7 ms·
We investigated Amsterdam's attempt to build a 'fair' fraud detection model
- djoldman 1y ago"Unbiased," and "fair" models are generally somewhat ironic. It's generally straightforward to develop one if we don't care much about the performance metric: If we want the output to match a population distribution, we just force it by taking the top predicted for each class and then filling up the class buckets. For example, if we have 75% squares and 25% circles, but circles are predicted at a 10-1 rate, who cares, just take the top 3 squares predicted and the top 1 circle predicted until we fill the quota.
- Scarblac 1y agoBut that's a bias, if circles are actually more likely to be fraudulant.
- djoldman 1y agoIf the definition of "unbiased" and "fair" is that the model flags squares and circles at a rate or proportion equal to the population distribution of squares and circles, then the model is unbiased and fair. As noted above, this doesn't do anything for performance.
- wongarsu 1y agoSo if I want to make a model to recommend inkjet printers then a quarter of all recommendations should be for HP printers? After all, a quarter of all sold printers are HP. As you say, that would be a crappy model. But in my opinion that would also be hardly a fair or unbiased model. That would be a model unfairly biased in favor of HP, who barely sell anything worth recommending
- djoldman 1y agoYes, 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 data matches that assumption. Do subgroups submit bad applications at the same rate? It may be that they don't have the data and therefore can't answer that.
- teekert 1y agoI 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 Genuinely curious what would be fair and effective here. You can’t be a Bayesian.
- lostlogin 1y agoIf 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.
- djohnston 1y ago[flagged]
- BonoboIO 1y agoYes it is. This is some ideal world thinking, that has nothing to do with reality and is easily falsifiable, but only if you want to see the real world.
- throwawayqqq11 1y ago[flagged]
- djohnston 1y agoNo... the pre-determined bias in this story is obviously that all subgroups of people behave identically w.r.t. welfare applications, which the data itself did not support and a momentary consideration of socioeconomics would debunk. When they tried to cludge the weights to fit their predetermined bias, the model did so poorly on a pilot run that the city shut it down.
- throwawayqqq11 1y agoBeing flagged as potential fraud based on eg. ethnicity is what you want to eliminate, so you have to start with the assumption of an even distristribution. From the article: > Deciding which definition of fairness to optimize for is a question of values and context. This optimization is the human feedback required to not have the model stagnate in a local optimum.
- andy99 1y agoThe better definition of equal performance would obviously be that the metrics for the detector - accuracy or false positive rate etc would be the same for all groups. I won't comment on why it's defined the way that it is. Edit: it looks like they define several metrics, including ones like I mention above that consider performance and at least one based on what number or percentage is flagged in each group.
- BonoboIO 1y agoThe article talks a lot about fairness metrics but never mentions whether the system actually catches fraud. Without figures for true positives, recall, or financial recoveries, its effectiveness remains completely in the dark. In short: great for moral grandstanding in the comments section, but zero evidence that taxpayer money or investigative time was ever saved.
- TacticalCoder 1y ago[dead]
- stefan_ 1y agoIt also doesn't mention what numbers we are even talking about that given the expansive size of the Dutch government make this an at all useful thing.
- tomp 1y agoKey point: The model is considered fair if its performance is equal across these groups. One can immediately see why this is problematic, easily by considering equivalent example in less controversial (i.e. emotionally charged) situations. Should basketball performance be equal across racial, or sex groups? How about marathon performance? It’s not unusual that relevant features are correlated with protected features. In the specific example above, being an immigrant is likely correlated with not knowing the local language, therefore being underemployed and hence more likely to apply for benefits.
- atherton33 1y agoI think they're saying something more subtle. In your basketball analogy, it's more like they have a model that predicts basketball performance, and they're saying that model should predict performance equally well across groups, not that the groups should themselves perform equally well.
- tomp 1y agoYou’re right, I misinterpreted it.
