comment, which did seem relevant to the thread
It is a sensitive subject I suspect
And I cannot find the original parent comment in the discussion and I look like a total dick with my comment coming out of nowhere.
I will try and understand what moved where and get back to you
Aha:
"""A great example is how very resentful many young white men of college age are that universities are requiring them to take sensitivity courses designed to reduce the instance of campus rape, but strictly speaking men of that age are the overwhelming majority of bad actors in that environment. Statist"""
It is only my memory but I am fairly sure that the above comment said "strictly speaking young white men ..." which what prompted my comment. Maybe I transposed the white in the first part of the paragraph to the final part. Not sure. Please do check, let me know if I was being a fool or not.
Darn I need to md5 hash parent comments I reply to now :-)
Ok, this has been a horrific morning - that first paragraph should have had ??!!!! or similar at the end. Which I hope turns it into more of a challenge than the total dick move it looks like.
I should have gone with "citation please"
Anyway, I have written up a sort of overly long comment which won't fit in 2000 chars so I can't submit it and so this is the link
I apologise for thinking this was not a slippery slope and for not reviewing my text for misunderstanding. And I apologise for putting a piece of text up there that is so blatantly... horrific. It's awful to have that in my permanent history even if I know how it was a mistake.
As it says in the article I am going to go and have some reflection time.
My prior is that that race has no influence on behaviour therefore I would assume no difference between rape tendency (indeed any criminal tendency) amount young men of different races.
As in I am surprised that the parent claimed ... err the parent I replied too seems to have vanished and my comment is looking pretty weird
Anyway from memory he raised that young white men had a higher rape stats than any other group - I was surprised by the inclusion of race in that
Race and ability to repay a loan, are - and this is scientifically proven - uncorrelated. What is correlated is your social status and your ability to repay a loan.
However, there is a problem because the population of the US is not uniform. Because of history, some races were less wealthy than others. So far, this makes sense I suppose.
Now, talking about machine learning. The big specificity of machine learning (when it is implemented with neural nets) is that it is trained on data only. The machine doesn't know about biology, and it doesn't know about history. Therefore, the model sort of converges to a place where effectively it "believes" from the data it has seen that there is a correlation between race and ability to repay a loan. So basically, the historical bias that is in the data translates into a logical bias. And so, because of history, you're less likely to get a loan if you're a black person than if you're a white person. That is what I call "amplifying the status quo"
Now, as we live in a society that holds equality of chances as one of its core values, I think it is good that people who work in machine learning look at these bias, and have a criticism towards the results of the algorithms in terms of societal impacts, and not only in terms of economical gain.
This has nothing to do with totalitarianism. Yes it is not libertarian, but we don't live in a libertarian world.
Can you cite the scientific proof? From what I've seen, race is correlated with default probability after taking other obvious factors into account (e.g. income, education, single motherhood).
Figure 7 of the underlying paper we are discussing shows exactly that - a white/asian person with a 700 FICO score has about a 10% chance of defaulting, while a black person with a 700 FICO has about a 15% chance of default. (Those numbers are eyeballed from the graph, might not be that accurate.)
I can't cite all the information I have on this, but I've found a blog post which analyzes similar data and derives similar relationships in publicly available data:
No, I think he's more advocating for a world where we don't encode the literal status quo into computer models that make decisions for us going forward, which is something that nobody of any political stripe is likely to want.
What's wrong with encoding the status quo? Why do we have to encode progressive extremism into computer models?
Being progressive for the sake of "going forward" sounds like misguided idealism. What do we do when progressive computer models lead us down a harmful path, do we simply play the typical liberal blame game and point fingers everywhere else while digging our head into sand?
I'm not sure you're following my point. Conservatives should be no happier than liberals about the prospect of a deeply imperfect status quo being baked into future decisionmaking, because there's much about the status quo conservatives don't like either.
This isn't even remotely far-fetched. If you're a rural working class person, it's not at all hard to see how machine learning algorithms could adversely affect decisions about your insurance rates, loan acceptances, sentencing, the frequency with which your homes and businesses are inspected, and so on.
The idea that "discrimination" is somehow a progressive bugbear is --- well, I don't know how better to say this --- deeply stupid. Swirling eyes-within-eyes sticking out from blobs of fur inverted machine learning stupid.
