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We investigated Amsterdam's attempt to build a 'fair' fraud detection model
31–40 of 87 posts
Re: We investigated Amsterdam's attempt to build a 'fair' fraud detection model
#32[flagged]
Oh no you really stepped in it now
Re: We investigated Amsterdam's attempt to build a 'fair' fraud detection model
#33[flagged]
> Why would you assume that all groupings of people commit welfare fraud at the same rate?
What's the alternative? It's an unattainable statistic, the people who get away with crime. Instead, what ends up getting used is the fraud rates under the old system, or ad hoc rules of thumb based in bigoted anecdotes.
So instead you delcare that you don't think that ethnicity is in and of itself a cause of fraud. Even if there may be any number of characteristics that tend to indicate or motivate fraud that are seen more in one specified ethnicity than another (poverty, etc.), and even though we should expect that to lead to more fraud. We can choose to say that those characteristics lead to fraud, rather than the ethnicity, and put that out of scope.
Then we can say that this algorithm isn't meant to solve multiculturalism, it's meant hopefully not to exacerbate the problems with it. If one wants to get rid of weird immigrants, non-whites, or non-Christians, just do it, instead of automating a system to be bigoted.
Also, going after the marginal increase of rates of fraud through defining groups that represent a small portion of the whole is likely to be a waste of money. If 90% of people commit fraud at a 5% rate and 10% commit it at a 10% rate, where should you be spending your time?
Re: We investigated Amsterdam's attempt to build a 'fair' fraud detection model
#34I have a growing feeling that the only way to be fair in these situations is to be completely random.
Re: We investigated Amsterdam's attempt to build a 'fair' fraud detection model
#35Amsterdam reduced bias by one measure (False Positive Share) and bias increased by another measure (False Discovery Rate). This isn’t a failure of implementation; it’s a mathematical reality that you often can’t satisfy multiple fairness criteria simultaneously.
Training on past human decisions inevitably bakes in existing biases.
Re: We investigated Amsterdam's attempt to build a 'fair' fraud detection model
#36[flagged]
The goal is to avoid penalizing people for their skin color, or for gender/sex/ethnicity/whatever. If some group have higher rate of welfare fraud, the fair/unbiased system must keep false positives for that group at the same level as for general population. Ideally there should be no false positives at all, because they are costly for people, who were marked wrongly, but sadly real systems are not like that. So thes…
>The goal is to avoid penalizing people for their skin color [...]
That's not correct. The goal is to identify and flag fraud cases. If one group has a higher likelihood to perform that, then this will show up in the data. The solution should not be to change the data but educate that group to change their behavior.
Please note that I have neither mentioned any specific group and do not have a specific group in mind. However, an example for such a group that I have seen in my professional life could be female 20 year old CEOs of construction companies (often connected to organized crime)
Re: We investigated Amsterdam's attempt to build a 'fair' fraud detection model
#37Earlier quoted context omitted.
Yes, well there's the irony. "Unbiased" and "fair" are quite overloaded here, to borrow a programming term. I think it's one of those times where single words should expressly NOT be used to describe the intent. The intent of this is to presume that the rate of the thing we are trying to detect is constant across subgroups. The definition of a "good" model therefore is one that approximates this. I'm curious if their…
I know a cop, they do public searchings for weapons or drugs. Our law dictates fairness. So every now and then they search an elderly couple. You know how this goes and what the results are. Any model would be unfair, age-wise but also ethnically. To be most effective the model would have to be unfair. It would suck to be a law abiding young specific ethnic minority. But does it help to search elderly couples? I’m Ge…
If this strategy was applied across policing, their metrics would likely improve markedly.
Eg, police shooting and brutality stats wouldn’t be tolerated for very long.
Re: We investigated Amsterdam's attempt to build a 'fair' fraud detection model
#38In my view, we need to move the goalposts.
Fraud detection models will never be fair. Their job is to find fraud. They will never be perfect, and the mistaken cases will cause a perfectly honest citizen to be disadvantaged in some way.
It does not matter if that group is predominantly 'people with skin colour X' or 'people born on a Tuesday'.
What matters is that the disadvantage those people face is so small as to be irrelevant.
I propose a good starting point would be for each person investigated to be paid money to compensate them for the effort involved - whether or not they committed fraud.
Re: We investigated Amsterdam's attempt to build a 'fair' fraud detection model
#39> But the model designers were aware that features could be correlated with demographic groups in a way that would make them proxies. There's a huge problem with people trying to use umbrella usage to predict flooding. Some people are trying to develop a computer model that uses rainfall instead, but watchdog groups have raised concerns that rainfall may be used as a proxy for umbrella usage. (It seems rather strange…
They correctly note the existence of a tradeoff, but I don't find their statement of it very clear. Ideally, a model would be fair in the senses that: 1. In aggregate over any nationality, people face the same probability of a false positive. 2. Two people who are identical except for their nationality face the same probability of a false positive. In general, it's impossible to achieve both properties. If the output…
This is a really key result. You can't effectively be "blind" to a parameter that is significantly correlated to multiple inputs and your output prediction. By using those inputs to minimize false positives you are not statistically blind, and you can't correct the statistics while being blind.
My suspicion is that in many situations you could build a detector/estimator which was fairly close to being blind without a significant total increase in false positives, but how much is too much?
I'm actually more concerned that where I live even accuracy has ceased to be the point.
Re: We investigated Amsterdam's attempt to build a 'fair' fraud detection model
#40[flagged]
Using the word “retarded” like that negates any other point you tried to make.