- wongarsu 1y agoA big part of the difficulty of such an attempt is that we don't know the ground truth. A model is fair or unbiased if its performance is equally good for all groups. Meaning e.g. if 90% of cases of Arabs committing fraud are flagged as fraud, then 90% of cases of Danish people committing fraud should be flagged as fraud. The paper agrees on this. The issue is that we don't know how many Danish commit fraud, and we don't know how many Arabs commit fraud, because we don't trust the old process to be unbiased. So how are we supposed to judge if the new model is unbiased? This seems fundamentally impossible without improving our ground truth in some way. The project presented here instead tries to do some mental gymnastics to define a version of "fair" that doesn't require that better ground truth. They were able to evaluate their results on the false-positive rate by investigating the flagged cases, but they were completely in the dark about the false-negative rate. In the end, the new model was just as biased, but in the other direction, and performance was simply worse: > In addition to the reappearance of biases, the model’s performance in the pilot also deteriorated. Crucially, the model was meant to lead to fewer investigations and more rejections. What happened instead was mostly an increase in investigations , while the likelihood to find investigation worthy applications barely changed in comparison to the analogue process. In late November 2023, the city announced that it would shelve the pilot.
- golemiprague 1y ago[dead]
- zeroCalories 1y agoDoes anyone know what they mean by reweighing demographics? Are they penalizing incorrect classifications more heavily for those demographics, or making sure that each demographic is equally represented, or something else? Putting aside the model's degraded performance, I think it's fair to try and make sure the model is performing well for all demographics.
- 3abiton 1y ago> A more concerning limitation is that when the city re-ran parts of its analysis, it did not fully replicate its own data and results. For example, the city was unable to replicate its train and test split. Furthermore, the data related to the model after reweighting is not identical to what the city published in its bias report and although the results are substantively the same, the differences cannot be explained by mere rounding errors. Very well written, but that last part id concerning and point to one part: did they hire interns? How cone they do not have systems? It just cast a big doubt on the whole experiment.
- tbrownaw 1y ago> 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 to expect a statistical model trained for accuracy to infer and indirect through a shadow variable that makes it less accurate, simply because it's something easy for humans to observe directly and then use as a lossy shortcut or to promote alternate goals that aren't part of the labels being trained for or whatever.) > These are two sets of unavoidable tradeoffs: focusing on one fairness definition can lead to worse outcomes on others. Similarly, focusing on one group can lead to worse performance for other groups. In evaluating its model, the city made a choice to focus on false positives and on reducing ethnicity/nationality based disparities. Precisely because the reweighting procedure made some gains in this direction, the model did worse on other dimensions. Nice to see an investigation that's serious enough to acknowledge this.
- tripletao 1y agoThey 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 and at least one other input correlate with nationality, then a model that ignores nationality fails (1). We can add back nationality and reweight to fix that, but then it fails (2). This tradeoff is most frequently discussed in the context of statistical models, since those make that explicit. It applies to any process for deciding though, including human decisions.
- kurthr 1y agoThis 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.
- talkingtab 1y agoIs this crazy or what? My take away is that the factors the city of Amsterdam is using to predict fraud are probably not actually predictors. For example if you use the last digit of someones phone number as a fraud predictor, you might discover there is a bias against low numbers. So you adjust your model to make it less likely that low numbers generate investigations. It is unlikely that your model will be any more fair after your adjustment. One has to wonder if the study is more valid a predictor of the implementers' biases than that of the subjects.
- precommunicator 1y agoYou can find the parameters used in GitHub repository linked from the article, and the phone number isn't one of them (https://github.com/Lighthouse-Reports/amsterdam_fairness/tree/main/wpi_uitkeringsfraude/model/classifier https://github.com/Lighthouse-Reports/amsterdam_fairness/tre...)
- thatguymike 1y agoCongrats Amsterdam: they funded a worthy and feasible project; put appropriate ethical guardrails in place; iterated scientifically; then didn’t deploy when they couldn’t achieve a result that satisfied their guardrails. We need more of this in the world.
- deleted 1y ago[deleted]
- ncruces 1y agoI have a growing feeling that the only way to be fair in these situations is to be completely random.