I do understand the point you're making -- my concern centers around deeply flawed models being used despite their shortcomings because their inherently progressive slant is sacrosanct. They would instead be held up and fingers pointed elsewhere, either due to lack of data or incorrect usage of the resulting advice derived from existing bodies of data (or something more sinister, eg: sabotage by opposition parties).
What I'm trying to say is correcting discrimination under the guise that it's unfair to certain sections of society is chasing your own tail out of boredom -- what's the point? To improve society, or improve usefulness of machine learning-derived data to make radical (but necessary) decisions? Do we maintain our objectivity and move away from "improving society" when it's found to be counter-productive, or do we keep going forward with improving society and hope things get better?
Trying to "break out of the status quo" to produce more a accurate representation of the world sounds great, I'm totally all for removing human biases, but my concern is that it'll be handled badly to mistakenly attempt to benefit certain classes of society (eg: repeat of subprime mortgage crises from 07-09 but much more difficult to diagnose).
I'm not sure you understand my point, because you keep talking about "progressive slant", and I keep trying to explain to you that ML is going to be just as hard --- in fact, probably far harsher --- on "red state" voters.
Think of it in terms of survival bias. ML models that discriminate against the well-educated urban workers that design and deploy them are not going to make it to production if they obviously discriminate against their own designers. Those developers and "data scientists" are not going to notice if those same models happen to deny college admission to kids who took a year off after graduating to work at a factory to help pay for their siblings room and board.
No, no, he's advocating for a dreadful, sinister world where a bunch of high priests strip you of your freedom to decide how you conduct business...
How much "discrimination" will be good enough in the end? I'll tell you what the endgame is: no discrimination at all, everybody gets a loan. So, in other words, no loans at all because nobody would conduct business in those conditions.
Second, there's no political orientation I can think of that is entirely comfortable with the status quo. Conservatives believe themselves to be discriminated against, just in a different set of circumstances. The underlying problem with ML is conceptual, not political.
I agree that this should be a possibility, while I think the odds that millions of law enforcement officers and criminal justice faculty would spontaneously conspire against a particular race, I also think it's more likely than some genetic predisposition toward crime (i.e., the first option).
There are so many places where this thread took a dismal turn that I suppose it's hopeless to even try pruning it, but since this is one of those places, we detached it from https://news.ycombinator.com/item?id=13005641 and marked it off-topic.
Fair enough. It would be unlikely that millions of police would be spontaneously biased against African Americans, particularly when you factor in that those officers most likely to use excessive force against African Americans are themselves African American.
> Nobody is talking about the bias being spontaneous. It is chronic and cultural.
It's very counterintuitive that police officers around the country would have a homogeneous culture that diverges so strongly from the cultures of the diverse communities from which they come and in which they live and in which they work.
It's necessarily "spontaneous" that police officers around the country coming from diverse backgrounds and living and working in diverse communities would spontaneously adopt a culture that biases them against one race in particular, especially when many of those officers are of that same race.
> Do you have anything to back up your claim that African American officers are more likely to use force against African Americans than white officers?
2 separate analyses come to mind, but there are others (I don't have the time to dig up at the moment). As far as I know, this isn't disputed among criminologists.
There is nothing counterintuitive or spontaneous about police culture being homogeneous and very different from the surrounding communities. A simple google search will dismiss this notification instantly. https://encrypted.google.com/search?hl=en&q=police%20culture...
Secondly you imply that police diversity somehow matches community diversity, when this is manifestly untrue, particularly in places where it is asserted that discrimination is active.
Take Ferguson Missouri, 67.4 percent of the city’s 21,000 residents are black, and 29.3 percent are white.
2. I made no such implication; only that police come from and work in diverse communities. This means it should be surprising that police from all over and in all places would spontaneously converge on a single, hateful culture.
The facts may not bear out to my conjecture, but you've done nothing to demonstrate this.
My google search shows that there is an abundance of research into problems with the police culture. That is firmly established.
Your second claim is false. See my reference to the diversity of the ferguson police department. You are simply shown to be wrong on this count.
Also, nobody has said anything about convergence on a single hateful culture, and nowhere is there anything that requires a biased culture of policing to have occurred spontaneously. That is simply a straw man.
> My google search shows that there is an abundance of research into problems with the police culture. That is firmly established.
It only demonstrates that there are a lot of search terms that match those criteria. Without diving into each source, we don't know what's even relevant to this conversation (we have no idea how "police culture" is defined in each of these sources, for example).
> Your second claim is false. See my reference to the diversity of the ferguson police department. You are simply shown to be wrong on this count.
I saw your reference to the diversity of the ferguson police department, but it has no bearing to my statement. Pointing out the racial disparity between the Ferguson PD and the community it serves does not disprove the notion that police come from diverse communities, nor that police serve diverse communities. It merely proves that in Ferguson, the racial composition of the police don't match that of the surrounding community.
> Also, nobody has said anything about convergence on a single hateful culture, and nowhere is there anything that requires a biased culture of policing to have occurred spontaneously. That is simply a straw man.
Unless you're proposing that police bias is organized (e.g., conferences about how to better oppress black Americans), then it is necessarily spontaneous if it exists at all. If you want to make a compelling case that racial disparities in criminal justice are principally caused by racist police, you had better at least provide a good explanation for how such a diverse occupation might become racist (in particular because the officers most likely to abuse force are themselves minorities). In particular, it is well-documented that African Americans commit more crime per capita than other American racial groups, so your case must sufficiently explain why these data are inaccurate or how to better interpret them.
Now, this is his first offence, but a young man is found high on PCB walking down the street hitting cars with a baseball bat. It takes five cops and a significant struggle to arrest him.
Having read that, what would you assume is race and economic background is?
After criminal proceedings he was sentenced to community service.
Now, what would you assume is race and economic background is?
PS: Bias is insidious and really hard to control for.
Interesting you said poor, at the time he had over 6 million in a trust fund and stood to inherent vastly more. Some people change their minds because of the community service vs. prison but some don't.
PS: PCP has a reputation as a rural poor white persons drug. I wonder if you would have had a different impression if I started by saying he got community service.
Look, if we're talking about assumptions, we're talking about probability. I have no idea who you're talking about. I'll happen upon some statistics once in a while, but otherwise will avoid making assumptions because I simply don't know. You asked me for what I'd say was most statistically probable, you got it.
I assume you mean "PCP" and not what circuit boards are printed on, yeah. You're right, I've never heard of a rich person taking PCP (cocaine, fancy liquor, etc. for them, right?) although I'm absolutely unsure of how PCP use breaks down racially. (If you have any source for those statistics, you've piqued my interest in them and I'd love a link)
Again, my guess of "white" was based on the fact that most people are white, and thus most cases of violence (without further stratification) are probably by whites. And what, do African-Americans not get community service? That probably wouldn't have changed my mind, and actually sounds a little bit racist.
Poor people are more likely to commit street crime.
> Findings on social class differences in crime are less clear than they are for gender or age differences. Arrests statistics and much research indicate that poor people are much more likely than wealthier people to commit street crime.
> [...] most criminologists would probably agree that social class differences in criminal offending are “unmistakable” (Harris & Shaw, 2000, p. 138).
And again, as you admitted, PCP has the connotation of being used by poor people. I can't find stats on it right now, having just run out of time, but you even admitted it directly.
As to connotation's, that's faulty reasoning. The only correct response to any of my questions was, not enough information. Yet, you where more than happy to try and both pick a response and then justify it. Even when I directly said you were wrong.
Now, the same identical reasoning happens all over the place in the criminal justice system. Making the link between crime statistics and crimes almost meaningless. Arrest statistics are just that arrest statistics and they don't tell you about who was actually committing crimes.
PS: I suspect you feel very confidante about his race, except I never confirmed or denied your assumption.
Edit: TLDR; Making a judgement with limited evidence is a really bad habit. People soon forget they chose something because it was slightly better odds and reinforce the judgement so something that may have been even odds to start with often feels much more likely over time.
No, I just don't understand your question. Do you want me to make a crappy guess based on the crappy data you provided? If so, I don't understand why... Judging by subthreads, you just want to say "Ha! Your crappy guess was wrong!". If you want me to pretend that I'm somehow involved in the processing of this case, but I'm restricted to only the crappy data provided, I wouldn't "assume" anything--I would say I don't have enough information to establish any individual's guilt much less sentence them.
> Do you want me to make a crappy guess based on the crappy data you provided?
No, I had zero problem with your response (assuming it was genuine). As I said, it sounds like you are avoiding the question, however avoiding making those associations is the correct response.
Anyway, I was not asking if you thought they where guilty or anything. This was a friend of mine and he had zero problem admitting what happened.
This empty snark would make more sense if you weren't commenting on machine learning, where the problem is emergent discrimination unintended by the designers --- in other words, discrimination that isn't chosen.