- Jimmc414 1y agoAmsterdam 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.
- londons_explore 1y agoIn 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.
- WhyIsItAlwaysHN 1y agoSome groups will be more disadvantaged than others by being investigated. For example for welfare, I expect fraudsters to have more money to support themselves or less people to support (unless the criteria for welfare is something unexpected). So I'd say that there also needs to be more protections than just providing money. Nevertheless the idea of giving money is still good imo, because it also incentivizes the fraud detection becoming more efficient, since mistakes now cost more. Unfortunately I have a feeling people might game that to get more money by triggering false investigations.
- LorenPechtel 1y agoWhy is there so much focus on "fair" even when reality isn't? Not all misdeeds are equally likely to be detected. What matter is minimizing the false positives and false negatives. But it sounds like they don't even have a base truth to be comparing it against, making the whole thing an exercise in bureaucracy.
- Fraterkes 1y agoWho says reality isnt fair? Isnt that up to us, the people inhabiting reality?
- LorenPechtel 1y agoReality gives some people an easier time than others. If reality were fair there would be no need of a welfare system in the first place.
- bananaquant 1y agoWhat nobody seems to talk about is that their resulting models are basically garbage. If you look at the last provided confusion matrix, their model is right in about 2/3 of cases when it makes a positive prediction. The actual positives are about 60%. So, any improvement is marginal at best and a far cry from ~90% accuracy you would expect from a model in such a high-stakes scenario. They could have thrown a half of cases out at random and had about the same reduction in case load without introducing any bias into the process.
- xyzal 1y agoYou can't tell a project will fail until you undertake it. Amsterdam didn't deploy their models when they found their outcome is not satisfactory. I find it a perfectly fine result.
- delusional 1y ago> What nobody seems to talk about is that their resulting models are basically garbage. The post does talk about it when it briefly mentions that the goal of building the model (to decrease the number of cases investigated while increasing the rate of finding fraud) wasn't achieved. They don't say any more than that because that's not the point they are making. Anyway, the project was shelved after a pilot. So your point is entirely false.
- bananaquant 1y agoGood catch about the project being shelved. It is buried pretty deep in the document to the point of making it misleading: > In late November 2023, the city announced that it would shelve the pilot. I would agree that implications regarding the use of those models do not hold, but not the ones about their quality.
- GardenLetter27 1y ago> None of these features explicitly referred to an applicant’s gender or racial background, as well as other demographic characteristics protected by anti-discrimination law. But the model designers were aware that features could be correlated with demographic groups in a way that would make them proxies. What's the problem with this? It isn't racism, it's literally just Bayes' Law.
- crote 1y agoLet's say you are making a model to judge job applicants. You are aware that the training data is biased in favor of men, so you remove all explicit mentions of gender from their CVs and cover letters. Upon evaluation, your model seems to accept everyone who mentions a "fraternity" and reject anyone who mentions a "sorority". Swapping out the words turns a strong reject into a strong accept, and vice versa. But you removed any explicit mention of gender, so surely your model couldn't possibly be showing an anti-women bias, right?
- alternatex 1y agoI've never had any implication of my gender other than my name in any CV over the past decade. Who are these people who make a career history doc include gender-implicating data? And if there are such CVs, they should be stripped of such data before processing. The fraternity example is such a specific 1 in a 1000 case.
- triceratops 1y ago> I've never had any implication of my gender other than my name in any CV So you're not implying gender other than by implying gender? If humans can use names to classify people into genders, a model can do the same thing.
- alternatex 1y agoIt's information that's easy to strip before running it through machine learning. The implication in the parent comment is that CVs are inherently bound to gender and I cannot see that to be the case for most.
- octo888 1y agoIMO the title would benefit from the word "welfare" before "fraud"
- dannykwells 1y agoCan someone explain to me - assuming they have enough data - why not train different models explicitly for each group / subgroup you want to model? You even could then just take the top N% of each group by score, effectively guaranteeing equal treatment for each group. Why would this not work?