As it stands, all you've done here is reveal to the thread that you don't understand why discrimination is a concern with machine learning, but you have very strong feelings about it anyways. Congratulations? Good talk?
While unintended discrimination emerging from machine learning techniques is a very important problem that needs to be addresses, it's also important to remember that practices such as "redlining" show that there is still an unfortunate amount of intentional discrimination. The complexity of machine learning creates a lot of opportunity to hide intentionally biased features.
I think he means something that I quoted in an AirBnb thread a 73 days ago (shameless self-quoting)
Whenever you
- book a flight,
- buy bread or milk, or
- get a girl friend
you discrimate in favor of one and against all others.
Subjective discrimination is the only way to choose, discrimination is the sure consequence of your every choice.
This is how it should be:
"Max Profit. The most profitable, since there are no constraints. But the two groups have different thresholds, meaning they are held to different standards."
Here's the big fallacy: "the two groups have different thresholds, meaning they are held to different standards."
They are not held to different standards because they're different groups, but because of other reasons that indicate different loan default rates. So you cannot call this "discrimination". This is how things should be.
Since you've ignored our repeated requests to stop breaking the HN guidelines, we've banned your account.
If you don't want to be banned, you're welcome to email hn@ycombinator.com. We're happy to unban people if they give us reason to believe they'll only post civil, substantive comments in the future.
Racewar threads are off topic on the grounds of ultimate tediousness.
All it leads to is flamewars, and no flamewar will ever resolve any of it. Meanwhile it poisons what we do care about: intellectual curiosity, and civil, substantive discussion.
Gwern has a nice bit somewhere about how you can either devote your entire life to this stuff or give up. Most of us, the vast majority, have given up. The rest of you true believers can take your stone tablets with "Science" engraved on them to some place where the audience actually likes it.
(Edit: your comment history has blatantly been breaking the HN guidelines about other things as well. We ban accounts that do that, so please stop doing that.)
Edit: ok, you probably have loads of comments to moderate and do this a lot, but this is a big shock to me - being accused of racewar is a bit of a "pull up".
Ok, I can see how we got here - next time I will just say "citations" but now that we have broached the subject that I am being monitored and have breeched guidelines - can you give specifics so I can learn something. If I ever do learn?
Please respond assuming my comment was not intended to troll, which it was not, even if it was badly worded.
Ps what's gwern?
PPS Stone tablets with science? I don't get the conjunction but it is really dismissive - just how often do you have to do this removal thing.
Sorry I'm late to seeing this, but you seem to have mistakenly thought that my comment applied to one of yours? It didn't—I was replying to https://news.ycombinator.com/item?id=13006905.
No, he doesn't. He has a mixture of early Jensenist psychometric research that has been superseded, and neo-phrenologists like Rushton that have been discredited.
But of course it's easy to drop little bombs like this into threads and put the onus on other people to explain the science. That's what makes it trolling.
Whatever snappy response you have for this, please spare us. There is a reason "the subject of racial IQ gaps" is a controversial scientific debate, and I'm quite confident you haven't settled it.
Also: please lose your condescending tone. There are something like 5 comments on this thread in which I attempt to explain how the problems this CS research (which you appear to find intolerable) are going to cause problems for conservatives, rural citizens, and libertarians. All I see from you is derisive comments and an inexplicable defense of an off-topic introduction of racial IQ gaps to the thread.
If you have something to say about computer science research, go for it. Please do not pretend that you're occupying some kind of rationalist high ground while militantly preventing other people from discussing computer science on HN. The person in this conversation most diligently trying to turn it towards politics is you.
That is correct: I do not want more cites. Please race troll elsewhere. I had to look up what "kafka trap" means; it appears to be an Eric S. Raymond term. Let me suggest Eric Raymond's blog as a place more welcoming to your urge to litigate the merits of different races.
Do you have something to say about computer science, or is your sole comment in a thread about machine learning research going to be first-principles race trolling? Comments like these appear literally to be the only kind you write on HN. There must be a better place for you to talk politics than here.
A real life example: In many (all?) countries, it's common to pay more for your car insurance if you are young, if you are male, or if you recently acquired your driver's license. One might frame this as a prejudice towards young, male drivers as being reckless. But statistically they are just that, so the prejudice is balanced (within a reasonable tolerance). It's unfair to the careful young, male driver, but well, life is not always fair.